From 4257e35caf6d2715a033ccf8d19570871e130aee Mon Sep 17 00:00:00 2001
From: Fanny Risbourg <frisbourg@laas.fr>
Date: Fri, 11 Mar 2022 13:00:08 +0000
Subject: [PATCH] Clean repository
---
CMakeLists.txt | 29 +-
.../Equations_MPC_22_02_2020.pdf | Bin
.../Evaluation_number_of_iterations.pdf | Bin
.../Log_simulations.pdf | Bin
.../cost_function.pdf | Bin
.../dynamic_analysis.pdf | Bin
{scripts => config}/config_solo12.yaml | 0
{src => config}/walk_parameters.yaml | 2 +-
include/qrw/Estimator.hpp | 12 +-
include/qrw/FakeRobot.hpp | 2 -
include/qrw/Filter.hpp | 4 -
include/qrw/FootTrajectoryGenerator.hpp | 1 -
include/qrw/FootstepPlanner.hpp | 6 +-
include/qrw/Gait.hpp | 1 -
include/qrw/InvKin.hpp | 1 -
include/qrw/Joystick.hpp | 19 +-
include/qrw/MpcWrapper.hpp | 5 -
.../FootTrajectoryGeneratorBezier.hpp | 8 +-
.../qrw/{ => Solo3D}/FootstepPlannerQP.hpp | 7 +-
include/qrw/{ => Solo3D}/Heightmap.hpp | 7 -
include/qrw/{ => Solo3D}/StatePlanner3D.hpp | 5 +-
include/qrw/{ => Solo3D}/Surface.hpp | 0
include/qrw/StatePlanner.hpp | 2 -
include/qrw/WbcWrapper.hpp | 2 +-
.../Solo3D/FootTrajectoryGeneratorBezier.cpp | 2 +-
python/Solo3D/FootstepPlannerQP.cpp | 2 +-
python/Solo3D/StatePlanner3D.cpp | 2 +-
python/Solo3D/Surface.cpp | 2 +-
scripts/Controller.py | 10 +-
.../MPC_crocoddyl.py | 0
.../MPC_crocoddyl_2.py | 0
.../MPC_crocoddyl_planner.py | 0
.../Equations_MPC_22_02_2020.aux | 38 -
.../Equations_MPC_22_02_2020.out | 10 -
.../Equations_MPC_22_02_2020.synctex.gz | Bin 185264 -> 0 bytes
.../Equations_MPC_22_02_2020.tex | 645 ------------
scripts/LICENSE | 25 -
scripts/Logger.py | 944 ------------------
scripts/MPC_Wrapper.py | 16 +-
scripts/__init__.py | 0
scripts/bauzil_stairs.stl | Bin 32684 -> 0 bytes
scripts/config_solo12_bis.yaml | 58 --
scripts/main_mcapi_solo8.py | 280 ------
scripts/main_minimal_controler.py | 212 ----
scripts/main_solo12_control.py | 78 +-
scripts/main_solo12_demo_estimator.py | 225 -----
scripts/main_solo12_replay.py | 142 +--
scripts/plot_IMU_mocap_result.py | 788 ---------------
scripts/plot_IMU_mocap_result_bis.py | 802 ---------------
scripts/plot_comparison_fb.py | 171 ----
scripts/solo3D/SurfacePlanner.py | 2 +-
scripts/solo3D/SurfacePlannerWrapper.py | 1 -
scripts/{ => solo3D}/heightmap_generator.py | 4 +-
scripts/solo3D/{tools => }/heightmap_tools.py | 2 -
.../solo3D/{tools => }/pyb_environment_3D.py | 0
scripts/solo3D/{tools => }/utils.py | 0
scripts/test_convergence_mpc.py | 563 -----------
scripts/test_mpc.py | 199 ----
scripts/test_new_InvKin.py | 214 ----
scripts/test_tasks_InvKin.py | 277 -----
scripts/{ => tools}/ForceMonitor.py | 4 -
scripts/{ => tools}/LoggerControl.py | 0
scripts/{ => tools}/LoggerSensors.py | 0
scripts/{ => tools}/PyBulletSimulator.py | 0
scripts/{ => tools}/gamepadClient.py | 0
scripts/{ => tools}/place_com_with_invkin.py | 3 +-
scripts/tools/qualisysClient.py | 161 +++
scripts/{ => tools}/utils_mpc.py | 37 +-
src/Controller.cpp | 4 +
src/FootstepPlanner.cpp | 5 +
src/Joystick.cpp | 10 +
src/MpcWrapper.cpp | 3 +
.../FootTrajectoryGeneratorBezier.cpp | 6 +-
src/{ => Solo3D}/FootstepPlannerQP.cpp | 7 +-
src/{ => Solo3D}/Heightmap.cpp | 2 +-
src/{ => Solo3D}/StatePlanner3D.cpp | 7 +-
src/{ => Solo3D}/Surface.cpp | 2 +-
src/st_to_cc.cpp | 3 +-
.../python/crocoddyl}/README.md | 0
.../crocoddyl}/analyse_control_cycle.py | 0
.../crocoddyl/analyse_control_cycle_2.py | 0
.../python/crocoddyl}/analyse_simu.py | 0
.../python/crocoddyl/analyse_simu_2.py | 0
.../python/crocoddyl}/compare_mpcs.py | 0
.../test_4 => tests/python/crocoddyl}/main.py | 0
.../python/crocoddyl/main_2.py | 0
.../python/crocoddyl}/run_scenarios.py | 0
.../python/crocoddyl/run_scenarios_2.py | 0
.../python/crocoddyl}/unittest_augmented.py | 0
.../python/crocoddyl}/unittest_linear.py | 0
.../python/crocoddyl}/unittest_nonlinear.py | 0
.../python/crocoddyl}/unittest_step.py | 0
92 files changed, 317 insertions(+), 5764 deletions(-)
rename {scripts/Documentation => Documentation}/Equations_MPC_22_02_2020.pdf (100%)
rename {scripts/crocoddyl_doc => Documentation}/Evaluation_number_of_iterations.pdf (100%)
rename {scripts/crocoddyl_doc => Documentation}/Log_simulations.pdf (100%)
rename {scripts/crocoddyl_doc => Documentation}/cost_function.pdf (100%)
rename {scripts/crocoddyl_doc => Documentation}/dynamic_analysis.pdf (100%)
rename {scripts => config}/config_solo12.yaml (100%)
rename {src => config}/walk_parameters.yaml (98%)
rename include/qrw/{ => Solo3D}/FootTrajectoryGeneratorBezier.hpp (97%)
rename include/qrw/{ => Solo3D}/FootstepPlannerQP.hpp (98%)
rename include/qrw/{ => Solo3D}/Heightmap.hpp (97%)
rename include/qrw/{ => Solo3D}/StatePlanner3D.hpp (97%)
rename include/qrw/{ => Solo3D}/Surface.hpp (100%)
rename scripts/{crocoddyl_class => Crocoddyl}/MPC_crocoddyl.py (100%)
rename scripts/{crocoddyl_class => Crocoddyl}/MPC_crocoddyl_2.py (100%)
rename scripts/{crocoddyl_class => Crocoddyl}/MPC_crocoddyl_planner.py (100%)
delete mode 100644 scripts/Documentation/Equations_MPC_22_02_2020.aux
delete mode 100644 scripts/Documentation/Equations_MPC_22_02_2020.out
delete mode 100644 scripts/Documentation/Equations_MPC_22_02_2020.synctex.gz
delete mode 100644 scripts/Documentation/Equations_MPC_22_02_2020.tex
delete mode 100644 scripts/LICENSE
delete mode 100644 scripts/Logger.py
delete mode 100644 scripts/__init__.py
delete mode 100644 scripts/bauzil_stairs.stl
delete mode 100644 scripts/config_solo12_bis.yaml
delete mode 100644 scripts/main_mcapi_solo8.py
delete mode 100644 scripts/main_minimal_controler.py
delete mode 100644 scripts/main_solo12_demo_estimator.py
delete mode 100644 scripts/plot_IMU_mocap_result.py
delete mode 100644 scripts/plot_IMU_mocap_result_bis.py
delete mode 100644 scripts/plot_comparison_fb.py
rename scripts/{ => solo3D}/heightmap_generator.py (97%)
rename scripts/solo3D/{tools => }/heightmap_tools.py (99%)
rename scripts/solo3D/{tools => }/pyb_environment_3D.py (100%)
rename scripts/solo3D/{tools => }/utils.py (100%)
delete mode 100644 scripts/test_convergence_mpc.py
delete mode 100644 scripts/test_mpc.py
delete mode 100644 scripts/test_new_InvKin.py
delete mode 100644 scripts/test_tasks_InvKin.py
rename scripts/{ => tools}/ForceMonitor.py (99%)
rename scripts/{ => tools}/LoggerControl.py (100%)
rename scripts/{ => tools}/LoggerSensors.py (100%)
rename scripts/{ => tools}/PyBulletSimulator.py (100%)
rename scripts/{ => tools}/gamepadClient.py (100%)
rename scripts/{ => tools}/place_com_with_invkin.py (99%)
create mode 100644 scripts/tools/qualisysClient.py
rename scripts/{ => tools}/utils_mpc.py (69%)
rename src/{ => Solo3D}/FootTrajectoryGeneratorBezier.cpp (99%)
rename src/{ => Solo3D}/FootstepPlannerQP.cpp (98%)
rename src/{ => Solo3D}/Heightmap.cpp (98%)
rename src/{ => Solo3D}/StatePlanner3D.cpp (96%)
rename src/{ => Solo3D}/Surface.cpp (96%)
rename {scripts/crocoddyl_eval => tests/python/crocoddyl}/README.md (100%)
rename {scripts/crocoddyl_eval/test_1 => tests/python/crocoddyl}/analyse_control_cycle.py (100%)
rename scripts/crocoddyl_eval/test_3/analyse_control_cycle.py => tests/python/crocoddyl/analyse_control_cycle_2.py (100%)
rename {scripts/crocoddyl_eval/test_4 => tests/python/crocoddyl}/analyse_simu.py (100%)
rename scripts/crocoddyl_eval/test_5/analyse_simu.py => tests/python/crocoddyl/analyse_simu_2.py (100%)
rename {scripts/crocoddyl_eval/test_6 => tests/python/crocoddyl}/compare_mpcs.py (100%)
rename {scripts/crocoddyl_eval/test_4 => tests/python/crocoddyl}/main.py (100%)
rename scripts/crocoddyl_eval/test_5/main.py => tests/python/crocoddyl/main_2.py (100%)
rename {scripts/crocoddyl_eval/test_4 => tests/python/crocoddyl}/run_scenarios.py (100%)
rename scripts/crocoddyl_eval/test_5/run_scenarios.py => tests/python/crocoddyl/run_scenarios_2.py (100%)
rename {scripts/crocoddyl_eval/test_2 => tests/python/crocoddyl}/unittest_augmented.py (100%)
rename {scripts/crocoddyl_eval/test_2 => tests/python/crocoddyl}/unittest_linear.py (100%)
rename {scripts/crocoddyl_eval/test_2 => tests/python/crocoddyl}/unittest_nonlinear.py (100%)
rename {scripts/crocoddyl_eval/test_2 => tests/python/crocoddyl}/unittest_step.py (100%)
diff --git a/CMakeLists.txt b/CMakeLists.txt
index c546d4b4..1bd332c2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -47,15 +47,16 @@ SEARCH_FOR_BOOST()
# Main Library
set(${PROJECT_NAME}_HEADERS
include/bindings/python.hpp
- include/qrw/Heightmap.hpp
+ include/qrw/Solo3D/Heightmap.hpp
+ include/qrw/Solo3D/FootTrajectoryGeneratorBezier.hpp
+ include/qrw/Solo3D/FootstepPlannerQP.hpp
+ include/qrw/Solo3D/StatePlanner3D.hpp
+ include/qrw/Solo3D/Surface.hpp
include/qrw/MPC.hpp
include/qrw/Gait.hpp
include/qrw/FootTrajectoryGenerator.hpp
- include/qrw/FootTrajectoryGeneratorBezier.hpp
include/qrw/FootstepPlanner.hpp
- include/qrw/FootstepPlannerQP.hpp
include/qrw/StatePlanner.hpp
- include/qrw/StatePlanner3D.hpp
include/qrw/Types.h
include/qrw/InvKin.hpp
include/qrw/QPWBC.hpp
@@ -69,22 +70,20 @@ set(${PROJECT_NAME}_HEADERS
include/qrw/MpcWrapper.hpp
include/qrw/FakeRobot.hpp
include/other/st_to_cc.hpp
- include/qrw/FootTrajectoryGeneratorBezier.hpp
- include/qrw/FootstepPlannerQP.hpp
- include/qrw/Surface.hpp
)
set(${PROJECT_NAME}_SOURCES
src/st_to_cc.cpp
- src/Heightmap.cpp
+ src/Solo3D/Heightmap.cpp
+ src/Solo3D/FootTrajectoryGeneratorBezier.cpp
+ src/Solo3D/FootstepPlannerQP.cpp
+ src/Solo3D/StatePlanner3D.cpp
+ src/Solo3D/Surface.cpp
src/MPC.cpp
src/Gait.cpp
src/FootTrajectoryGenerator.cpp
- src/FootTrajectoryGeneratorBezier.cpp
src/FootstepPlanner.cpp
- src/FootstepPlannerQP.cpp
src/StatePlanner.cpp
- src/StatePlanner3D.cpp
src/InvKin.cpp
src/QPWBC.cpp
src/WbcWrapper.cpp
@@ -95,9 +94,6 @@ set(${PROJECT_NAME}_SOURCES
src/Filter.cpp
src/Controller.cpp
src/MpcWrapper.cpp
- src/FootTrajectoryGeneratorBezier.cpp
- src/FootstepPlannerQP.cpp
- src/Surface.cpp
)
add_library(${PROJECT_NAME} SHARED ${${PROJECT_NAME}_SOURCES} ${${PROJECT_NAME}_HEADERS})
@@ -154,9 +150,8 @@ endif()
target_compile_options(${PROJECT_NAME} PUBLIC -DNDEBUG -O3)
-target_compile_definitions(${PROJECT_NAME} PUBLIC CONFIG_SOLO12_YAML="${PROJECT_SOURCE_DIR}/scripts/config_solo12.yaml")
-target_compile_definitions(${PROJECT_NAME} PUBLIC WALK_PARAMETERS_YAML="${PROJECT_SOURCE_DIR}/src/walk_parameters.yaml")
-
+target_compile_definitions(${PROJECT_NAME} PUBLIC CONFIG_SOLO12_YAML="${PROJECT_SOURCE_DIR}/config/config_solo12.yaml")
+target_compile_definitions(${PROJECT_NAME} PUBLIC WALK_PARAMETERS_YAML="${PROJECT_SOURCE_DIR}/config/walk_parameters.yaml")
# MQTT Interface
add_subdirectory (paho.mqtt.c)
diff --git a/scripts/Documentation/Equations_MPC_22_02_2020.pdf b/Documentation/Equations_MPC_22_02_2020.pdf
similarity index 100%
rename from scripts/Documentation/Equations_MPC_22_02_2020.pdf
rename to Documentation/Equations_MPC_22_02_2020.pdf
diff --git a/scripts/crocoddyl_doc/Evaluation_number_of_iterations.pdf b/Documentation/Evaluation_number_of_iterations.pdf
similarity index 100%
rename from scripts/crocoddyl_doc/Evaluation_number_of_iterations.pdf
rename to Documentation/Evaluation_number_of_iterations.pdf
diff --git a/scripts/crocoddyl_doc/Log_simulations.pdf b/Documentation/Log_simulations.pdf
similarity index 100%
rename from scripts/crocoddyl_doc/Log_simulations.pdf
rename to Documentation/Log_simulations.pdf
diff --git a/scripts/crocoddyl_doc/cost_function.pdf b/Documentation/cost_function.pdf
similarity index 100%
rename from scripts/crocoddyl_doc/cost_function.pdf
rename to Documentation/cost_function.pdf
diff --git a/scripts/crocoddyl_doc/dynamic_analysis.pdf b/Documentation/dynamic_analysis.pdf
similarity index 100%
rename from scripts/crocoddyl_doc/dynamic_analysis.pdf
rename to Documentation/dynamic_analysis.pdf
diff --git a/scripts/config_solo12.yaml b/config/config_solo12.yaml
similarity index 100%
rename from scripts/config_solo12.yaml
rename to config/config_solo12.yaml
diff --git a/src/walk_parameters.yaml b/config/walk_parameters.yaml
similarity index 98%
rename from src/walk_parameters.yaml
rename to config/walk_parameters.yaml
index 1d09a607..b8a25b29 100644
--- a/src/walk_parameters.yaml
+++ b/config/walk_parameters.yaml
@@ -24,7 +24,7 @@ robot:
q_init: [0.0, 0.7, -1.4, 0.0, 0.7, -1.4, 0.0, -0.7, 1.4, 0.0, -0.7, 1.4] # Initial articular positions
dt_wbc: 0.001 # Time step of the whole body control
dt_mpc: 0.02 # Time step of the model predictive control
- type_MPC: 0 # Which MPC solver you want to use: 0 for OSQP MPC, 1, 2, 3 for Crocoddyl MPCs
+ type_MPC: 3 # Which MPC solver you want to use: 0 for OSQP MPC, 1, 2, 3 for Crocoddyl MPCs
# Kp_main: [0.0, 0.0, 0.0] # Proportional gains for the PD+
Kp_main: [3.0, 3.0, 3.0] # Proportional gains for the PD+
# Kd_main: [0., 0., 0.] # Derivative gains for the PD+
diff --git a/include/qrw/Estimator.hpp b/include/qrw/Estimator.hpp
index d8c014f2..2e35fbaa 100644
--- a/include/qrw/Estimator.hpp
+++ b/include/qrw/Estimator.hpp
@@ -28,19 +28,19 @@ class Estimator {
////////////////////////////////////////////////////////////////////////////////////////////////
///
- /// \brief Initialize with given data
- ///
- /// \param[in] params Object that stores parameters
+ /// \brief Destructor.
///
////////////////////////////////////////////////////////////////////////////////////////////////
- void initialize(Params& params);
+ ~Estimator() {} // Empty destructor
////////////////////////////////////////////////////////////////////////////////////////////////
///
- /// \brief Destructor.
+ /// \brief Initialize with given data
+ ///
+ /// \param[in] params Object that stores parameters
///
////////////////////////////////////////////////////////////////////////////////////////////////
- ~Estimator() {} // Empty destructor
+ void initialize(Params& params);
////////////////////////////////////////////////////////////////////////////////////////////////
///
diff --git a/include/qrw/FakeRobot.hpp b/include/qrw/FakeRobot.hpp
index 96b9864d..0e32127d 100644
--- a/include/qrw/FakeRobot.hpp
+++ b/include/qrw/FakeRobot.hpp
@@ -9,8 +9,6 @@
#ifndef FAKEROBOT_H_INCLUDED
#define FAKEROBOT_H_INCLUDED
-#include <Eigen/Core>
-#include <Eigen/Dense>
#include "qrw/Types.h"
class FakeJoints {
diff --git a/include/qrw/Filter.hpp b/include/qrw/Filter.hpp
index b1ad99e3..65af7687 100644
--- a/include/qrw/Filter.hpp
+++ b/include/qrw/Filter.hpp
@@ -10,12 +10,8 @@
#ifndef FILTER_H_INCLUDED
#define FILTER_H_INCLUDED
-#include <cmath>
#include <deque>
-#include <fstream>
-#include <iostream>
-#include "pinocchio/math/rpy.hpp"
#include "qrw/Params.hpp"
class Filter {
diff --git a/include/qrw/FootTrajectoryGenerator.hpp b/include/qrw/FootTrajectoryGenerator.hpp
index 543731ad..a5f1866a 100644
--- a/include/qrw/FootTrajectoryGenerator.hpp
+++ b/include/qrw/FootTrajectoryGenerator.hpp
@@ -12,7 +12,6 @@
#include "qrw/Gait.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
class FootTrajectoryGenerator {
public:
diff --git a/include/qrw/FootstepPlanner.hpp b/include/qrw/FootstepPlanner.hpp
index 9a17ee53..09d9288f 100644
--- a/include/qrw/FootstepPlanner.hpp
+++ b/include/qrw/FootstepPlanner.hpp
@@ -11,15 +11,11 @@
#ifndef FOOTSTEPPLANNER_H_INCLUDED
#define FOOTSTEPPLANNER_H_INCLUDED
-#include "pinocchio/math/rpy.hpp"
#include "pinocchio/multibody/model.hpp"
#include "pinocchio/multibody/data.hpp"
-#include "pinocchio/parsers/urdf.hpp"
-#include "pinocchio/algorithm/compute-all-terms.hpp"
-#include "pinocchio/algorithm/frames.hpp"
+
#include "qrw/Gait.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
#include <vector>
// Order of feet/legs: FL, FR, HL, HR
diff --git a/include/qrw/Gait.hpp b/include/qrw/Gait.hpp
index b306a379..9b4f0b7a 100644
--- a/include/qrw/Gait.hpp
+++ b/include/qrw/Gait.hpp
@@ -13,7 +13,6 @@
#define GAIT_H_INCLUDED
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
// Order of feet/legs: FL, FR, HL, HR
diff --git a/include/qrw/InvKin.hpp b/include/qrw/InvKin.hpp
index 181098e6..b2259718 100644
--- a/include/qrw/InvKin.hpp
+++ b/include/qrw/InvKin.hpp
@@ -14,7 +14,6 @@
#include "pinocchio/multibody/data.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
class InvKin {
public:
diff --git a/include/qrw/Joystick.hpp b/include/qrw/Joystick.hpp
index 15e78c8e..08a8fcba 100644
--- a/include/qrw/Joystick.hpp
+++ b/include/qrw/Joystick.hpp
@@ -9,15 +9,10 @@
#ifndef JOYSTICK_H_INCLUDED
#define JOYSTICK_H_INCLUDED
-#include <fcntl.h>
-#include <linux/joystick.h>
-#include <stdio.h>
-#include <unistd.h>
-
#include <chrono>
+#include <linux/joystick.h>
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
struct gamepad_struct {
double v_x = 0.0; // Up/down status of left pad
@@ -57,11 +52,7 @@ class Joystick {
/// \brief Destructor.
///
////////////////////////////////////////////////////////////////////////////////////////////////
- ~Joystick() {
- if (js != -1) {
- close(js);
- }
- }
+ ~Joystick();
////////////////////////////////////////////////////////////////////////////////////////////////
///
@@ -146,7 +137,7 @@ class Joystick {
double dt_wbc = 0.0; // Time step of the WBC
int k_mpc = 0; // Number of WBC time step for one MPC time step
- VectorNi k_switch; // Key frames for the polynomial velocity interpolation
+ VectorNi k_switch; // Key frames for the polynomial velocity interpolation
Matrix6N v_switch; // Target velocity for the key frames
// How much the gamepad velocity and position is filtered to avoid sharp changes
@@ -155,8 +146,8 @@ class Joystick {
double gp_alpha_vel_heavy_filter = 0.002; // Low pass filter coefficient for v_ref_heavy_filter_
// Maximum velocity values
- double vXScale = 0.3; // Lateral
- double vYScale = 0.5; // Forward
+ double vXScale = 0.3; // Lateral
+ double vYScale = 0.5; // Forward
double vYawScale = 1.0; // Rotation
// Maximum position values
diff --git a/include/qrw/MpcWrapper.hpp b/include/qrw/MpcWrapper.hpp
index 5c85a5ba..bdf630e2 100644
--- a/include/qrw/MpcWrapper.hpp
+++ b/include/qrw/MpcWrapper.hpp
@@ -10,11 +10,6 @@
#define MPCWRAPPER_H_INCLUDED
#include "pinocchio/math/rpy.hpp"
-#include <Eigen/Core>
-#include <Eigen/Dense>
-#include <chrono>
-#include <thread>
-#include <mutex>
#include "qrw/Types.h"
#include "qrw/MPC.hpp"
diff --git a/include/qrw/FootTrajectoryGeneratorBezier.hpp b/include/qrw/Solo3D/FootTrajectoryGeneratorBezier.hpp
similarity index 97%
rename from include/qrw/FootTrajectoryGeneratorBezier.hpp
rename to include/qrw/Solo3D/FootTrajectoryGeneratorBezier.hpp
index 7cf1f09c..80b89b3d 100644
--- a/include/qrw/FootTrajectoryGeneratorBezier.hpp
+++ b/include/qrw/Solo3D/FootTrajectoryGeneratorBezier.hpp
@@ -10,23 +10,17 @@
#ifndef TRAJGEN_BEZIER_H_INCLUDED
#define TRAJGEN_BEZIER_H_INCLUDED
-#include <Eigen/Dense>
#include <vector>
#include "eiquadprog/eiquadprog-fast.hpp"
#include "ndcurves/bezier_curve.h"
#include "ndcurves/fwd.h"
#include "ndcurves/optimization/details.h"
-#include "pinocchio/algorithm/compute-all-terms.hpp"
-#include "pinocchio/algorithm/frames.hpp"
-#include "pinocchio/math/rpy.hpp"
#include "pinocchio/multibody/data.hpp"
#include "pinocchio/multibody/model.hpp"
-#include "pinocchio/parsers/urdf.hpp"
#include "qrw/Gait.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Surface.hpp"
-#include "qrw/Types.h"
+#include "qrw/Solo3D/Surface.hpp"
using namespace ndcurves;
using namespace eiquadprog::solvers;
diff --git a/include/qrw/FootstepPlannerQP.hpp b/include/qrw/Solo3D/FootstepPlannerQP.hpp
similarity index 98%
rename from include/qrw/FootstepPlannerQP.hpp
rename to include/qrw/Solo3D/FootstepPlannerQP.hpp
index 2c4ffd19..9a482b2f 100644
--- a/include/qrw/FootstepPlannerQP.hpp
+++ b/include/qrw/Solo3D/FootstepPlannerQP.hpp
@@ -14,16 +14,11 @@
#include <vector>
#include "eiquadprog/eiquadprog-fast.hpp"
-#include "pinocchio/algorithm/compute-all-terms.hpp"
-#include "pinocchio/algorithm/frames.hpp"
-#include "pinocchio/math/rpy.hpp"
#include "pinocchio/multibody/data.hpp"
#include "pinocchio/multibody/model.hpp"
-#include "pinocchio/parsers/urdf.hpp"
#include "qrw/Gait.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Surface.hpp"
-#include "qrw/Types.h"
+#include "qrw/Solo3D/Surface.hpp"
// Order of feet/legs: FL, FR, HL, HR
diff --git a/include/qrw/Heightmap.hpp b/include/qrw/Solo3D/Heightmap.hpp
similarity index 97%
rename from include/qrw/Heightmap.hpp
rename to include/qrw/Solo3D/Heightmap.hpp
index 124eb39d..8ae42851 100644
--- a/include/qrw/Heightmap.hpp
+++ b/include/qrw/Solo3D/Heightmap.hpp
@@ -9,15 +9,8 @@
#ifndef HEIGHTMAP_H_INCLUDED
#define HEIGHTMAP_H_INCLUDED
-#include <stdio.h>
-
-#include <Eigen/Dense>
-#include <fstream>
-#include <iostream>
-
#include "eiquadprog/eiquadprog-fast.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
using namespace std;
diff --git a/include/qrw/StatePlanner3D.hpp b/include/qrw/Solo3D/StatePlanner3D.hpp
similarity index 97%
rename from include/qrw/StatePlanner3D.hpp
rename to include/qrw/Solo3D/StatePlanner3D.hpp
index 261eea60..66945ea3 100644
--- a/include/qrw/StatePlanner3D.hpp
+++ b/include/qrw/Solo3D/StatePlanner3D.hpp
@@ -10,11 +10,8 @@
#ifndef STATEPLANNER3D_H_INCLUDED
#define STATEPLANNER3D_H_INCLUDED
-#include "pinocchio/math/rpy.hpp"
-#include "pinocchio/spatial/se3.hpp"
-#include "qrw/Heightmap.hpp"
+#include "qrw/Solo3D/Heightmap.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
class StatePlanner3D {
public:
diff --git a/include/qrw/Surface.hpp b/include/qrw/Solo3D/Surface.hpp
similarity index 100%
rename from include/qrw/Surface.hpp
rename to include/qrw/Solo3D/Surface.hpp
diff --git a/include/qrw/StatePlanner.hpp b/include/qrw/StatePlanner.hpp
index 7d8eedaa..9055eac0 100644
--- a/include/qrw/StatePlanner.hpp
+++ b/include/qrw/StatePlanner.hpp
@@ -10,9 +10,7 @@
#ifndef STATEPLANNER_H_INCLUDED
#define STATEPLANNER_H_INCLUDED
-#include "pinocchio/math/rpy.hpp"
#include "qrw/Params.hpp"
-#include "qrw/Types.h"
class StatePlanner {
public:
diff --git a/include/qrw/WbcWrapper.hpp b/include/qrw/WbcWrapper.hpp
index d74fe0b4..99c4eb51 100644
--- a/include/qrw/WbcWrapper.hpp
+++ b/include/qrw/WbcWrapper.hpp
@@ -12,8 +12,8 @@
#define WBC_WRAPPER_H_INCLUDED
#include "qrw/InvKin.hpp"
-#include "qrw/Params.hpp"
#include "qrw/QPWBC.hpp"
+#include "qrw/Params.hpp"
class WbcWrapper {
public:
diff --git a/python/Solo3D/FootTrajectoryGeneratorBezier.cpp b/python/Solo3D/FootTrajectoryGeneratorBezier.cpp
index 5acec150..16a09922 100644
--- a/python/Solo3D/FootTrajectoryGeneratorBezier.cpp
+++ b/python/Solo3D/FootTrajectoryGeneratorBezier.cpp
@@ -1,4 +1,4 @@
-#include "qrw/FootTrajectoryGeneratorBezier.hpp"
+#include "qrw/Solo3D/FootTrajectoryGeneratorBezier.hpp"
#include "bindings/python.hpp"
diff --git a/python/Solo3D/FootstepPlannerQP.cpp b/python/Solo3D/FootstepPlannerQP.cpp
index 0eabe80e..3fb91cc0 100644
--- a/python/Solo3D/FootstepPlannerQP.cpp
+++ b/python/Solo3D/FootstepPlannerQP.cpp
@@ -1,4 +1,4 @@
-#include "qrw/FootstepPlannerQP.hpp"
+#include "qrw/Solo3D/FootstepPlannerQP.hpp"
#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
#include "bindings/python.hpp"
diff --git a/python/Solo3D/StatePlanner3D.cpp b/python/Solo3D/StatePlanner3D.cpp
index 96dca84f..1ef2cc79 100644
--- a/python/Solo3D/StatePlanner3D.cpp
+++ b/python/Solo3D/StatePlanner3D.cpp
@@ -1,4 +1,4 @@
-#include "qrw/StatePlanner3D.hpp"
+#include "qrw/Solo3D/StatePlanner3D.hpp"
#include "bindings/python.hpp"
diff --git a/python/Solo3D/Surface.cpp b/python/Solo3D/Surface.cpp
index 6bc9a253..c15373ef 100644
--- a/python/Solo3D/Surface.cpp
+++ b/python/Solo3D/Surface.cpp
@@ -1,4 +1,4 @@
-#include "qrw/Surface.hpp"
+#include "qrw/Solo3D/Surface.hpp"
#include "bindings/python.hpp"
diff --git a/scripts/Controller.py b/scripts/Controller.py
index 8ea72b26..079fb719 100644
--- a/scripts/Controller.py
+++ b/scripts/Controller.py
@@ -1,13 +1,13 @@
import numpy as np
-import utils_mpc
import time
-import MPC_Wrapper
import pybullet as pyb
import pinocchio as pin
import quadruped_reactive_walking as qrw
-from solo3D.tools.utils import quaternionToRPY
+import MPC_Wrapper
+from tools.utils_mpc import init_robot
+from solo3D.utils import quaternionToRPY
class Result:
@@ -98,7 +98,7 @@ class Controller:
params.perfectEstimator = False # Cannot use perfect estimator if we are running on real robot
# Initialisation of the solo model/data and of the Gepetto viewer
- self.solo = utils_mpc.init_robot(q_init, params)
+ self.solo = init_robot(q_init, params)
# Create Joystick object
self.joystick = qrw.Joystick()
@@ -146,7 +146,7 @@ class Controller:
if params.solo3D:
from solo3D.SurfacePlannerWrapper import Surface_planner_wrapper
if self.SIMULATION:
- from solo3D.tools.pyb_environment_3D import PybEnvironment3D
+ from solo3D.pyb_environment_3D import PybEnvironment3D
self.enable_multiprocessing_mip = params.enable_multiprocessing_mip
self.offset_perfect_estimator = 0.
diff --git a/scripts/crocoddyl_class/MPC_crocoddyl.py b/scripts/Crocoddyl/MPC_crocoddyl.py
similarity index 100%
rename from scripts/crocoddyl_class/MPC_crocoddyl.py
rename to scripts/Crocoddyl/MPC_crocoddyl.py
diff --git a/scripts/crocoddyl_class/MPC_crocoddyl_2.py b/scripts/Crocoddyl/MPC_crocoddyl_2.py
similarity index 100%
rename from scripts/crocoddyl_class/MPC_crocoddyl_2.py
rename to scripts/Crocoddyl/MPC_crocoddyl_2.py
diff --git a/scripts/crocoddyl_class/MPC_crocoddyl_planner.py b/scripts/Crocoddyl/MPC_crocoddyl_planner.py
similarity index 100%
rename from scripts/crocoddyl_class/MPC_crocoddyl_planner.py
rename to scripts/Crocoddyl/MPC_crocoddyl_planner.py
diff --git a/scripts/Documentation/Equations_MPC_22_02_2020.aux b/scripts/Documentation/Equations_MPC_22_02_2020.aux
deleted file mode 100644
index 2771ee2b..00000000
--- a/scripts/Documentation/Equations_MPC_22_02_2020.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\providecommand\hyper@newdestlabel[2]{}
-\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
-\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
-\global\let\oldcontentsline\contentsline
-\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
-\global\let\oldnewlabel\newlabel
-\gdef\newlabel#1#2{\newlabelxx{#1}#2}
-\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
-\AtEndDocument{\ifx\hyper@anchor\@undefined
-\let\contentsline\oldcontentsline
-\let\newlabel\oldnewlabel
-\fi}
-\fi}
-\global\let\hyper@last\relax
-\gdef\HyperFirstAtBeginDocument#1{#1}
-\providecommand\HyField@AuxAddToFields[1]{}
-\providecommand\HyField@AuxAddToCoFields[2]{}
-\babel@aux{english}{}
-\@writefile{toc}{\contentsline {section}{\numberline {1}State Estimator}{1}{section.1}}
-\@writefile{toc}{\contentsline {section}{\numberline {2}MpcInterface}{1}{section.2}}
-\newlabel{eq:oMl_T}{{7}{2}{MpcInterface}{equation.2.7}{}}
-\newlabel{eq:oMl_R}{{8}{2}{MpcInterface}{equation.2.8}{}}
-\@writefile{toc}{\contentsline {section}{\numberline {3}Footstep Planner}{3}{section.3}}
-\newlabel{eq:gait0}{{18}{3}{Footstep Planner}{equation.3.18}{}}
-\newlabel{eq:gait123}{{19}{3}{Footstep Planner}{equation.3.19}{}}
-\@writefile{toc}{\contentsline {section}{\numberline {4}Foot trajectory generator}{5}{section.4}}
-\@writefile{toc}{\contentsline {section}{\numberline {5}State vector}{7}{section.5}}
-\@writefile{toc}{\contentsline {section}{\numberline {6}Dynamics equations and constraints}{9}{section.6}}
-\newlabel{eq:c_linear}{{49}{9}{Dynamics equations and constraints}{equation.6.49}{}}
-\newlabel{eq:c_angular}{{50}{9}{Dynamics equations and constraints}{equation.6.50}{}}
-\newlabel{eq:assumption1}{{51}{9}{Dynamics equations and constraints}{equation.6.51}{}}
-\newlabel{eq:c_angular_bis}{{54}{9}{Dynamics equations and constraints}{equation.6.54}{}}
-\newlabel{eq:approx_angular_velocity}{{55}{9}{Dynamics equations and constraints}{equation.6.55}{}}
-\@writefile{toc}{\contentsline {section}{\numberline {7}MPC matrices for dynamics and constraints}{11}{section.7}}
-\@writefile{toc}{\contentsline {section}{\numberline {8}MPC cost function}{13}{section.8}}
-\@writefile{toc}{\contentsline {section}{\numberline {9}Output of the MPC}{14}{section.9}}
-\@writefile{toc}{\contentsline {section}{\numberline {10}Inverse dynamics}{14}{section.10}}
diff --git a/scripts/Documentation/Equations_MPC_22_02_2020.out b/scripts/Documentation/Equations_MPC_22_02_2020.out
deleted file mode 100644
index e4dbcef9..00000000
--- a/scripts/Documentation/Equations_MPC_22_02_2020.out
+++ /dev/null
@@ -1,10 +0,0 @@
-\BOOKMARK [1][-]{section.1}{State Estimator}{}% 1
-\BOOKMARK [1][-]{section.2}{MpcInterface}{}% 2
-\BOOKMARK [1][-]{section.3}{Footstep Planner}{}% 3
-\BOOKMARK [1][-]{section.4}{Foot trajectory generator}{}% 4
-\BOOKMARK [1][-]{section.5}{State vector}{}% 5
-\BOOKMARK [1][-]{section.6}{Dynamics equations and constraints}{}% 6
-\BOOKMARK [1][-]{section.7}{MPC matrices for dynamics and constraints}{}% 7
-\BOOKMARK [1][-]{section.8}{MPC cost function}{}% 8
-\BOOKMARK [1][-]{section.9}{Output of the MPC}{}% 9
-\BOOKMARK [1][-]{section.10}{Inverse dynamics}{}% 10
diff --git a/scripts/Documentation/Equations_MPC_22_02_2020.synctex.gz b/scripts/Documentation/Equations_MPC_22_02_2020.synctex.gz
deleted file mode 100644
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diff --git a/scripts/Documentation/Equations_MPC_22_02_2020.tex b/scripts/Documentation/Equations_MPC_22_02_2020.tex
deleted file mode 100644
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@@ -1,645 +0,0 @@
-\documentclass[a4paper,11pt]{article}
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-% End of title area
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-\setlength{\parskip}{0.9em} % Space between paragraphs
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-\usepackage[utf8]{inputenc}
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-\title{Récapitulatif TSID-Rigid}
-\author{}
-\date{\today}
-
-%\usepackage{fancyhdr}{\headheight=15pt} % This should be set AFTER setting up the page geometry
-%\pagestyle{fancy} % options: empty , plain , fancy
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-%\lhead{Printemps 2019 | Pierre-Alexandre Léziart}\chead{}\rhead{Pense-bête pour l'oral de l'agrégation}
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-
-
-\begin{document}
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-%\maketitle
-
-\section*{Récapitulatif de la méthode MPC 09/04/2020}
-
-\vspace{0.8cm}
-\PRLsep
-\vspace{0.5cm}
-
-See \url{https://arxiv.org/pdf/1909.06586.pdf} for the original MIT article ``Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control''
-
-\section{State Estimator}
-
-The role of the state estimator is to provide an estimation of the position, orientation, linear velocity and angular velocity of the base of the quadruped as well as the angular position of the actuators. In the case of a simulation with PyBullet this information is perfectly known and can be retrieved with the API.
-
-The position/orientation state vector of the quadruped in world frame is:
-\begin{equation}
-{}^o\!q = \begin{bmatrix} {}^o\!x & {}^o\!y & {}^o\!z & {}^o\!a & {}^o\!b & {}^o\!c & {}^o\!d & \theta_0 & \dots & \theta_{11} \end{bmatrix}^T
-\end{equation}
-
-With $({}^o\!a,{}^o\!b,{}^o\!c,{}^o\!d)$ the quaternion associated with the orientation of the base in world frame (usually written $(x,y,z,w)$ but $x$, $y$ and $z$ are already used for the position). $\theta_{0}$ to $\theta_{11}$ are the angular positions of the 12 actuators of the quadruped.
-
-The velocity state vector of the quadruped in world frame is:
-\begin{equation}
-{}^o\!\dot q = \begin{bmatrix} {}^o\!\dot x & {}^o\!\dot y & {}^o\!\dot z & {}^o\!\omega_x & {}^o\!\omega_y & {}^o\!\omega_z & \dot \theta_0 & \dots & \dot \theta_{11} \end{bmatrix}^T
-\end{equation}
-
-With $({}^o\!\omega_x, {}^o\!\omega_y, {}^o\!\omega_z)$ the angular velocities about the $x$, $y$ and $z$ axes of the world frame.
-
-%With $({}^o\!\phi, {}^o\!\theta, {}^o\!\psi)$ the roll, pitch and yaw angles (Tait-Bryan Euler angles) associated with the quaternion $({}^o\!a,{}^o\!b,{}^o\!c,{}^o\!d)$.
-
-\section{MpcInterface}
-
-The role of the MpcInterface object is to transform data coming from PyBullet (simulation) or the state estimator of the robot (real world) into useful information for the rest of the control loop.
-
-Data coming from PyBullet is retrieved in the world frame $o$. The position of the base of the quadruped in this frame can be noted $[{}^o\!x ~ {}^o\!y ~ {}^o\!z]^T$ and its orientation $[{}^o\!\phi ~ {}^o\!\theta ~ {}^o\!\psi]^T$ with $(\phi, \theta, \psi)$ the roll, pitch and yaw angles (Tait-Bryan Euler angles). These angles corresponds to a sequence of rotations about the $z$, then $y$ and then $x$ axis such that the transform from body to world coordinates can be expressed as:
-\begin{equation}
- R = R_z(\psi) R_y(\theta) R_x(\phi)
-\end{equation}
-
-Position, orientation, linear velocity and angular velocity of the base of the quadruped in world frame can be transformed either into the base frame $b$ or into the local frame $l$, as defined in Figure X. The transform between two frames $1$ and $2$ can be stored in an object $\fM{1}{2}$ that contains the translation part $^1\!T_2$ and the rotation part $^1\!R_2$ of the transform. The relation between position $[^2\!x ~ ^2\!y ~ ^2\!z]^T$ in frame $2$ and the same position in frame $1$ is:
-\begin{equation}
-\begin{bmatrix}^1\!x \\ ^1\!y \\ ^1\!z \end{bmatrix} = {}^1\!R_2 \cdot \begin{bmatrix}^2\!x \\ ^2\!y \\ ^2\!z\end{bmatrix} + {}^1\!T_2 = \fM{1}{2} \cdot \begin{bmatrix}^2\!x \\ ^2\!y \\ ^2\!z\end{bmatrix}
-\end{equation}
-
-Based on Figure X the transforms are defined as follows:
-\begin{align}
-{}^o\!T_b &= \begin{bmatrix} {}^o\!x & {}^o\!y & {}^o\!z \end{bmatrix}^T \\
-{}^o\!R_b &= R_3({}^o\phi) \cdot R_3({}^o\theta) \cdot R_3({}^o\psi) \\
-{}^o\!T_l &= \begin{bmatrix} {}^o\!x & {}^o\!y & {}^o\!z_{min} \end{bmatrix}^T \label{eq:oMl_T}\\
-{}^o\!R_l &= R_3({}^o\psi) \label{eq:oMl_R}
-\end{align}
-
-%\begin{equation}
-%{}^o\!R_l = R_3({}^o\psi) = \begin{bmatrix} \cos({}^o\psi) & -\sin({}^o\psi) & 0 \\ \sin({}^o\psi) & \cos({}^o\psi) & 0 \\ 0 & 0 & 1\end{bmatrix} \text{ and } {}^o\!T_l = \begin{bmatrix} {}^o\!x \\ {}^o\!y \\ {}^o\!z_{min} \end{bmatrix}
-%\end{equation}
-
-${}^o\!z_{min}$ is the altitude of the lowest feet in world frame i.e. the $z$ coordinate of its center in this frame. Position of feet in world frame are retrieved from PyBullet.
-
-
-To get the position and velocity of the center of mass (CoM) of the quadruped, Pinocchio requires the position and orientation of the base in world frame, the angular positions of the actuators and the linear and angular velocities of the base in base frame. All of them are directly retrieved from PyBullet except the linear and angular velocities ${}^b\!V_{base}$ and ${}^b\!W_{base}$ in base frame: % /!\ Assumptions? Wrong? /!\
-\begin{align}
-{}^bV_{base} &= ({}^o\!R_b)^{-1} \cdot {}^oV_{base} \\
-{}^bW_{base} &= ({}^o\!R_b)^{-1} \cdot {}^oW_{base}
-\end{align}
-
-The resulting position and linear velocity of the CoM in world frame are noted ${}^o\!C$ and ${}^o\!V$. The angular velocity in world frame ${}^oW$ is directly retrieved from PyBullet (assumption that ${}^oW = {}^oW_{base}$), just like the orientation ${}^o\!RPY = [{}^o\!\phi ~ {}^o\!\theta ~ {}^o\!\psi]^T$.
-
-The position, orientation, linear velocity and angular velocity of the base of the quadruped in local frame can be retrieved using the transform $\fM{o}{l}$ :
-\begin{align}
-{}^l\!C &= (\fM{o}{l})^{-1} \cdot {}^o\!C \\
-{}^l\!RPY &= [{}^o\phi~~{}^o\theta~~0]^T \\
-{}^l\!V &= ({}^o\!R_l)^{-1} \cdot {}^o\!V \\
-{}^l\!W &= ({}^o\!R_l)^{-1} \cdot {}^o\!W
-\end{align}
-
-The projections on the ground of the shoulders of the quadruped are supposed constant even if in practice there is a dependence to roll and pitch. Order of shoulders is Front-Left, Front-Right, Hind-Left, Hind-Right:
-\begin{align}
-{}^l\!shoulders &= \begin{bmatrix} 0.19 & 0.19 & -0.19 & -0.19 \\ 0.15005 & -0.15005 & 0.15005 & -0.15005 \\ 0.0 & 0.0 & 0.0 & 0.0 \end{bmatrix} \\
-{}^o\!shoulders &= \fM{o}{l} \cdot {}^l\!shoulders
-\end{align}
-
-Positions of feet in world frame ${}^o\!r$ are directly retrieved from PyBullet and transformed in local frame for the MPC:
-\begin{equation}
-{}^l\!r = (\fM{o}{l})^{-1} \cdot {}^o\!r
-\end{equation}
-
-\newpage
-\section{Footstep Planner}
-
-The desired gait for the quadruped is defined in a gait matrix of size 6 by 5. Each row contains information about one phase of the gait. The first column contains the number of remaining time steps of the MPC for each phase and the four remaining columns contains the desired contact status for each foot and for each phase (0 if the foot is in swing phase or 1 if it is in stance phase).
-
-With a time step of 0.02 s for the MPC and a gait period of 0.32 s, the matrix of a walking trot gait is defined as follows:
-\begin{equation}
-gait(0) = \begin{bmatrix}
-1 & 1 & 1 & 1 & 1 \\
-7 & 1 & 0 & 0 & 1 \\
-1 & 1 & 1 & 1 & 1 \\
-7 & 0 & 1 & 1 & 0 \\
-0 & 0 & 0 & 0 & 0 \\
-0 & 0 & 0 & 0 & 0 \end{bmatrix} \label{eq:gait0}
-\end{equation}
-
-The first phase is a 4-feet stance phase that lasts 1 iteration of the MPC, followed by a phase with 2 feet in stance phase and the other 2 in swing phase during 7 iterations, then again a 4-feet stance phase and finally 2 feet in stance phase and 2 feet in swing phase. As the quadruped moves forward in the gait, the gait matrix undergoes a rolling process. For instance after 3 iterations of the MPC this matrix becomes:
-\begin{equation}
-gait(1) = \begin{bmatrix}
-7 & 1 & 0 & 0 & 1 \\
-1 & 1 & 1 & 1 & 1 \\
-7 & 0 & 1 & 1 & 0 \\
-1 & 1 & 1 & 1 & 1 \\
-0 & 0 & 0 & 0 & 0 \\
-0 & 0 & 0 & 0 & 0 \end{bmatrix}
-\text{ }
-gait(2) = \begin{bmatrix}
-6 & 1 & 0 & 0 & 1 \\
-1 & 1 & 1 & 1 & 1 \\
-7 & 0 & 1 & 1 & 0 \\
-1 & 1 & 1 & 1 & 1 \\
-1 & 1 & 0 & 0 & 1 \\
-0 & 0 & 0 & 0 & 0 \end{bmatrix}
-\text{ }
-gait(3) = \begin{bmatrix}
-5 & 1 & 0 & 0 & 1 \\
-1 & 1 & 1 & 1 & 1 \\
-7 & 0 & 1 & 1 & 0 \\
-1 & 1 & 1 & 1 & 1 \\
-2 & 1 & 0 & 0 & 1 \\
-0 & 0 & 0 & 0 & 0 \\\end{bmatrix} \label{eq:gait123}
-\end{equation}
-
-Additional rows could be added to be able to handle more complex gaits.
-
-The gait being defined, let's describe the way the location of future footsteps is computed. The footstep planner only works in 2 dimensions as it outputs the desired position $(r^{x, \star}, r^{y,\star})$ of each foot on the ground which is assumed flat. It only considers as input an horizontal linear reference velocity with an angular reference velocity about the vertical axis: $(\dot x^\star, \dot y^\star, \omega_{z}^\star)$.
-
-The default position of footsteps in local frame is on the ground under the shoulders:
-\begin{equation}
-{r}_{sh} = {}^l\!shoulders = \begin{bmatrix} 0.19 & 0.19 & -0.19 & -0.19 \\ 0.15005 & -0.15005 & 0.15005 & -0.15005 \end{bmatrix}
-\end{equation}
-
-A symmetry term is added to this position to make the gait more symmetric compared to the shoulders when moving. If the base moves forwards at speed $v$ then if a foot lands under its associated shoulder it will spend the whole stance phase ``behind'' the shoulder as the base keeps moving forwards while the foot in contact does not move (in world frame). During the duration of the stance phase, the displacement of the base is equal to $t_{stance} v$. That is why with the symmetry term trying to land $\frac{t_{stance}}{2} v$ in front of the shoulder feet in contact spend half the stance phase in front of the shoulder and the other half behind it.
-\begin{equation}
-{r}_{sym} = \frac{t_{stance}}{2} ~ {}^l\!v = \frac{t_{stance}}{2} \begin{bmatrix} {}^l\!\dot x \\ {}^l\!\dot y \end{bmatrix}
-\end{equation}
-
-A feedback term is added to the footstep planner to make it easier for the robot to reach the reference velocity. The only way the quadruped can interact with its environment is by pushing on the ground with its feet (cannot pull). As per Newton's second law, if the quadruped wants to move in a given direction it has to apply a force in the inverse direction. So the feedback term makes it easier to do that by shifting the desired location of footsteps in the inverse direction of the velocity error $(v^\star-v)$. For instance if the robot is not moving fast enough forwards then the feedback term will slightly shift the footsteps backwards so that it's easier to push on the ground backwards and as a result to increase its forward velocity.
-\begin{equation}
-{r}_{fb} = k ~ ({}^l\!v - {}^l\!v^\star) = k \begin{bmatrix} {}^l\!\dot x - {}^l\!\dot x^\star \\ {}^l\!\dot y - {}^l\!\dot y^\star \end{bmatrix}
-\end{equation}
-
-The feedback gain $k$ is equal to 0.03 (MIT's value).
-
-A centrifugal term is added to the footstep planner to make it easier to compensate the centrifugal effect when the robot is turning about the vertical axis by adjusting the location of footsteps accordingly. As the formula involves the desired angular speed rather than the current one, it could also be seen as a way to help the quadruped reach the reference angular velocity in a way similar to what the feedback term does for the linear velocity.
-\begin{equation}
-{r}_{c} = \frac{1}{2} \sqrt{\frac{h}{g}} ~ {}^l\!v \times {}^l\!\omega^\star = \frac{1}{2} \sqrt{\frac{h}{g}} \begin{bmatrix} {}^l\!\dot y ~ \omega_{{}^l\!z}^\star \\ - {}^l\!\dot x ~ \omega_{{}^l\!z}^\star \end{bmatrix}
-%k ~ ({}^l\!v - {}^l\!v^\star) = k \begin{bmatrix} {}^l\!\dot x - {}^l\!\dot x^\star \\ {}^l\!\dot y - {}^l\!\dot y^\star \end{bmatrix}
-\end{equation}
-
-Finally, another term is added to the footstep planning to take into account a temporal aspect. With all previous terms, there is none: whether a foot is just at the start of its swing phase or almost at the end, the desired target location returned by the footstep planner is the same. If the quadruped is moving forwards at the reference velocity then during the whole duration of a swing phase the target position will be $\Delta x$ meters in front of the shoulder in local frame. Except since the base is moving in world frame then the target position in world frame is moving as well. At the start of each swing phase the associated foot will target a position $x_0 + \Delta x$ in world frame but by the end of the swing phase this position becomes $x_0 + \Delta x + t_{swing} ~ \dot x^\star$ due to the movement of the base. With the assumption that current and reference velocities do not change much over one period of gait feet could directly aim for their final target location by taking into account the movement of the base during their swing phase.
-
-With the assumption that the quadruped moves with constant linear and angular velocities during the remaining duration of the swing phase then the predicted movement is, if $\omega_{{}^l\!z} \neq 0$:
-\begin{align}
-{}^l\!x_{pred}(t_r) &= \int_{0}^{t_r} \left( {}^l\!\dot x \cos(\omega_{{}^l\!z} ~ t) - {}^l\!\dot y \sin(\omega_{{}^l\!z} ~ t) \right) ~dt \\
-{}^l\!y_{pred}(t_r) &= \int_{0}^{t_r} \left( {}^l\!\dot x \sin(\omega_{{}^l\!z} ~ t) + {}^l\!\dot y \cos(\omega_{{}^l\!z} ~ t) \right) ~dt \\
-{}^l\!x_{pred}(t_r) &= \frac{{}^l\!\dot x \sin(\omega_{{}^l\!z} ~ t_r) + {}^l\!\dot y \left( \cos(\omega_{{}^l\!z} ~ t_r) - 1 \right)}{\omega_{{}^l\!z}} \\
-{}^l\!y_{pred}(t_r) &= \frac{- {}^l\!\dot x \left( \cos(\omega_{{}^l\!z} ~ t_r) - 1 \right) + {}^l\!\dot y \sin(\omega_{{}^l\!z} ~ t_r)}{\omega_{{}^l\!z}}
-\end{align}
-
-If $\omega_{{}^l\!z} = 0$:
-\begin{align}
-{}^l\!x_{pred}(t_r) &= {}^l\!\dot x ~ t_r \\
-{}^l\!y_{pred}(t_r) &= {}^l\!\dot y ~ t_r
-\end{align}
-
-The remaining duration $t_r$ for the swing phase of a foot can be directly retrieved using information contained in the gait matrix like the contact status (0 for a swing phase) and the remaining number of time steps (first column).
-
-The desired location of footsteps is the sum of all described terms. Symmetry, feedback and centrifugal terms are the same for all feet contrary to the shoulder and prediction terms.
-
-The desired location of footsteps for each gait phase in the prediction horizon are stored in a 6 by 13 matrix (same number of rows than the gait matrix). The first column is the same and contains the remaining number of footsteps for each phase. The twelve other columns contains the desired location of the footstep (if in stance phase) or Not-A-Number (if in swing phase) for the 4 feet. For instance for the $gait(1)$ matrix of equation \ref{eq:gait123} this matrix would be: \vspace{-0.6cm}
-
-\begin{center}
-\begin{tabular}{ccccccccccccccc}
- [ & $7$ & ${}^l\!r_0^x$ & ${}^l\!r_0^y$ & ${}^l\!r_0^z$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & ${}^l\!r_3^x$ & ${}^l\!r_3^y$ & ${}^l\!r_3^z$ & \\
- & $1$ & ${}^l\!r_0^x$ & ${}^l\!r_0^y$ & ${}^l\!r_0^z$ & ${}^l\!r_1^{x,\star}$ & ${}^l\!r_1^{y,\star}$ & 0 & ${}^l\!r_2^{x,\star}$ & ${}^l\!r_2^{y,\star}$ & 0 & ${}^l\!r_3^x$ & ${}^l\!r_3^y$ & ${}^l\!r_3^z$ & \\
- & $7$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & ${}^l\!r_1^{x,\star}$ & ${}^l\!r_1^{y,\star}$ & 0 & ${}^l\!r_2^{x,\star}$ & ${}^l\!r_2^{y,\star}$ & 0 & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & \\
- & $1$ & ${}^l\!r_0^{x,\star}$ & ${}^l\!r_0^{y,\star}$ & 0 & ${}^l\!r_1^{x,\star}$ & ${}^l\!r_1^{y,\star}$ & 0 & ${}^l\!r_2^{x,\star}$ & ${}^l\!r_2^{y,\star}$ & 0 & ${}^l\!r_3^{x,\star}$ & ${}^l\!r_3^{y,\star}$ & 0 & \\
- & 0 & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & \\
- & 0 & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & $\mathit{NaN}$ & ]
-\end{tabular}
-\end{center}
-
-The first row is a phase with two feet in swing phase (feet 1 and 2) and two feet in stance phase (feet 0 and 3). Feet 1 and 2 in swing phase receive a $\mathit{NaN}$ value while feet 0 and 3 in stance phase receive their desired location. Since the first row is the current active phase the desired position of feet in stance phase is their current position in local frame. Due to how $\fM{o}{l}$ has been defined in equation \ref{eq:oMl_T} and \ref{eq:oMl_R} and the fact that ${}^l\!r = (\fM{o}{l})^{-1} \cdot {}^o\!r$ that means ${}^l\!r_0^z = {}^o\!r_0^z - {}^o\!z_{min}$. For a ground that is completely flat ${}^o\!r_0^z = {}^o\!r_3^z = {}^o\!z_{min}$ so ${}^l\!r_0^z = {}^l\!r_3^z = 0$. However if foot 0 is on a small step then ${}^o\!r_3^z = {}^o\!z_{min}$ and ${}^o\!r_0^z = {}^o\!z_{min} + h_{step}$ so ${}^l\!r_3^z = 0$ and ${}^l\!r_0^z = h_{step}$. That way the fact that foot 0 is not strictly on the ground but on a small step is taken into account.
-
-For the second row (the next phase), feet 0 and 3 are still in stance phase so their desired position is still their current position. For feet 1 and 2 that will be in stance phase (they are currently in swing phase) their desired position is the one outputted by the footstep planner. It only provides desired position for the $x$ and $y$ components so for $z$ the assumption is made that the ground is flat (${}^l\!r_1^{z,\star} = {}^l\!r_2^{z,\star} = 0$) since there is no \textit{a priori} knowledge about the environment.
-
-For the third phase, feet 0 and 3 are now in swing phase so they have a $\mathit{NaN}$ value while feet 1 and 2 are still in stance phase compared to the previous phase so their desired positions do not change. For the fourth phase feet 0 and 3 are back in stance phase. Their current position is not used since this stance phase happens in the future after a swing phase so instead their desired position is the one outputted by the footstep planner.
-
-\section{Foot trajectory generator}
-
-$x$, $y$ and $z$ in this section replace ${}^o\!r^x$, ${}^o\!r^y$ and ${}^o\!r^z$ for clarity purpose.
-
-During swing phases feet have to be guided from their current position to their target position on the ground outputted by the footstep planner. To generate their trajectory in the air, foot trajectory generators are used, one for each foot. Each generator takes as input the current state of its associated foot $[x_{ft} ~ \dot x_{ft} ~ \ddot x_{ft} ~ y_{ft} ~ \dot y_{ft} ~ \ddot y_{ft}]$, the desired position on the ground $[x_{goal} ~ y_{goal}]$, the control time step $dt$, the time $t_0$ elapsed since the start of the swing phase and the expected duration $t_1$ of the swing phase. This information is processed to output a command $[x^\star ~ \dot x^\star ~ \ddot x^\star ~ y^\star ~ \dot y^\star ~ \ddot y^\star ~ z^\star ~ \dot z^\star ~ \ddot z^\star]$ for the foot. Generators work in the world frame and use data provided by the MpcInterface.
-
-For the $x$ component, what the generator does is to tune a 5-th polynomial to have $x(t_0) = x_{ft}$, $\dot x(t_0) = \dot x_{ft}$ and $\ddot x(t_0) = \ddot x_{ft}$ while having $x(t_1) = x_{goal}$, $\dot x(t_1) = \ddot x(t_1) = 0$. The generator can then output $[x^\star ~ \dot x^\star ~ \ddot x^\star]$ by computing $x(t_0 + dt)$, $\dot x(t_0 + dt)$ and $\ddot x(t_0 + dt)$. The same happens for the $y$ component to output $[y^\star ~ \dot y^\star ~ \ddot y^\star]$.
-
-Command for the $z$ component is deterministic and there is no feedback like for the $x$ and $y$ components for which the current position, velocity and acceleration of the foot are taken into account. Trajectory for $z$ is a 6-th order polynomial that does not change and defined in such a way that $z(0) = \dot z(0) = \ddot z(0) = 0$ and $z(t_1) = \dot z(t_1) = \ddot z(t_1) = 0$ with $z(\frac{t_1}{2}) = h$. $h$ is a constant value defined when the foot trajectory generator is created to set the desired apex height of the foot during its swing phase.
-
-Due to these characteristics, the trajectory generated can be described as a bell-like trajectory that goes from the initial position of the foot to its target trajectory while respecting non-slipping constraints during take-off and landing (no horizontal speed) and trying to land softly (0 final velocity and acceleration for $z$).
-
-To keep this slipping-avoidance property, the target position on the ground $[x_{goal} ~ y_{goal}]$ is locked $t_{lock}$ seconds before landing. Basically $[x_{goal} ~ y_{goal}]$ is not updated if $t_0 > (t_1-t_{lock})$. Changing the desired position on the ground just before landing would create a non-negligible horizontal speed to correct the position of the foot in order to land at the new position. It is required because the target position is always changing since it is linked to the current velocity of the robot through the symmetry term of the footstep planner and this velocity is never exactly the same from one time step to another.
-
-Since a 3D tracking task is in charge of applying the adequate torques to follow the command of the trajectory generator depending on the current state of the foot (more details in the Inverse Dynamics section), there is already a feedback for the position, velocity and acceleration of the foot. To avoid having two feedback loops that try to do the same thing, the feedback of the foot trajectory generator is not used. What that means is that at the start of the swing phase the trajectory generator receives $[x_{ft} ~ 0 ~ 0 ~ y_{ft} ~ 0 ~ 0]$ to update the position of the foot and then the command of the generator is supposed to be perfectly followed. Therefore at the next iteration the generator is given the command $[x^\star ~ \dot x^\star ~ \ddot x^\star ~ y^\star ~ \dot y^\star ~ \ddot y^\star]$ as the state of the foot. That way the feedback capabilities of the generator are not used.
-
-As explained earlier, the trajectory for the $z$ component is deterministic: it always starts at $z(0)=0$ m and ends at $z(t_1)=0$ m. That is why a small offset is added to the command $z^\star$ that is sent to the 3D tracking task to take into account the altitude of the ground the robot is walking on. This $z_{offset}$ is determined by taking the minimum altitude of all feet in contact with the ground which uses the assumption that there is at least one feet in contact when the other feet are in swing phase (equal to ${}^o\!z_{min}$ introduced in the MpcInterface section). With this offset the command received by the tracking task will start and end at the correct altitude.
-
-\newpage
-\section{State vector}
-
-
-The reference velocity $\dot q^\star$ that is sent to the robot is expressed in its local frame. It has 6 dimensions: 3 for the linear velocity and 3 for the angular one.
-\begin{equation}
-{}^l\! \dot q^\star = [{}^l\! \dot x^\star ~~ {}^l\! \dot y^\star ~~ {}^l\! \dot z^\star ~~ \omega_{{}^l\!x}^\star ~~ \omega_{{}^l\!y}^\star ~~ \omega_{{}^l\!z}^\star ]^T
-\end{equation}
-
-The velocity vector of the robot is:
-\begin{equation}
-{}^l\! \dot q = [{}^l\! \dot x ~~ {}^l\! \dot y ~~ {}^l\! \dot z ~~ \omega_{{}^l\!x} ~~ \omega_{{}^l\!y} ~~ \omega_{{}^l\!z} ]^T
-\end{equation}
-
-At the start each iteration of the MPC, the current position and orientation of the robot defines a new frame in which the solver will work. This frame is a copy of the local frame so it is at ground level with the $x$ axis pointing forwards ($x$ axis of the local frame), the $y$ axis pointing to the left ($y$ axis of the local frame) and the $z$ axis point upwards. Instead of working in terms of rotation around the $x$, $y$ and $z$ axes of the world frame, the solver will work with the pitch, roll and yaw angles defined in this new frame. The solver is working in a copy of the local frame, initial conditions of the solving process are as follows:
-\begin{align}
-q_0 &= [{}^l\!x ~~ {}^l\!y ~~ {}^l\!z ~~ {}^l\! \phi ~~ {}^l\! \theta ~~ {}^l\! \psi ]^T = [ {}^l\!C_x ~~ {}^l\!C_y ~~ {}^l\!C_z ~~ {}^l\! \phi ~~ {}^l\! \theta ~~ 0]^T \\
-\dot q_0 &= [{}^l\! \dot x ~~ {}^l\! \dot y ~~ {}^l\! \dot z ~~ \omega_{{}^l\!x} ~~ \omega_{{}^l\!y} ~~ \omega_{{}^l\!z} ]^T
-\end{align}
-
-With $[{}^l\! C_x ~~ {}^l\! C_y ~~ {}^l\! C_z ]$ the position of the center of mass in local frame.
-
-The state vector of the robot and the reference state vector are then:
-\begin{equation}X = \begin{bmatrix} q \\ \dot q \end{bmatrix} \text{~~~~}X^\star = \begin{bmatrix} q^{\star} \\ \dot q^{\star} \end{bmatrix}\end{equation}
-
-The reference velocity is supposed constant over the prediction horizon in the local frame of the robot so it has to be properly rotated to be consistent with its future orientation.
-
-For time step $k$ of the prediction horizon, the reference velocity vector is defined as follows:
-\begin{align}
-\forall k \in [1, n_{steps}], ~ \dot q_k^\star = \begin{bmatrix} R_z(\Delta t \cdot k \cdot \omega_{{}^l\!z}^\star) \\ R_z(\Delta t \cdot k \cdot \omega_{{}^l\!z}^\star) \end{bmatrix} \cdot {}^l\! \dot q^\star
-\end{align}
-
-with $R_z(\psi)$ the 3 by 3 rotation matrix by an angle $\psi$ about the vertical axis. There is no rotation about the roll and pitch axes due to the assumption that the trunk is almost horizontal.
-
-%with $\forall k, \dot q_k^\star = \dot q^\star$ since the reference velocity is supposed constant over the prediction horizon. $q_k^\star$ is obtained by integrating the $\dot q^\star$ starting from the current position vector $q_0$.
-%\begin{align}
-%q_0 &= [0 ~~ 0 ~~ z ~~ \phi ~~ \theta ~~ 0 ]^T \\
-%q_{k+1} &= q_k + R(\psi_k) \cdot \dot q^\star
-%\end{align}
-
-To get the reference position vector for all time steps of the prediction horizon an integration similar to the one that has been done for the prediction term of the footstep planner is performed. If $\omega_{{}^l\!z}^\star = 0$:
-\begin{equation}
-\forall k \in [1, n_{steps}], ~ q_{k}^\star = q_0 + k ~ \Delta t ~ {}^l\! \dot q^\star
-\end{equation}
-
-If $\omega_{{}^l\!z^\star} \neq 0$:
-\begin{align}
-x_k^\star &= {}^l\! C_x + \frac{{}^l\!\dot x^\star \sin(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) + {}^l\!\dot y^\star \left( \cos(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) - 1 \right)}{\omega_{{}^l\!z}^\star} \\
-y_k^\star &= {}^l\! C_y + \frac{- {}^l\!\dot x^\star \left( \cos(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) - 1 \right) + {}^l\!\dot y^\star \sin(\omega_{{}^l\!z}^\star ~ k ~ \Delta t)}{\omega_{{}^l\!z}^\star} \\
-z_k^\star &= {}^l\! C_z + k ~ \Delta t ~ {}^l\! \dot z^\star \\
-\phi_k^\star &= {}^l\! \phi + \frac{\omega_{{}^l\!x}^\star \sin(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) + \omega_{{}^l\!y}^\star \left( \cos(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) - 1 \right)}{\omega_{{}^l\!z}^\star} \\
-\theta_k^\star &= {}^l\! \theta + \frac{- \omega_{{}^l\!x}^\star \left( \cos(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) - 1 \right) + \omega_{{}^l\!y}^\star \sin(\omega_{{}^l\!z}^\star ~ k ~ \Delta t)}{\omega_{{}^l\!z}^\star} \\
-\psi_k^\star &= 0 + k ~ \Delta t ~ \omega_{{}^l\!z}^\star \\
-\end{align}
-
-%\begin{equation} q_{k+1}^\star = q_k^\star + \begin{bmatrix} R_z(\Delta t \cdot k \cdot \dot \phi^\star) \\ R_z(\Delta t \cdot k \cdot \dot \phi^\star) \end{bmatrix} \cdot \dot q_0^\star \end{equation}
-
-Previous equations could be used in a general case for which there is a velocity control for all linear and angular components. However, in our case, since we want the quadruped to move around while keeping the trunk horizontal and at constant height, we want a velocity control in $x$, $y$ and $\psi$ and a position control in $z$, $\phi$ and $\theta$ to keep $\forall t, ~z(t) = h$ and $\phi(t) = \theta(t) = 0~rad$. To avoid having too many feedback loop (reference velocity for $z$, $\phi$ and $\theta$ depending on the position error) we set $\forall k \in [1, n_{steps}]$:
-\begin{align}
-\dot z_k^\star = 0 \text{ and } z_k^\star = h \\
-\omega_{{}^l\!x}^\star = 0 \text{ and } \phi_k^\star = 0 \\
-\omega_{{}^l\!y}^\star = 0 \text{ and } \theta_k^\star = 0
-\end{align}
-
-To sum things up:
-\begin{equation}
- \forall k \in [1, n_{steps}], ~ X_k^\star = \begin{bmatrix}
- {}^l\! C_x + \frac{{}^l\!\dot x^\star \sin(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) + {}^l\!\dot y^\star \left( \cos(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) - 1 \right)}{\omega_{{}^l\!z}^\star} \\
- {}^l\! C_y + \frac{- {}^l\!\dot x^\star \left( \cos(\omega_{{}^l\!z}^\star ~ k ~ \Delta t) - 1 \right) + {}^l\!\dot y^\star \sin(\omega_{{}^l\!z}^\star ~ k ~ \Delta t)}{\omega_{{}^l\!z}^\star} \\
- h \\ 0 \\ 0 \\ k ~ \Delta t ~ \omega_{{}^l\!z}^\star \\
- {}^l\! \dot x^\star \cos(k ~ \Delta t ~ \omega_{{}^l\!z}^\star) - {}^l\! \dot y^\star \sin(k ~ \Delta t ~ \omega_{{}^l\!z}^\star) \\
- {}^l\! \dot x^\star \sin(k ~ \Delta t ~ \omega_{{}^l\!z}^\star) + {}^l\! \dot y^\star \cos(k ~ \Delta t ~ \omega_{{}^l\!z}^\star) \\ 0 \\ 0 \\ 0 \\ \omega_{{}^l\!z}^\star
- \end{bmatrix}
-\end{equation}
-
-
-
-The solver will work around the reference trajectory so we define the optimization state vector as follows:
-\begin{equation}\mathcal{X}_k = X_k - X_k^\star \end{equation}
-
-\newpage
-\section{Dynamics equations and constraints}
-
-The MPC works with a simple lumped mass model (centroidal dynamics). It can be written in world frame as follows:
-\begin{align}
- m ~ {}^o\! \ddot C &= \sum_{i=0}^{n_c - 1} {}^o\!f_i - \begin{bmatrix} 0 \\ 0 \\ mg \end{bmatrix} \label{eq:c_linear}\\
- %m \begin{bmatrix} {}^o\! \ddot x \\ {}^o\! \ddot y \\ {}^o\! \ddot z \end{bmatrix} &= \sum_{i=0}^{n_c - 1} {}^o\!f_i - \begin{bmatrix} 0 \\ 0 \\ mg \end{bmatrix} \\
- \frac{d}{dt}({}^o\!\mathcal{I} {}^o\!\omega) &= \sum_{i=0}^{n_c - 1} ({}^o\!r_i - {}^o\!C) \times {}^o\!f_i \label{eq:c_angular}
- %\frac{d}{dt}({}^o\!\mathcal{I} \cdot \begin{bmatrix} {}^o\! \dot \phi \\ {}^o\! \dot \theta \\ {}^o\! \dot \psi \end{bmatrix} ) &= \sum_{i=0}^{n_c - 1} ({}^o\!r_i - {}^o\!C) \times {}^o\!f_i
-\end{align}
-
-With $n_c$ the number of footholds, ${}^o\!f_i$ the reaction forces, ${}^o\!r_i$ the location of contact points, ${}^o\!C$ the position of the center of mass, ${}^o\!\mathcal{I}$ the rotational inertia tensor and ${}^o\!w$ the angular velocity of the body.
-
-The first assumption is that roll and pitch angles are small, it follows that:
-\begin{align}
-\begin{bmatrix} {}^o\! \dot \phi \\ {}^o\! \dot \theta \\ {}^o\! \dot \psi \end{bmatrix} &\approx R_z(\psi)^{-1} \cdot {}^o\!\omega \label{eq:assumption1} \\
-{}^o\! \mathcal{I} &\approx R_z(\psi) \cdot {}^b\! \mathcal{I} \cdot R_z(\psi)^{-1}
-\end{align}
-
-The second assumption is that states are close to the desired trajectory so in equation \ref{eq:c_angular} we can replace the position of the center of mass ${}^o\!C$ by the desired position for the center of mass.
-
-The last assumption is that pitch and roll velocities are small so:
-\begin{equation}
- \frac{d}{dt}({}^o\!\mathcal{I} {}^o\!\omega) = {}^o\!\mathcal{I} {}^o\! \dot \omega + {}^o\! \omega \times ({}^o\!\mathcal{I} {}^o\! \omega) \approx {}^o\!\mathcal{I} {}^o\! \dot \omega
-\end{equation}
-
-With these assumptions, equation \ref{eq:c_angular} is simplified into:
-\begin{equation}
-{}^o\!\mathcal{I} ~ {}^o\! \dot \omega = \sum_{i=0}^{n_c - 1} ({}^o\!r_i - {}^o\!C^\star) \times {}^o\!f_i \label{eq:c_angular_bis}
-\end{equation}
-
-The local frame that the solver is working in is actually the world frame rotated by $\psi$ about the vertical axis $z$ so equation \ref{eq:assumption1} becomes:
-\begin{equation}
-\begin{bmatrix} {}^o\! \dot \phi \\ {}^o\! \dot \theta \\ {}^o\! \dot \psi \end{bmatrix} \approx {}^l\!\omega \label{eq:approx_angular_velocity}
-\end{equation}
-
-Equations \ref{eq:c_linear} and \ref{eq:c_angular_bis} can be written in local frame:
-\begin{align}
-m ~ R_z(\psi)^{-1} {}^l\! \ddot C &= \sum_{i=0}^{n_c - 1} R_z(\psi)^{-1} {}^l\!f_i - R_z(\psi)^{-1} \begin{bmatrix} 0 \\ 0 \\ mg \end{bmatrix} \\
-R_z(\psi)^{-1} {}^l\!\mathcal{I} R_z(\psi) ~ R_z(\psi)^{-1} {}^l\! \dot \omega &= \sum_{i=0}^{n_c - 1} R_z(\psi)^{-1} ({}^l\!r_i - {}^l\!C^\star) \times R_z(\psi)^{-1} {}^l\!f_i
-\end{align}
-
-As cross product is invariant by rotation these equations result in:
-\begin{align}
-m ~ {}^l\! \ddot C &= \sum_{i=0}^{n_c - 1} {}^l\!f_i - \begin{bmatrix} 0 \\ 0 \\ mg \end{bmatrix} \\
-{}^l\!\mathcal{I} ~ {}^l\! \dot \omega &= \sum_{i=0}^{n_c - 1} ({}^l\!r_i - {}^l\!C^\star) \times {}^l\!f_i
-\end{align}
-
-Once discretized and considering equation \ref{eq:approx_angular_velocity}, evolution of state variables becomes $\forall k \in [0, n_{steps}-1]$:
-\begin{align}
-\begin{bmatrix} {}^l\! x_{k+1} \\ {}^l\! y_{k+1} \\ {}^l\! z_{k+1} \end{bmatrix} &= \begin{bmatrix} {}^l\! x_{k} \\ {}^l\! y_{k} \\ {}^l\! z_{k} \end{bmatrix} + \Delta t \begin{bmatrix} {}^l\! \dot x_{k} \\ {}^l\! \dot y_{k} \\ {}^l\! \dot z_{k} \end{bmatrix} \\
-\begin{bmatrix} {}^l\! \phi_{k+1} \\ {}^l\! \theta_{k+1} \\ {}^l\! \psi_{k+1} \end{bmatrix} &= \begin{bmatrix} {}^l\! \phi_{k} \\ {}^l\! \theta_{k} \\ {}^l\! \psi_{k} \end{bmatrix} + \Delta t \begin{bmatrix} \omega_{{}^l\!x,k} \\ \omega_{{}^l\!y,k} \\ \omega_{{}^l\! z,k} \end{bmatrix} \\
-\begin{bmatrix} {}^l\! \dot x_{k+1} \\ {}^l\! \dot y_{k+1} \\ {}^l\! \dot z_{k+1} \end{bmatrix} &= \begin{bmatrix} {}^l\! \dot x_{k} \\ {}^l\! \dot y_{k} \\ {}^l\! \dot z_{k} \end{bmatrix} + \Delta t \left( \sum_{i=0}^{n_{c,k} - 1} \frac{{}^l\!f_{i,k}}{m} - \begin{bmatrix} 0 \\ 0 \\ g \end{bmatrix} \right) \\
-\begin{bmatrix} \omega_{{}^l\!x,k+1} \\ \omega_{{}^l\!y,k+1} \\ \omega_{{}^l\! z,k+1} \end{bmatrix} &= \begin{bmatrix} \omega_{{}^l\!x,k} \\ \omega_{{}^l\!y,k} \\ \omega_{{}^l\! z,k} \end{bmatrix} + \Delta t \left( {}^l\!\mathcal{I}^{-1}\sum_{i=0}^{n_{c,k} - 1} [{}^l\!r_{i,k} - {}^l\!C_{k}^\star]_\times \cdot {}^l\!f_{i,k} \right)
-\end{align}
-
-In terms of constraints, friction cone conditions to avoid slipping are linearized to the first order:
-\begin{equation}
-\forall i \in [0, 3], \forall k \in [0, n_{steps}-1],~|f_{i,k}^x| \leq \mu ~ f_{i,k}^z \text{ and } |f_{i,k}^y| \leq \mu ~ f_{i,k}^z
-\end{equation}
-
-An upper limit has to be set for contact forces to respect hardware limits (maximum torque of actuators). This limit is only applied to the $z$ component since it will also limit the force along $x$ and $y$ due to the friction cone contraints.
-\begin{equation}
-\forall i \in [0, 3], \forall k \in [0, n_{steps}-1],~f_{i,k}^z \leq f_{max}
-\end{equation}
-
-The quadruped cannot pull on the ground, it can only push so the normal component of the contact forces has to be positive:
-\begin{equation}
-\forall i \in [0, 3], \forall k \in [0, n_{steps}-1], f_{i,k}^z \geq 0~N
-\end{equation}
-
-To be sure that there is no slipping, we could impose a minimal non-zero vertical component of the contact forces because if it is close to 0 N the friction cone is small so on the real robot slipping could happen. In practise to due the way the MPC is currently programmed to disable a foot when it is in swing phase we set a constraint that its contact force is equal to 0 so it is not directly compatible with $\forall i \in [0, 3], \forall k \in [0, n_{steps}-1], f_{i,k}^z \geq f_{min}$. We would have to change more coefficients to temporarily disable this $f_{i,k}^z \geq f_{min}$ for feet in swing phase.
-
-This minimal non-zero vertical component of the contact forces is taken into account by the inverse dynamics block (TSID) so even if the output of the MPC contains a 0 N vertical component for a foot in contact it will be equal to $f_{min}$ after being processed by TSID.
-
-
-\newpage
-\section{MPC matrices for dynamics and constraints}
-
-\textbf{Goal:} create the matrices that are used by standard QP solvers. These solvers try to find a vector $\mathbb{X}$ that minimizes a cost function $\frac{1}{2}\mathbb{X}^T.P.\mathbb{X} + \mathbb{X}^T.Q$ under constraints $M.\mathbb{X} = N$ and $L.\mathbb{X} \leq K$. In this section the construction of matrices $M$, $N$, $L$ and $K$ is described.
-
-The evolution of the state vector of the robot over time can be described as follows:
-\begin{equation}
-x(k+1) = A(k) x(k) + B(k) f(k) + g
-\end{equation}
-
-Matrices A et B depends on $k$ and $g = [0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ -9.81 \cdot \Delta t~ 0 ~ 0 ~ 0]^T$
-
-The contact forces vector $f(k) = f_k$ always include the forces applied on the four feet even if some of them are not touching the ground. In that case we will set the problem in such a way that forces for such feet are not considered in the solving process.
-\begin{align}
-f_k &= [f_{0,k}^T ~~ f_{1,k}^T ~~ f_{2,k}^T ~~ f_{3,k}^T]^T \\
-\forall i \in [0, 3],~k\in[0, n_{steps}-1], ~f_{i, k} &= \begin{bmatrix} f^x_{i,k} \\ f^y_{i,k} \\ f^z_{i,k} \end{bmatrix}
-\end{align}
-
-with $f^x_{i,k}$, $f^y_{i,k}$ and $f^z_{i,k}$ the components along the $x$, $y$ and $z$ axes of the solver frame for the $i$-th foothold at time step $k$.
-
-\textbf{Let's consider a case with only 3 time steps in the prediction horizon.}
-
-The goal of the MPC is to find contacts forces $f$ that should be applied to have the state vector $X$ of the robot as close as possible to $X^\star$. The QP solver outputs at the end of the optimization process the optimization vector $\mathbb{X}$ that minimizes the cost function locally (globally in the best case). The QP problem can be written in a simple way by putting both $f$ (the output of the MPC) and $\mathcal{X}_k = X_k - X_k^\star$ (quantity that should be minimized) in the optimization vector:
-\begin{equation}
-\mathbb{X} = [\mathcal{X}_1^T ~~ \mathcal{X}_2^T ~~ \mathcal{X}_3^T ~~ f_0^T ~~ f_1^T ~~ f_2^T ]^T
-\end{equation}
-
-
-Matrix $M$ is defined as follows:
-\begin{equation}
-M = \begin{bmatrix}
--I_{12} & 0_{12} & 0_{12} & B_0 & 0_{12} & 0_{12} \\
-A_1 & -I_{12} & 0_{12} & 0_{12} & B_1 & 0_{12} \\
-0_{12} & A_2 & -I_{12} & 0_{12} & 0_{12} & B_2 \\
-0_{12} & 0_{12} & 0_{12} & E_0 & 0_{12} & 0_{12} \\
-0_{12} & 0_{12} & 0_{12} & 0_{12} & E_1 & 0_{12} \\
-0_{12} & 0_{12} & 0_{12} & 0_{12} & 0_{12} & E_2 \\ \end{bmatrix}
-\end{equation}
-
-$A$, $B$ and $E$ have a size of 12 by 12.
-
-Matrix $N$ is defined as follows:
-\begin{equation}
-N = \begin{bmatrix} -g \\ -g \\ -g \\ 0_{12 \times 1}Â \\Â 0_{12 \times 1} \\Â 0_{12 \times 1} \end{bmatrix} + \begin{bmatrix} -A_0 X_0 \\ 0_{12 \times 1} \\ 0_{12 \times 1} \\ 0_{12 \times 1} \\ 0_{12 \times 1} \\ 0_{12 \times 1} \end{bmatrix} + \begin{bmatrix} I_{12} & 0_{12} & 0_{12} & 0_{12} & 0_{12} & 0_{12}\\ -A_1 & I_{12} & 0_{12} & 0_{12} & 0_{12} & 0_{12} \\ 0_{12} & -A_2 & I_{12} & 0_{12} & 0_{12} & 0_{12}\end{bmatrix} \cdot \begin{bmatrix} X_1^\star \\ X_2^\star \\ X_3^\star \\ 0_{12 \times 1} \\ 0_{12 \times 1} \\ 0_{12 \times 1} \end{bmatrix}
-\end{equation}
-
-Matrix $A_k$ for time step $k$ is defined as follows:
-\begin{equation}
-A_k = \begin{bmatrix}
-I_3 & 0_3 & \Delta t \cdot I_3 & 0_3 \\
-0_3 & I_3 & 0_3 & \Delta t \cdot I_3 \\
-0_3 & 0_3 & I_3 & 0_3 \\
-0_3 & 0_3 & 0_3 & I_3
-\end{bmatrix}
-\end{equation}
-
-%with $R(\psi_k = \psi_0+k~dt~\dot \psi^\star)$ the 3 by 3 rotation matrix about the vertical axis.
-
-With the assumption that roll and pitch angles are small the inertia matrix of the robot in solver frame rotated according to the orientation of the robot at time step $k$ is:
-\begin{equation}
-{}^l\! \mathcal{I}_k = R_z(\Delta t \cdot k \cdot \omega_{{}^l\!z}^\star) \cdot {}^b\!\mathcal{I}
-\end{equation}
-
-Matrix $B_k$ for time step $k$ is defined as follows:
-\begin{equation}
-B = \Delta t \cdot \begin{bmatrix}
-0_3 & 0_3 & 0_3 & 0_3 \\
-0_3 & 0_3 & 0_3 & 0_3 \\
-I_3/m & I_3/m & I_3/m & I_3/m \\
-{}^l\!\mathcal{I}_k^{-1} \cdot [{}^l\! r_{0,k} - {}^l\! C_k^\star]_\times & {}^l\!\mathcal{I}_k^{-1} \cdot [{}^l\! r_{1,k} - {}^l\! C_k^\star]_\times & {}^l\!\mathcal{I}_k^{-1} \cdot [{}^l\! r_{2,k} - {}^l\! C_k^\star]_\times & {}^l\!\mathcal{I}_k^{-1} \cdot [{}^l\! r_{3,k} - {}^l\! C_k^\star]_\times
-\end{bmatrix}
-\end{equation}
-
-with $({}^l\!r_{i,k} - {}^l\! C_k^\star)$ the vector in local frame going from the desired position of the center of mass at time step $k$ to the position of the $i$-th foothold. $[r_{k,i} - {}^l\! C_k^\star]_\times$ is the associated skew-symmetric matrix.
-
-Matrix $E_k$ for time step $k$ is defined as follows:
-\begin{equation}
-E_k = \begin{bmatrix}
-e_{0,k} & 0_3 & 0_3 & 0_3 \\
-0_3 & e_{1,k} & 0_3 & 0_3 \\
-0_3 & 0_3 & e_{2,k} & 0_3 \\
-0_3 & 0_3 & 0_3 & e_{3,k}
-\end{bmatrix}
-\end{equation}
-
-$e_{i,k} = 0_3$ if the $i$-th foot is touching the ground during time step $k$, $e_{i,k} = I_3$ otherwise. In fact, if $e_{i,k} = I_3$ then with $M.X = N$ we are setting the constraint that $f_{i,k} = [0 ~~ 0 ~~ 0]^T$ (no reaction force since the foot is not touching the ground).
-
-Matrix $L$ is defined as follows:
-\begin{equation}
-L = \begin{bmatrix}
- 0_{20\times12} & 0_{20\times12} & 0_{20\times12} & F_\mu & 0_{20\times12} & 0_{20\times12} \\
- 0_{20\times12} & 0_{20\times12} & 0_{20\times12} & 0_{20\times12} & F_\mu & 0_{20\times12} \\
- 0_{20\times12} & 0_{20\times12} & 0_{20\times12} & 0_{20\times12} & 0_{20\times12} & F_\mu \end{bmatrix}
-\end{equation}
-
-With:
-\begin{equation}
-F_\mu = \begin{bmatrix}
- G & 0_{5\times3} & 0_{5\times3} & 0_{5\times3} \\
- 0_{5\times3} & G & 0_{5\times3} & 0_{5\times3} \\
- 0_{5\times3} & 0_{5\times3} & G & 0_{5\times3} \\
- 0_{5\times3} & 0_{5\times3} & 0_{5\times3} & G
-\end{bmatrix} \text{ and } G = \begin{bmatrix}
-1 & 0 & -\mu \\
--1 & 0 & -\mu \\
-0 & 1 & -\mu \\
-0 & -1 & -\mu \\
-0 & 0 & - 1 \\
-\end{bmatrix}
-\end{equation}
-
-The $K$ matrix is defined as $K = 0_{60 \times 1}$.
-
-\section{MPC cost function}
-
-
-QP solvers try to find a vector $\mathbb{X}$ that minimizes a cost function $\frac{1}{2}\mathbb{X}^T.P.\mathbb{X} + \mathbb{X}^T.Q$ under constraints $M.\mathbb{X} = N$ and $L.\mathbb{X} \leq K$. Matrices $P$ and $Q$ define the shape of the cost function.
-
-The goal of the MPC is to find which contact forces should be applied at contact points so that the predicted trajectory of the center of mass is as close as possible to the reference trajectory. With previous notation, that means we want to minimize $|X - X^\star|$. Function $|\cdot|$ is not directly available with a matrix product $\frac{1}{2}\mathbb{X}^T.P.\mathbb{X} + \mathbb{X}^T.Q$ so we can try to minimize $(X - X^\star)^2$ instead. Since:
-\begin{equation}
-\mathbb{X} = \begin{bmatrix} \mathcal{X}_1 \\ \vdots \\ \mathcal{X}_{n_{steps}} \\ f_0 \\ \vdots \\ f_{n_{steps}-1} \end{bmatrix} = \begin{bmatrix} X_1 - X_1^\star \\ \vdots \\ X_{n_{steps}} - X_{n_{steps}}^\star\\ f_0 \\ \vdots \\ f_{n_{steps}-1} \end{bmatrix}
-\end{equation}
-
-then the upper left portion of $P$ can be diagonal:
-\begin{equation}
-P = \begin{bmatrix}
-c_{X,1} & & 0 & * \\
- & \ddots & & * \\
-0 & & c_{X,n_{steps}} & * \\
-* & * & * & *
-\end{bmatrix}
-\end{equation}
-
-With $\forall k \in [1, n_{steps}],~ c_{X,k}$ being 12 by 12 diagonal matrices with coefficients $\geq 0$ the deviation from the reference trajectory is penalized by the cost function as it push the solver into minimizing the error $(X_k - X_k^\star)^2$. For safety reason, for energy consumption and for the actuators, it is better to keep the contact forces low if possible. That is why a small regularization term is added to slightly penalize the norm of contact forces. Since the $\sqrt{\cdot}$ function is not directly available in the matrix product of the cost function, we regularize the square of the norm instead ($\Vert f_k \Vert^2$).
-
-\begin{equation}
-P = \begin{bmatrix}
-c_{X,1} & & 0 & * & * & *\\
-& \ddots & & * & * & * \\
-0 & & c_{X,n_{steps}} & * & * & * \\
-* & * & * & c_{f,0} & & 0 \\
-* & * & * & & \ddots & \\
-* & * & * & 0 & & c_{f,n_{steps}-1}
-\end{bmatrix}
-\end{equation}
-
-With $\forall k \in [0, n_{steps}-1],~ c_{f,k}$ being 12 by 12 diagonal matrices with coefficients $\geq 0$. There is no cross-coupling between $\mathcal{X}$ and force components so the upper-right and lower-left parts of $P$ are zeros.
-
-As there is no focus on any part of the prediction horizon, all $c_{X,k}$ are equal, same of all $c_{f,k}$. Coefficient at position $[i,i]$ in $c_{X,k}$ weights the deviation of the $i$-th component of the state vector from the reference trajectory. Remember that components of the state vector are in this order: $[ {}^l\!x ~ {}^l\!y ~ {}^l\!z ~ {}^l\!\phi ~ {}^l\!\theta ~ {}^l\!\psi ~ {}^l\!\dot x ~ {}^l\!\dot y ~{}^l\! \dot z ~ \omega_{{}^l\!x} ~ \omega_{{}^l\!y} ~ \omega_{{}^l\! z} ]$. Coefficient at position $[i,i]$ in $c_{f,k}$ weights the $i$-th component of the force vector for regularization purpose. Remember that components of the force vector are in this order: $[ {}^l\!f_0^x ~ {}^l\!f_0^y ~ {}^l\!f_0^z ~ {}^l\!f_1^x ~ {}^l\!f_1^y ~ {}^l\!f_1^z ~ {}^l\!f_2^x ~ {}^l\!f_2^y ~ {}^l\!f_2^z ~ v {}^l\!f_3^x ~ {}^l\!f_3^y ~ {}^l\!f_3^z ]$. To regularize properly the norm of contact forces $\Vert f_{i,k} \Vert^2 = (f_{i,k}^{x})^2 + (f_{i,k}^{y})^2 + (f_{i,k}^{z})^2$ coefficients for the $x$, $y$ and $z$ components have to be equal:
-\begin{equation}
- \forall k \in [0, n_{steps}-1],~\forall i \in [0, 3],~ c_{f,k}[3i,3i] = c_{f,k}[3i+1,3i+1] = c_{f,k}[3i+2,3i+2]
-\end{equation}
-
-If no leg is privileged (to mimic a wounded leg we would try to apply less force with it) then all coefficients on the diagonal of $c_{X,k}$ are equal and $\forall k \in [0, n_{steps}-1],~c_{X,k}=w_f I_{12}$ with $w_f \in \mathbb{R}^+$
-
-Matrix $Q$ in $\mathbb{X}^T \cdot Q$ only contains zeroes since there is no reason to push $X_k - X_k^\star$ or $f_k$ into being as negative/positive as possible. For instance if a coefficient of $Q$ is positive then the solver will try to have the associated variable as negative as possible to have a high negative product between the coefficient and the variable since that minimizes the cost.
-
-In the 3 time steps example of the previous section $P$ has a size of 72 by 72 (12 x 3 lines/columns for $\mathcal{X}_{1,2,3}$ and 12 x 3 lines/columns for $f_{1,2,3}$) and $Q$ has a size size 72 by 1.
-
-The cost function during the optimization process is then:
-\begin{equation}
-cost(X - X^\star, f) = \sum_{k=1}^{n_{steps}} \left( \sum_{i=0}^{11} \left[ w_{X}^i (X_k^i - X_k^{i,\star})^2 \right] + w_f \sum_{i=0}^{3} \left[ (f_{i,k}^x)^2 + (f_{i,k}^y)^2 + (f_{i,k}^z)^2 \right] \right)
-\end{equation}
-
-
-%Matrix $P$ in the cost function $\frac{1}{2}\mathbb{X}^T.P.\mathbb{X} + \mathbb{X}^T.Q$ is diagonal. That way the first term of the cost function looks like $\sum_{k=1}^{3} c_{k,X} \cdot (X_1 - X_1^\star)^2 + \sum_{k=0}^{2} c_{k,f} \cdot f_k^2$ with $c_{k,X}$ and $c_{k,f}$ with size $(12, 1)$. With $c_{k,X} > 0$ it push the solver into minimizing the error $X_k - X_k^\star$ to the reference trajectory that we want to follow while $c_{k,f}$ are small positive coefficients that act as a regularization of the force to get a solution with a norm as low as possible for the reaction force.
-
-
-
-%\section*{Update of solver matrices}
-%
-%In the $M$ matrix:
-%\begin{itemize}
-%\item $A_k$ matrices are constant and all the same
-%\item $[r_{k,i}]$ need to be updated at each iteration since the robot moves between each iteration
-%\item $\mathcal{I}_k^{-1}$ need to be updated if the reference velocity vector is modified
-%\item $E_{k,i}$ in the lower part of M need to be set to $I_3$ if the $j$-th foot was previously touching the ground and is not touching it anymore. And to $0_3$ if it was not touching the ground and is now touching it.
-%\end{itemize}
-%
-%In the $N$ matrix:
-%\begin{itemize}
-% \item $X_0$ need to be updated at each iteration since the robot moves between each iteration
-% \item $- g$ never changes
-% \item $X_k^\star$ need to be updated if the reference velocity vector is modified
-%\end{itemize}
-%
-%In the $L$ matrix:
-%\begin{itemize}
-% \item Columns of $F_\mu$ matrices have to be set to zero or set back to non-zero values depending on the states of the contacts (just like the columns of $B_k$ matrices)
-%\end{itemize}
-%
-%The $K$ matrix never change and is always full of zeros.
-
-\section{Output of the MPC}
-
-The desired reaction forces that need to be applied (by TSID or the real robot) are stored in $f_0$. It contains the desired reaction forces in local frame so they will have to be brought back to the world frame that TSID is working in.
-
-The same applies for the next desired position of the robot that is stored in $\mathcal{X}_1$ and can be retrieved by adding $X_1^\star$ to $\mathcal{X}_1$. As the next position/orientation is expressed in local frame we would have to rotate them to be able to use them for the inverse dynamics.
-
-\section{Inverse dynamics}
-
-The goal of the inverse dynamics is to make the link between the high level control (MPC contact forces and desired position of footsteps) and the low level control (desired torques sent to the drivers of the actuators). To do that, we use Efficient Task Space Inverse Dynamics (TSID, \url{https://github.com/stack-of-tasks/tsid}). This library allows to perform task-orientated optimization-based inverse-dynamics control based on the rigid multi-body dynamics library Pinocchio. Unlike the MPC it does not consider a prediction horizon but only the current state of the robot and the current defined tasks.
-
-Currently four kind of tasks are being used:
-\begin{itemize}
- \item[$\bullet$] Contact tasks for feet in contact with the ground to inform the solver that these feet should not move and that they can be used to apply forces on the ground.
- \item[$\bullet$] Force tasks to have the contact forces close to the desired contact forces outputted by the MPC. These tasks are associated with the contact tasks.
- \item[$\bullet$] Tracking tasks for feet in swing phase to follow the 3D trajectory generated by the foot trajectory generator to land at the position desired by the footstep planner.
- \item[$\bullet$] Posture task for all legs to get back to a default position if some degrees of freedom are not used.
-\end{itemize}
-
-One instance of the first three task is initially created and assigned to each foot. Then during the gait these tasks are enabled or disabled depending on the state of the foot. In swing phase only the tracking task is active while in stance phase only contact and force tasks are enabled. There exists a single posture task which is always active and affect the whole body.
-
-A weight is assigned to each task to make it more or less important compared to the other ones. ``Contact + Force'' and 3D tracking are never active at the same time so they do not compete with each other. As the posture task is just intended to be use as a form of regularization, it should not interfere with the other tasks. Its weight is kept at least $10^{-2}$ times less than the others to mimic a hierarchical solver: the relative weight is so small that it does not impact the other tasks even if in practice all tasks are considered together during the solving process.
-
-If the $i$-th foot is in stance phase then the force reference of its force task is updated with the desired contact force $[f^x_{i,0} ~ f^y_{i,0} ~ f^z_{i,0}]$ outputted by the MPC. If the $i$-th foot is in swing phase then its tracking task is updated with the desired position $[x^\star ~ y^\star ~ z^\star]$, velocity $[\dot x^\star ~ \dot y^\star ~ \dot z^\star]$ and acceleration $[\ddot x^\star ~\ddot y^\star ~ \ddot z^\star]$ outputted by the foot trajectory generator associated with this foot.
-
-The inverse dynamics solver is first updated with the current state of the quadruped (position/orientation/velocity of the base and angular position/velocity of the joints). It then tries to find the accelerations (base+joints) and the contact forces that minimize the cost function (weighted sum of task errors) while respecting the constraints (contacts, dynamics equations, torque limits). Joint torques can be retrieved at the end of the optimization thanks to the accelerations and the contact forces.
-
-Retrieved torques are sent to the PyBullet simulator that makes the assumption that torques are perfectly followed by the actuators. On the real robot they would be sent to the drivers that would have to modulate the current sent to the actuators to get the desired torques.
-
-
-\end{document}
\ No newline at end of file
diff --git a/scripts/LICENSE b/scripts/LICENSE
deleted file mode 100644
index fa966dac..00000000
--- a/scripts/LICENSE
+++ /dev/null
@@ -1,25 +0,0 @@
-BSD 2-Clause License
-
-Copyright (c) 2020, paLeziart
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
- list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/scripts/Logger.py b/scripts/Logger.py
deleted file mode 100644
index 86048232..00000000
--- a/scripts/Logger.py
+++ /dev/null
@@ -1,944 +0,0 @@
-# coding: utf8
-
-import numpy as np
-#from matplotlib import pyplot as plt
-import pybullet as pyb
-import pinocchio as pin
-# import scipy.stats as scipystats
-from matplotlib import cm
-
-
-class Logger:
- """Logger object to store information about plenty of different quantities in the simulation
-
- Args:
- k_max_loop (int): maximum number of iterations of the simulation
- dt (float): time step of TSID
- dt_mpc (float): time step of the MPC
- k_mpc (int): number of tsid iterations for one iteration of the mpc
- T_mpc (float): duration of mpc prediction horizon
- type_MPC (int): 0 for OSQP MPC, 1, 2, 3 for Crocoddyl MPCs
- """
-
- def __init__(self, k_max_loop, dt, dt_mpc, k_mpc, T_mpc, type_MPC):
-
- # Max number of iterations of the main loop
- self.k_max_loop = k_max_loop
-
- # Time step of TSID
- self.dt = dt
-
- # Time step of MPC
- self.dt_mpc = dt_mpc
-
- # Number of TSID steps for 1 step of the MPC
- self.k_mpc = k_mpc
-
- # Which MPC solver you want to use
- # True to have PA's MPC, to False to have Thomas's MPC
- self.type_MPC = type_MPC
-
- """# Log state vector and reference state vector
- self.log_state = np.zeros((12, k_max_loop))
- self.log_state_ref = np.zeros((12, k_max_loop))
-
- # Log footholds position in world frame
- self.log_footholds = np.zeros((4, k_max_loop, 2))
- self.log_footholds_w = np.zeros((4, k_max_loop, 2))
-
- # Log a few predicted trajectories along time to see how it changes
- self.log_predicted_traj = np.zeros((12, int((k_max_loop/20)), 60))
- self.log_predicted_fc = np.zeros((12, int((k_max_loop/20)), 60))
-
- # Log contact forces
- self.log_contact_forces = np.zeros((4, k_max_loop, 3))"""
-
- ###
- ###
-
- # Store time range
- self.t_range = np.array([k*dt for k in range(self.k_max_loop)])
-
- # Store logging timestamps
- self.tstamps = np.zeros(k_max_loop)
-
- # Store current and desired position, velocity and acceleration of feet over time
- # Used in log_footsteps function
- self.feet_pos = np.zeros((3, 4, k_max_loop))
- self.feet_vel = np.zeros((3, 4, k_max_loop))
- self.feet_acc = np.zeros((3, 4, k_max_loop))
- self.feet_pos_target = np.zeros((3, 4, k_max_loop))
- self.feet_vel_target = np.zeros((3, 4, k_max_loop))
- self.feet_acc_target = np.zeros((3, 4, k_max_loop))
-
- # Store information about the state of the robot
- self.RPY = np.zeros((3, k_max_loop)) # roll, pitch, yaw of the base in world frame
- self.oC = np.zeros((3, k_max_loop)) # Â position of the CoM in world frame
- self.oV = np.zeros((3, k_max_loop)) # Â linear velocity of the CoM in world frame
- self.oW = np.zeros((3, k_max_loop)) # angular velocity of the CoM in world frame
- self.lC = np.zeros((3, k_max_loop)) # Â position of the CoM in local frame
- self.lV = np.zeros((3, k_max_loop)) # Â linear velocity of the CoM in local frame
- self.lW = np.zeros((3, k_max_loop)) # Â angular velocity of the CoM in local frame
- self.state_ref = np.zeros((12, k_max_loop)) # Â reference state vector
- self.mot = np.zeros((12, k_max_loop)) # Â angular position of actuators
- self.Vmot = np.zeros((12, k_max_loop)) # Â angular velocity of actuators
-
- # Position and velocity data in PyBullet simulation
- self.lC_pyb = np.zeros((3, k_max_loop))
- self.RPY_pyb = np.zeros((3, k_max_loop))
- self.mot_pyb = np.zeros((12, k_max_loop))
- self.lV_pyb = np.zeros((3, k_max_loop))
- self.lW_pyb = np.zeros((3, k_max_loop))
- self.Vmot_pyb = np.zeros((12, k_max_loop))
-
- # Store information about contact forces
- self.forces_order = [0, 1, 2, 3]
- self.forces_status = [1, 1, 1, 1]
- self.forces_mpc = np.zeros((12, k_max_loop)) # output of MPC
- self.forces_tsid = np.zeros((12, k_max_loop)) # output of TSID
- self.forces_pyb = np.zeros((12, k_max_loop)) # forces applied in PyBullet
-
- # Store information about torques
- self.torques_ff = np.zeros((12, k_max_loop))
- self.torques_pd = np.zeros((12, k_max_loop))
- self.torques_sent = np.zeros((12, k_max_loop))
-
- # Store information about the cost function of the MPC
- self.cost_components = np.zeros((13, k_max_loop))
-
- # Store information about the predicted evolution of the optimization vector components
- # Usefull to compare osqp & ddp solver
- self.T = T_mpc
- self.pred_trajectories = np.zeros((12, int(T_mpc/dt_mpc), int(k_max_loop/k_mpc)))
- self.pred_forces = np.zeros((12, int(T_mpc/dt_mpc), int(k_max_loop/k_mpc)))
- self.fsteps = np.zeros((20, 13, int(k_max_loop/k_mpc)))
- self.xref = np.zeros((12, int(T_mpc/dt_mpc) + 1, int(k_max_loop/k_mpc)))
-
- # Store information about one of the tracking task
- self.pos = np.zeros((12, k_max_loop))
- self.pos_ref = np.zeros((12, k_max_loop))
- self.pos_err = np.zeros((3, k_max_loop))
- self.vel = np.zeros((6, k_max_loop))
- self.vel_ref = np.zeros((6, k_max_loop))
- self.vel_err = np.zeros((3, k_max_loop))
-
- # Store information about shoulder position
- self.o_shoulders = np.zeros((3, 4,k_max_loop))
-
- def log_footsteps(self, k, interface, tsid_controller):
- """ Store current and desired position, velocity and acceleration of feet over time
- """
-
- self.feet_pos[:, :, k] = interface.o_feet
- self.feet_vel[:, :, k] = interface.ov_feet
- self.feet_acc[:, :, k] = interface.oa_feet
- self.feet_pos_target[:, :, k] = tsid_controller.goals.copy()
- self.feet_vel_target[:, :, k] = tsid_controller.vgoals.copy()
- self.feet_acc_target[:, :, k] = tsid_controller.agoals.copy()
-
- # Shoulder position in world frame
- self.o_shoulders[:, :, k] = interface.o_shoulders
-
- return 0
-
- def plot_footsteps(self):
- """ Plot current and desired position, velocity and acceleration of feet over time
- """
-
- # Target feet positions are only updated during swing phase so during the initial stance phase these
- # positions are 0.0 for x, y, z even if it is not the initial feet positions
- # For display purpose we set feet target positions to feet positions from the beginning of the simulation to
- # the start of the first swing phase
- # Instead of having 0.0 0.0 0.0 0.19 0.19 0.19 (step when the first swing phase start and the target position
- # is updated) we have 0.19 0.19 0.19 0.19 0.19 0.19 (avoid the step on the graph)
- for i in range(4):
- index = next((idx for idx, val in np.ndenumerate(
- self.feet_pos_target[0, i, :]) if ((not (val == 0.0)))), [-1])[0]
- if index > 0:
- for j in range(2):
- self.feet_pos_target[j, i, :(index+1)] = self.feet_pos_target[j, i, index+1]
-
- index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- lgd_X = ["FL", "FR", "HL", "HR"]
- lgd_Y = ["Pos X", "Pos Y", "Pos Z"]
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
- plt.plot(self.t_range, self.feet_pos[i % 3, np.int(i/3), :], color='b', linewidth=3, marker='')
- plt.plot(self.t_range, self.feet_pos_target[i % 3, np.int(i/3), :], color='r', linewidth=3, marker='')
- plt.legend([lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)], lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+" Ref"])
- plt.suptitle("Current and reference positions of feet (world frame)")
-
- index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- lgd_X = ["FL", "FR", "HL", "HR"]
- lgd_Y = ["Vel X", "Vel Y", "Vel Z"]
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
- plt.plot(self.t_range, self.feet_vel[i % 3, np.int(i/3), :], color='b', linewidth=3, marker='')
- plt.plot(self.t_range, self.feet_vel_target[i % 3, np.int(i/3), :], color='r', linewidth=3, marker='')
- plt.legend([lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)], lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+" Ref"])
- plt.suptitle("Current and reference velocities of feet (world frame)")
-
- index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- lgd_X = ["FL", "FR", "HL", "HR"]
- lgd_Y = ["Acc X", "Acc Y", "Acc Z"]
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
- plt.plot(self.t_range, self.feet_acc[i % 3, np.int(i/3), :], color='b', linewidth=3, marker='')
- plt.plot(self.t_range, self.feet_acc_target[i % 3, np.int(i/3), :], color='r', linewidth=3, marker='')
- plt.legend([lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)], lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+" Ref"])
- plt.suptitle("Current and reference accelerations of feet (world frame)")
-
- return 0
-
- def log_state(self, k, pyb_sim, joystick, interface, mpc_wrapper, solo):
- """ Store information about the state of the robot
- """
-
- self.RPY[:, k:(k+1)] = np.reshape(interface.RPY[:], (3, 1)) # roll, pitch, yaw of the base in world frame
- self.oC[:, k:(k+1)] = np.reshape(interface.oC[:, 0], (3, 1)) # Â position of the CoM in world frame
- self.oV[:, k:(k+1)] = np.reshape(interface.oV[:, 0], (3, 1)) # Â linear velocity of the CoM in world frame
- self.oW[:, k] = interface.oW[:, 0] # angular velocity of the CoM in world frame
- self.lC[:, k:(k+1)] = np.reshape(interface.lC[:, 0], (3, 1)) # Â position of the CoM in local frame
- self.lV[:, k:(k+1)] = np.reshape(interface.lV[:, 0], (3, 1)) # Â linear velocity of the CoM in local frame
- self.lW[:, k:(k+1)] = np.reshape(interface.lW[:, 0], (3, 1)) # Â angular velocity of the CoM in local frame
- self.mot[:, k:(k+1)] = interface.mot[:, 0:1]
- self.Vmot[:, k:(k+1)] = interface.vmes12_base[6:, 0:1]
-
- # Get PyBullet velocity in base frame for Pinocchio
- oRb = pin.Quaternion(pyb_sim.qmes12[3:7]).matrix()
- vmes12_base = pyb_sim.vmes12.copy()
- vmes12_base[0:3, 0:1] = oRb.transpose() @ vmes12_base[0:3, 0:1]
- vmes12_base[3:6, 0:1] = oRb.transpose() @ vmes12_base[3:6, 0:1]
-
- # Get CoM position in PyBullet simulation
- pin.centerOfMass(solo.model, solo.data, pyb_sim.qmes12, vmes12_base)
-
- self.RPY_pyb[:, k:(k+1)] = np.reshape(pin.rpy.matrixToRpy((pin.SE3(pin.Quaternion(pyb_sim.qmes12[3:7]),
- np.array([0.0, 0.0, 0.0]))).rotation),
- (3, 1))
- oMl = pin.SE3(pin.utils.rotate('z', self.RPY_pyb[2, k]),
- np.array([pyb_sim.qmes12[0, 0], pyb_sim.qmes12[1, 0], interface.mean_feet_z]))
-
- # Store data about PyBullet simulation
- self.lC_pyb[:, k:(k+1)] = np.reshape(oMl.inverse() * solo.data.com[0], (3, 1))
- # self.RPY_pyb[2, k:(k+1)] = 0.0
- self.mot_pyb[:, k:(k+1)] = pyb_sim.qmes12[7:, 0:1]
- self.lV_pyb[:, k:(k+1)] = np.reshape(oMl.rotation.transpose() @ solo.data.vcom[0], (3, 1))
- self.lW_pyb[:, k:(k+1)] = np.reshape(oMl.rotation.transpose() @ pyb_sim.vmes12[3:6, 0:1], (3, 1))
- self.Vmot_pyb[:, k:(k+1)] = pyb_sim.vmes12[6:, 0:1]
-
- # Reference state vector in local frame
- # Velocity control for x, y and yaw components (user input)
- # Position control for z, roll and pitch components (hardcoded default values of h_ref, 0.0 and 0.0)
- # if self.type_MPC:
- # self.state_ref[0:6, k] = np.array([0.0, 0.0, mpc_wrapper.mpc.h_ref, 0.0, 0.0, 0.0])
- # else:
- self.state_ref[0:6, k] = np.array([0.0, 0.0, 0.2027682, 0.0, 0.0, 0.0])
- self.state_ref[6:12, k] = joystick.v_ref[:, 0]
-
- return 0
-
- def plot_state(self):
- """ Plot information about the state of the robot
- """
-
- index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- lgd = ["Pos CoM X [m]", "Pos CoM Y [m]", "Pos CoM Z [m]", "Roll [deg]", "Pitch [deg]", "Yaw [deg]",
- "Lin Vel CoM X [m/s]", "Lin Vel CoM Y [m/s]", "Lin Vel CoM Z [m/s]", "Ang Vel Roll [deg/s]",
- "Ang Vel Pitch [deg/s]", "Ang Vel Yaw [deg/s]"]
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
- if i < 3:
- plt.plot(self.t_range, self.lC[i, :], "b", linewidth=3)
- plt.plot(self.t_range, self.lC_pyb[i, :], "g", linewidth=3)
- elif i < 6:
- plt.plot(self.t_range, (180/3.1415)*self.RPY[i-3, :], "b", linewidth=3)
- plt.plot(self.t_range, (180/3.1415)*self.RPY_pyb[i-3, :], "g", linewidth=3)
- elif i < 9:
- plt.plot(self.t_range, self.lV[i-6, :], "b", linewidth=3)
- plt.plot(self.t_range, self.lV_pyb[i-6, :], "g", linewidth=3)
- else:
- plt.plot(self.t_range, (180/3.1415)*self.lW[i-9, :], "b", linewidth=3)
- plt.plot(self.t_range, (180/3.1415)*self.lW_pyb[i-9, :], "g", linewidth=3)
-
- if i in [2, 6, 7, 8]:
- plt.plot(self.t_range, self.state_ref[i, :], "r", linewidth=3)
- elif i in [3, 4, 9, 10, 11]:
- plt.plot(self.t_range, (180/3.1415)*self.state_ref[i, :], "r", linewidth=3)
-
- plt.xlabel("Time [s]")
- plt.ylabel(lgd[i])
-
- plt.legend(["TSID", "Pyb", "Ref"])
-
- if i < 2:
- plt.ylim([-0.07, 0.07])
- elif i == 2:
- plt.ylim([0.16, 0.24])
- elif i < 6:
- plt.ylim([-10, 10])
- elif i == 6:
- plt.ylim([-0.05, 0.7])
- elif i == 7:
- plt.ylim([-0.1, 0.1])
- elif i == 8:
- plt.ylim([-0.2, 0.2])
- else:
- plt.ylim([-80.0, 80.0])
-
- plt.suptitle("State of the robot in TSID Vs PyBullet Vs Reference (local frame)")
-
- index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
-
- lgd = ["DoF 1 FL", "DoF 2 FL", "DoF 3 FL", "DoF 1 FR", "DoF 2 FR", "DoF 3 FR",
- "DoF 1 HL", "DoF 2 HL", "DoF 3 HL", "DoF 1 HR", "DoF 2 HR", "DoF 3 HR"]
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
- h1, = plt.plot(self.t_range, self.mot[i, :], color="b", linewidth=3)
- h2, = plt.plot(self.t_range, self.mot_pyb[i, :], color="g", linewidth=3)
- plt.legend([h1, h2], [lgd[i] + " TSID", lgd[i] + " Pyb"])
- plt.xlabel("Time [s]")
- plt.ylabel("Angular position [rad]")
- plt.suptitle("Angular positions of actuators in TSID and PyBullet")
-
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
- h1, = plt.plot(self.t_range, self.Vmot[i, :], color="b", linewidth=3)
- h2, = plt.plot(self.t_range, self.Vmot_pyb[i, :], color="g", linewidth=3)
- plt.legend([h1, h2], [lgd[i] + " TSID", lgd[i] + " Pyb"])
- plt.xlabel("Time [s]")
- plt.ylabel("Angular velocity [rad/s]")
- plt.suptitle("Angular velocities of actuators in TSID and PyBullet")
-
- print_correlation = False
- if not print_correlation:
- return 0
-
- R = np.zeros((12, 12))
-
- plt.figure()
- lgd = ["Pos CoM X [m]", "Pos CoM Y [m]", "Pos CoM Z [m]",
- "Roll [rad]", "Pitch [rad]", "Yaw [rad]",
- "Vel CoM X [m]", "Vel CoM Y [m]", "Vel CoM Z [m]",
- "Ang Vel Roll", "Ang Vel Pitch", "Ang Vel Yaw"]
- for i in range(12):
- for j in range(12):
- plt.subplot(12, 12, (11-i) * 12 + j + 1)
- if i < 3:
- x1 = self.lC[i, :]
- x2 = self.lC_pyb[i, :]
- if i == 2:
- x1[0] = x1[1]
- elif i < 6:
- x1 = self.RPY[i-3, :]
- x2 = self.RPY_pyb[i-3, :]
- elif i < 9:
- x1 = self.lV[i-6, :]
- x2 = self.lV_pyb[i-6, :]
- else:
- x1 = self.lW[i-9, :]
- x2 = self.lW_pyb[i-9, :]
- if j < 3:
- y1 = self.lC[j, :]
- y2 = self.lC_pyb[j, :]
- if j == 2:
- y1[0] = y1[1]
- elif j < 6:
- y1 = self.RPY[j-3, :]
- y2 = self.RPY_pyb[j-3, :]
- elif j < 9:
- y1 = self.lV[j-6, :]
- y2 = self.lV_pyb[j-6, :]
- else:
- y1 = self.lW[j-9, :]
- y2 = self.lW_pyb[j-9, :]
-
- plt.plot(y1, x1, color="b", linestyle='', marker='*', markersize=6)
- plt.tick_params(
- axis='both', # changes apply to the x-axis
- which='both', # both major and minor ticks are affected
- bottom=False, # ticks along the bottom edge are off
- left=False, # ticks along the top edge are off
- labelbottom=False, # labels along the bottom edge are off
- labelleft=False)
- if i == 0:
- plt.xlabel(lgd[j])
- if j == 0:
- plt.ylabel(lgd[i])
-
- # slope, intercept, r_value, p_value, std_err = scipystats.linregress(x1, y1)
-
- R[i, j] = r_value
- plt.suptitle("Correlation of state variables")
-
- cmap = cm.get_cmap('gist_heat', 256)
- fig = plt.figure()
- ax = fig.gca()
- psm = ax.pcolormesh(R, cmap=cmap, rasterized=True, vmin=0, vmax=1)
- fig.colorbar(psm, ax=ax)
- plt.suptitle("R coefficient of a first order regression")
-
- return 0
-
- def getContactPoint(self, contactPoints):
- """ Sort contacts points as there should be only one contact per foot
- and sometimes PyBullet detect several of them. There is one contact
- with a non zero force and the others have a zero contact force
- """
-
- indexes = []
- for i in range(0, len(contactPoints)):
- # There may be several contact points for each foot but only one of them as a non zero normal force
- if (contactPoints[i][9] != 0):
- indexes.append(i)
-
- if len(indexes) > 0:
- # Return contact points
- return [contactPoints[i] for i in indexes]
- else:
- # If it returns 0 then it means there is no contact point with a non zero normal force (should not happen)
- return [0]
-
- def log_forces(self, k, interface, tsid_controller, robotId, planeId):
- """ Store information about contact forces
- """
-
- """if (k > 0) and (k % 20 == 0):
-
- # Index of the first empty line
- index = next((idx for idx, val in np.ndenumerate(fstep_planner.gait[:, 0]) if val == 0.0), 0.0)[0]
-
- # Change of state (+1 if start of stance phase, -1 if start of swing phase, 0 if no state change)
- if not np.array_equal(fstep_planner.gait[0, 1:], fstep_planner.gait[index-1, 1:]):
- self.forces_status = fstep_planner.gait[0, 1:] - fstep_planner.gait[index-1, 1:]
-
- # Update contact forces order
- for i in range(4):
- if self.forces_status[i] == 1:
- self.forces_order.append(i)
- elif self.forces_status[i] == -1:
- self.forces_order.remove(i)
-
- print(self.forces_status)
- deb = 1 """
-
- # Contact forces desired by MPC (transformed into world frame)
- for f in range(4):
- self.forces_mpc[3*f:(3*(f+1)), k:(k+1)] = np.reshape((interface.oMl.rotation @
- tsid_controller.f_applied[3*f:3*(f+1)]).T, (3, 1))
-
- # Contact forces desired by TSID (world frame)
- for i, j in enumerate(tsid_controller.contacts_order):
- self.forces_tsid[(3*j):(3*(j+1)), k:(k+1)] = np.reshape(tsid_controller.fc[(3*i):(3*(i+1))], (3, 1))
-
- # Contact forces applied in PyBullet
- contactPoints_FL = pyb.getContactPoints(robotId, planeId, linkIndexA=3) # Front left foot
- contactPoints_FR = pyb.getContactPoints(robotId, planeId, linkIndexA=7) # Front right foot
- contactPoints_HL = pyb.getContactPoints(robotId, planeId, linkIndexA=11) # Hind left foot
- contactPoints_HR = pyb.getContactPoints(robotId, planeId, linkIndexA=15) # Hind right foot
-
- # Sort contacts points to get only one contact per foot
- contactPoints = []
- contactPoints += self.getContactPoint(contactPoints_FL)
- contactPoints += self.getContactPoint(contactPoints_FR)
- contactPoints += self.getContactPoint(contactPoints_HL)
- contactPoints += self.getContactPoint(contactPoints_HR)
-
- # Display debug lines for contact forces visualization
- f_tmps = np.zeros((3, 4))
- f_tmp = [0.0] * 3
- for contact in contactPoints:
- if not isinstance(contact, int): # type(contact) != type(0):
- start = [contact[6][0], contact[6][1], contact[6][2]+0.04]
- end = [contact[6][0], contact[6][1], contact[6][2]+0.04]
- K = 0.02
- for i_direction in range(0, 3):
- f_tmp[i_direction] = (contact[9] * contact[7][i_direction] + contact[10] *
- contact[11][i_direction] + contact[12] * contact[13][i_direction])
- end[i_direction] += K * f_tmp[i_direction]
-
- """if contact[3] < 10:
- print("Link ", contact[3], "| Contact force: (", f_tmp[0], ", ", f_tmp[1], ", ", f_tmp[2], ")")
- else:
- print("Link ", contact[3], "| Contact force: (", f_tmp[0], ", ", f_tmp[1], ", ", f_tmp[2], ")")"""
-
- f_tmps[:, int(contact[3]/4)] += np.array(f_tmp)
-
- for i in range(4):
- self.forces_pyb[(3*i):(3*(i+1)), k] = - f_tmps[:, i]
-
- return 0
-
- def plot_forces(self):
- """ Plot information about contact forces
- """
-
- index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- lgd1 = ["Ctct force X", "Ctct force Y", "Ctct force Z"]
- lgd2 = ["FL", "FR", "HL", "HR"]
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
-
- h1, = plt.plot(self.t_range, self.forces_mpc[i, :], "r", linewidth=5)
- h2, = plt.plot(self.t_range, self.forces_tsid[i, :], "b", linewidth=3)
- h3, = plt.plot(self.t_range, self.forces_pyb[i, :], "g", linewidth=3, linestyle="--")
-
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)])
-
- plt.legend([h1, h2, h3], [lgd1[i % 3]+" "+lgd2[int(i/3)], lgd1[i % 3]+" "+lgd2[int(i/3)],
- lgd1[i % 3]+" "+lgd2[int(i/3)]])
-
- if (i % 3) == 2:
- plt.ylim([-1.0, 20.0])
- else:
- plt.ylim([-8.0, 8.0])
-
- plt.suptitle("MPC, TSID and PyBullet contact forces (world frame)")
-
- plt.figure()
- plt.plot(self.t_range, np.sum(self.forces_mpc[2::3, :], axis=0), "r", linewidth=5)
- plt.plot(self.t_range, np.sum(self.forces_tsid[2::3, :], axis=0), "b", linewidth=3)
- plt.plot(self.t_range, np.sum(self.forces_pyb[2::3, :], axis=0), "g", linewidth=3, linestyle="--")
- plt.plot(self.t_range, 2.5*9.81*np.ones((len(self.t_range))), "k", linewidth=5)
- plt.suptitle("Total vertical contact force considering all contacts")
-
- index = [1, 2, 3, 4]
- lgd = ["FL", "FR", "HL", "HR"]
- plt.figure()
- for i in range(4):
- plt.subplot(2, 2, index[i])
-
- ctct_status = np.sum(np.abs(self.forces_mpc[(3*i):(3*(i+1)), :]), axis=0) > 0.05
- ctct_mismatch = np.logical_xor((np.sum(np.abs(self.forces_tsid[(3*i):(3*(i+1)), :]), axis=0) != 0),
- (np.sum(np.abs(self.forces_pyb[(3*i):(3*(i+1)), :]), axis=0) != 0))
-
- plt.plot(self.t_range, ctct_status, "r", linewidth=3)
- plt.plot(self.t_range, ctct_mismatch, "k", linewidth=3)
-
- plt.xlabel("Time [s]")
- plt.ylabel("Contact status mismatch (True/False)")
- plt.legend(["MPC Contact Status " + lgd[i], "Mismatch " + lgd[i]])
- plt.suptitle("Contact status mismatch between MPC/TSID and PyBullet")
-
- return 0
-
- def log_torques(self, k, tsid_controller):
- """ Store information about torques
- """
-
- self.torques_ff[:, k:(k+1)] = np.reshape(tsid_controller.tau_ff, (12, 1))
- self.torques_pd[:, k:(k+1)] = np.reshape(tsid_controller.tau_pd, (12, 1))
- self.torques_sent[:, k:(k+1)] = np.reshape(tsid_controller.torques12, (12, 1))
-
- return 0
-
- def plot_torques(self):
- """ Plot information about torques
- """
-
- index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
-
- lgd = ["DoF 1 FL", "DoF 2 FL", "DoF 3 FL", "DoF 1 FR", "DoF 2 FR", "DoF 3 FR",
- "DoF 1 HL", "DoF 2 HL", "DoF 3 HL", "DoF 1 HR", "DoF 2 HR", "DoF 3 HR"]
- plt.figure()
- for i in range(12):
- plt.subplot(3, 4, index[i])
- h1, = plt.plot(self.t_range, self.torques_ff[i, :], color="b", linewidth=3)
- h2, = plt.plot(self.t_range, self.torques_pd[i, :], color="r", linewidth=3)
- h3, = plt.plot(self.t_range, self.torques_sent[i, :], color="g", linewidth=3)
- plt.legend([h1, h2, h3], [lgd[i] + " FF", lgd[i] + " PD", lgd[i]+" Sent"])
- plt.xlabel("Time [s]")
- plt.ylabel("Torque [Nm]")
- plt.suptitle("Feedforward, PD and sent torques (output of PD+ with saturation)")
-
- return 0
-
- def log_cost_function(self, k, mpc_wrapper):
- """ Store information about the cost function of the mpc
- """
-
- # Cost of each component of the cost function over the prediction horizon (state vector and contact forces)
- cost = (np.diag(mpc_wrapper.mpc.x) @ np.diag(mpc_wrapper.mpc.P.data)
- ) @ np.array([mpc_wrapper.mpc.x]).transpose()
-
- # Sum components of the state vector
- for i in range(12):
- self.cost_components[i, k:(k+1)] = np.sum(cost[i:(12*mpc_wrapper.mpc.n_steps):12])
-
- # Sum components of the contact forces
- self.cost_components[12, k:(k+1)] = np.sum(cost[(12*mpc_wrapper.mpc.n_steps):])
-
- """if k % 50 == 0:
- print(np.sum(np.power(mpc_wrapper.solver.mpc.x[6:(12*mpc_wrapper.solver.mpc.n_steps):12], 2)))
-
- absc = np.array([i for i in range(16)])
- if k == 0:
- plt.figure()
- if k % 100 == 0:
- plt.plot(absc+k, np.power(mpc_wrapper.solver.mpc.x[6:(12*mpc_wrapper.solver.mpc.n_steps):12], 2))
- if k == 5999 == 0:
- plt.show(block=True)"""
- return 0
-
- def plot_cost_function(self):
- """ Plot information about the cost function of the mpc
- """
-
- lgd = ["$X$", "$Y$", "$Z$", "$\phi$", "$\\theta$", "$\psi$", "$\dot X$", "$\dot Y$", "$\dot Z$",
- "$\dot \phi$", "$\dot \\theta$", "$\dot \psi$", "$f$", "Total"]
- plt.figure()
- hs = []
- for i in range(14):
- if i < 10:
- h, = plt.plot(self.t_range, self.cost_components[i, :], linewidth=2)
- elif i <= 12:
- h, = plt.plot(self.t_range, self.cost_components[i, :], linewidth=2, linestyle="--")
- else:
- h, = plt.plot(self.t_range, np.sum(self.cost_components, axis=0), linewidth=2, linestyle="--")
- hs.append(h)
- plt.xlabel("Time [s]")
- plt.ylabel("Cost")
- plt.legend(hs, lgd)
- plt.title("Contribution of each component in the cost function")
-
- return 0
-
- def log_predicted_trajectories(self, k, mpc_wrapper):
- """ Store information about the predicted evolution of the optimization vector components
- """
- if self.type_MPC == 0:
- self.pred_trajectories[:, :, int(k/self.k_mpc)] = mpc_wrapper.mpc.x_robot
- self.pred_forces[:, :, int(k/self.k_mpc)] = mpc_wrapper.mpc.x[mpc_wrapper.mpc.xref.shape[0]*(mpc_wrapper.mpc.xref.shape[1]-1):].reshape((mpc_wrapper.mpc.xref.shape[0],
- mpc_wrapper.mpc.xref.shape[1]-1),
- order='F')
- else:
-
- self.pred_trajectories[:, :, int(k/self.k_mpc)] = mpc_wrapper.mpc.get_xrobot()
- self.pred_forces[:, :, int(k/self.k_mpc)] = mpc_wrapper.mpc.get_fpredicted()
-
- return 0
-
- def log_fstep_planner(self, k, fstep_planner):
-
- self.fsteps[:, :, int(k/self.k_mpc)] = fstep_planner.fsteps
- self.xref[:, :, int(k/self.k_mpc)] = fstep_planner.xref
-
- return 0
-
- def plot_fstep_planner(self):
- """ Plot information about the footstep planner
- """
-
- self.fsteps[np.isnan(self.fsteps)] = 0.0
- index = [1, 3, 2, 4]
- lgd = ["X FL", "Y FR", "X FR", "Y FR"]
- plt.figure()
- for i in range(4):
- plt.subplot(2, 2, index[i])
- if i < 2:
- plt.plot(self.t_range[::int(self.dt_mpc/self.dt)],
- self.fsteps[0, i+1, :], color='k', linewidth=2, marker='x')
- else:
- plt.plot(self.t_range[::int(self.dt_mpc/self.dt)],
- self.fsteps[0, i-2+3+1, :], color='k', linewidth=2, marker='x')
-
- plt.xlabel("Time [s]")
- plt.ylabel(lgd[i])
- plt.suptitle("Foostep planner output for FL and FR feet")
-
- def plot_predicted_trajectories(self):
- """ Plot information about the predicted evolution of the optimization vector components
- """
-
- t_pred = np.array([(k)*self.dt_mpc for k in range(np.int(self.T/self.dt_mpc))])
-
- #index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- index = [1, 4, 7, 2, 5, 8, 3, 6, 9]
-
- lgd = ["Position Z", "Position Roll", "Position Pitch", "Linear vel X", "Linear vel Y", "Linear vel Z",
- "Angular vel Roll", "Angular vel Pitch", "Angular vel Yaw"]
- plt.figure()
- for i, o in enumerate([2, 3, 4, 6, 7, 8, 9, 10, 11]):
- plt.subplot(3, 3, index[i])
- for j in range(self.pred_trajectories.shape[2]):
- if (j*self.k_mpc > self.k_max_loop):
- break
- # if (j % 1) == 0:
- #h, = plt.plot(t_pred[0:15] + j*self.dt_mpc, self.pred_trajectories[o, 0:15, j], linewidth=2, marker='x')
- h, = plt.plot(t_pred[0:16] + j*self.dt_mpc, np.hstack(([self.lW[1, j*self.k_mpc]],
- self.pred_trajectories[o, 0:15, j])), linewidth=2, marker='x')
- #h, = plt.plot(self.t_range[::20], self.state_ref[i, ::20], "r", linewidth=3, marker='*')
- if i == 0:
- plt.plot(self.t_range[::20], self.lC[2, ::20], "r", linewidth=2) # , marker='o', linestyle="--")
- elif i <= 2:
- plt.plot(self.t_range[::20], self.RPY[i-1, ::20], "r", linewidth=2) # , marker='o', linestyle="--")
- elif i <= 5:
- plt.plot(self.t_range[::20], self.lV[i-3, ::20], "r", linewidth=2) # , marker='o', linestyle="--")
- else:
- plt.plot(self.t_range[::20], self.lW[i-6, ::20], "r", linewidth=2) # , marker='o', linestyle="--")
- plt.ylabel(lgd[i])
- plt.suptitle("Predicted trajectories (local frame)")
-
- return 0
-
- def log_tracking_foot(self, k, tsid_controller, solo):
- """ Store information about one of the foot tracking task
- """
-
- self.pos[:, k:(k+1)] = np.reshape(tsid_controller.feetTask[1].position, (12, 1))
- self.pos_ref[:, k:(k+1)] = np.reshape(tsid_controller.feetTask[1].position_ref, (12, 1))
- self.pos_err[:, k:(k+1)] = np.reshape(tsid_controller.feetTask[1].position_error, (3, 1))
- self.vel[0:3, k:(k+1)] = np.reshape((solo.data.oMi[solo.model.frames[18].parent].act(solo.model.frames[18].placement)
- ).rotation @ tsid_controller.feetTask[1].velocity[0:3], (3, 1))
- self.vel[3:6, k:(k+1)] = np.reshape((solo.data.oMi[solo.model.frames[18].parent].act(solo.model.frames[18].placement)
- ).rotation @ tsid_controller.feetTask[1].velocity[3:6], (3, 1))
- self.vel_ref[:, k:(k+1)] = np.reshape(tsid_controller.feetTask[1].velocity_ref, (6, 1))
- self.vel_err[:, k:(k+1)] = np.reshape(tsid_controller.feetTask[1].velocity_error, (3, 1))
-
- return 0
-
- def plot_tracking_foot(self):
- """ Plot information about one of the foot tracking task
- """
-
- index = [1, 3, 5, 2, 4, 6]
- lgd = ["X", "Y", "Z", "$\dot X$", "$\dot Y$", "$\dot Z$"]
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index[i])
- if i < 3:
- plt.plot(self.pos[i, :], color='r', linewidth=2, marker='x')
- plt.plot(self.pos_ref[i, :], color='g', linewidth=2, marker='x')
- plt.plot(self.feet_pos_target[i, 1, :], color='b', linewidth=2, marker='x')
- #plt.plot(self.pos_err[i, :], color='b', linewidth=2, marker='x')
- plt.legend(["Pos", "Pos ref", "Pos target"])
- else:
- plt.plot(self.vel[i-3, :], color='r', linewidth=2, marker='x')
- plt.plot(self.vel_ref[i-3, :], color='g', linewidth=2, marker='x')
- #plt.plot(self.vel_err[i-3, :], color='b', linewidth=2, marker='x')
- plt.plot(self.feet_vel_target[i-3, 1, :], color='b', linewidth=2, marker='x')
- plt.legend(["Vel", "Vel ref", "Vel target"])
-
- plt.ylabel(lgd[i])
- plt.suptitle("Tracking FR foot")
-
- return 0
-
- def call_log_functions(self, k, pyb_sim, joystick, fstep_planner, interface, mpc_wrapper, tsid_controller, enable_multiprocessing, robotId, planeId, solo):
- """ Call logging functions of the Logger class
- """
-
- # Store current and desired position, velocity and acceleration of feet over time
- self.log_footsteps(k, interface, tsid_controller)
-
- # Store information about the state of the robot
- if not enable_multiprocessing:
- self.log_state(k, pyb_sim, joystick, interface, mpc_wrapper, solo)
-
- # Store information about contact forces
- self.log_forces(k, interface, tsid_controller, robotId, planeId)
-
- # Store information about torques
- self.log_torques(k, tsid_controller)
-
- # Store information about the cost function
- """if (self.type_MPC == 0) and not enable_multiprocessing:
- self.log_cost_function(k, mpc_wrapper)"""
-
- # Store information about the predicted evolution of the optimization vector components
- if not enable_multiprocessing and ((k % self.k_mpc) == 0):
- # self.log_predicted_trajectories(k, mpc_wrapper)
- self.log_fstep_planner(k, fstep_planner)
-
- # Store information about one of the foot tracking task
- self.log_tracking_foot(k, tsid_controller, solo)
-
- return 0
-
- def plot_graphs(self, enable_multiprocessing, show_block=True):
-
- # Plot current and desired position, velocity and acceleration of feet over time
- self.plot_footsteps()
-
- # Plot information about the state of the robot
- if not enable_multiprocessing:
- self.plot_state()
-
- # Plot information about the contact forces
- self.plot_forces()
-
- # Plot information about the torques
- self.plot_torques()
-
- # Plot information about the state of the robot
- # Cost not comparable between the two solvers
- if (self.type_MPC == 0) and not enable_multiprocessing:
- self.plot_cost_function()
-
- # Plot information about the predicted evolution of the optimization vector components
- # if not enable_multiprocessing:
- # self.plot_predicted_trajectories()
-
- # Plot information about one of the foot tracking task
- # self.plot_tracking_foot()
-
- # Display graphs
- plt.show(block=show_block)
-
- """# Evolution of the position and orientation of the robot over time
- plt.figure()
- ylabels = ["Position X", "Position Y", "Position Z",
- "Orientation Roll", "Orientation Pitch", "Orientation Yaw"]
- for i, j in enumerate([1, 3, 5, 2, 4, 6]):
- plt.subplot(3, 2, j)
- plt.plot(log_t, self.log_state[i, :], "b", linewidth=2)
- if i not in [0, 1, 6]:
- plt.plot(log_t, self.log_state_ref[i, :], "r", linewidth=2)
- plt.legend(["Robot", "Reference"])
- plt.xlabel("Time [s]")
- plt.ylabel(ylabels[i])
-
- # Evolution of the linear and angular velocities of the robot over time
- plt.figure()
- ylabels = ["Linear vel X", "Linear vel Y", "Linear vel Z",
- "Angular vel Roll", "Angular vel Pitch", "Angular vel Yaw"]
- for i, j in enumerate([1, 3, 5, 2, 4, 6]):
- plt.subplot(3, 2, j)
- plt.plot(log_t, self.log_state[i+6, :], "b", linewidth=2)
- plt.plot(log_t, self.log_state_ref[i+6, :], "r", linewidth=2)
- plt.legend(["Robot", "Reference"])
- plt.xlabel("Time [s]")
- plt.ylabel(ylabels[i])
-
- # Evolution of the position of the robot in world frame (X, Y) graph
- plt.figure()
- plt.plot(self.log_state[0, :], self.log_state[1, :], "b", linewidth=2)
- plt.plot(self.log_state_ref[0, :], self.log_state_ref[1, :], "r", linewidth=2)
- for i_foot in range(4):
- plt.plot(self.log_footholds_w[i_foot, :, 0],
- self.log_footholds_w[i_foot, :, 1], linestyle=None, marker="*")
- plt.legend(["Robot", "Reference"])
- plt.xlabel("Position X [m]")
- plt.ylabel("Position Y [m]")
-
- # Evolution of the position of footholds over time
- plt.figure()
- for i in range(4):
- plt.subplot(4, 2, 2*i+1)
- plt.plot(log_t, self.log_footholds[i, :, 0], linewidth=2)
- plt.xlabel("Time [s]")
- plt.ylabel("Position X [m]")
- plt.subplot(4, 2, 2*i+2)
- plt.plot(log_t, self.log_footholds[i, :, 1], linewidth=2)
- plt.xlabel("Time [s]")
- plt.ylabel("Position Y [m]")
-
- # Evolution of predicted trajectory over time
- log_t_pred = np.array([k*dt for k in range(self.log_predicted_traj.shape[2])])
-
- plt.figure()
- plt.subplot(3, 2, 1)
- c = [[i/(self.log_predicted_traj.shape[1]+5), 0.0, i/(self.log_predicted_traj.shape[1]+5)]
- for i in range(self.log_predicted_traj.shape[1])]
- for i in range(self.log_predicted_traj.shape[1]):
- plt.plot(log_t_pred+i*dt, self.log_predicted_traj[0, i, :], "b", linewidth=2, color=c[i])
- plt.plot(np.array([k*dt for k in range(self.log_predicted_traj.shape[1])]),
- self.log_predicted_traj[0, :, 0], linestyle=None, marker='x', color="r", linewidth=1)
- plt.xlabel("Time [s]")
- plt.title("Predicted trajectory along X [m]")
- plt.legend(["Robot"])
- plt.subplot(3, 2, 3)
- for i in range(self.log_predicted_traj.shape[1]):
- plt.plot(log_t_pred+i*dt, self.log_predicted_traj[1, i, :], "b", linewidth=2, color=c[i])
- plt.plot(np.array([k*dt for k in range(self.log_predicted_traj.shape[1])]),
- self.log_predicted_traj[1, :, 0], linestyle=None, marker='x', color="r", linewidth=1)
- plt.xlabel("Time [s]")
- plt.title("Predicted trajectory along Y [m]")
- plt.legend(["Robot"])
- plt.subplot(3, 2, 5)
- for i in range(self.log_predicted_traj.shape[1]):
- plt.plot(log_t_pred+i*dt, self.log_predicted_traj[2, i, :], "b", linewidth=2, color=c[i])
- plt.plot(np.array([k*dt for k in range(self.log_predicted_traj.shape[1])]),
- self.log_predicted_traj[2, :, 0], linestyle=None, marker='x', color="r", linewidth=1)
- plt.xlabel("Time [s]")
- plt.title("Predicted trajectory along Z [m]")
- plt.legend(["Robot"])
- plt.subplot(3, 2, 2)
- for i in range(self.log_predicted_traj.shape[1]):
- plt.plot(log_t_pred+i*dt, self.log_predicted_traj[3, i, :], "b", linewidth=2, color=c[i])
- plt.plot(np.array([k*dt for k in range(self.log_predicted_traj.shape[1])]),
- self.log_predicted_traj[3, :, 0], linestyle=None, marker='x', color="r", linewidth=1)
- plt.xlabel("Time [s]")
- plt.title("Predicted trajectory in Roll [rad/s]")
- plt.legend(["Robot"])
- plt.subplot(3, 2, 4)
- for i in range(self.log_predicted_traj.shape[1]):
- plt.plot(log_t_pred+i*dt, self.log_predicted_traj[4, i, :], "b", linewidth=2, color=c[i])
- plt.plot(np.array([k*dt for k in range(self.log_predicted_traj.shape[1])]),
- self.log_predicted_traj[4, :, 0], linestyle=None, marker='x', color="r", linewidth=1)
- plt.xlabel("Time [s]")
- plt.title("Predicted trajectory in Pitch [rad/s]")
- plt.legend(["Robot"])
- plt.subplot(3, 2, 6)
- for i in range(self.log_predicted_traj.shape[1]):
- plt.plot(log_t_pred+i*dt, self.log_predicted_traj[5, i, :], "b", linewidth=2, color=c[i])
- plt.plot(np.array([k*dt for k in range(self.log_predicted_traj.shape[1])]),
- self.log_predicted_traj[5, :, 0], linestyle=None, marker='x', color="r", linewidth=1)
- plt.xlabel("Time [s]")
- plt.title("Predicted trajectory in Yaw [rad/s]")
- plt.legend(["Robot"])
-
- # Plot desired contact forces
- plt.figure()
- c = ["r", "g", "b", "rebeccapurple"]
- legends = ["FL", "FR", "HL", "HR"]
- for i in range(4):
- plt.subplot(3, 4, i+1)
- plt.plot(log_t, self.log_contact_forces[i, :, 0], color="r", linewidth=3)
- if mycontroller is not None:
- plt.plot(log_t, mycontroller.c_forces[i, :, 0], color="b", linewidth=2)
- tmp = np.zeros((k_max_loop,))
- for k in range(np.floor(k_max_loop/(0.3/0.005)+1).astype(int)):
- plt.plot([k*0.3, k*0.3], [np.min(self.log_contact_forces[i, :, 0]),
- np.max(self.log_contact_forces[i, :, 0])], "grey", linewidth=1, linestyle="--")
- plt.xlabel("Time [s]")
- plt.ylabel("Contact force along X [N]")
- plt.legend([legends[i] + "_MPC", legends[i] + "_TSID"])
-
- for i in range(4):
- plt.subplot(3, 4, i+5)
- plt.plot(log_t, self.log_contact_forces[i, :, 1], color="r", linewidth=3)
- if mycontroller is not None:
- plt.plot(log_t, mycontroller.c_forces[i, :, 1], color="b", linewidth=2)
- tmp = np.zeros((k_max_loop,))
- for k in range(np.floor(k_max_loop/(0.3/0.005)+1).astype(int)):
- plt.plot([k*0.3, k*0.3], [np.min(self.log_contact_forces[i, :, 1]),
- np.max(self.log_contact_forces[i, :, 1])], "grey", linewidth=1, linestyle="--")
- plt.xlabel("Time [s]")
- plt.ylabel("Contact force along Y [N]")
- plt.legend([legends[i] + "_MPC", legends[i] + "_TSID"])
-
- for i in range(4):
- plt.subplot(3, 4, i+9)
- plt.plot(log_t, self.log_contact_forces[i, :, 2], color="r", linewidth=3)
- if mycontroller is not None:
- plt.plot(log_t, mycontroller.c_forces[i, :, 2], color="b", linewidth=2)
- tmp = np.zeros((k_max_loop,))
- for k in range(np.floor(k_max_loop/(0.3/0.005)+1).astype(int)):
- plt.plot([k*0.3, k*0.3], [np.min(self.log_contact_forces[i, :, 2]),
- np.max(self.log_contact_forces[i, :, 2])], "grey", linewidth=1, linestyle="--")
- plt.xlabel("Time [s]")
- plt.ylabel("Contact force along Z [N]")
- plt.legend([legends[i] + "_MPC", legends[i] + "_TSID"])
-
- plt.show(block=False)"""
-
- return 0
diff --git a/scripts/MPC_Wrapper.py b/scripts/MPC_Wrapper.py
index 32a93c83..320b27c8 100644
--- a/scripts/MPC_Wrapper.py
+++ b/scripts/MPC_Wrapper.py
@@ -1,17 +1,13 @@
-# coding: utf8
-
-from operator import pos
-from typing import FrozenSet
import numpy as np
from multiprocessing import Process, Value, Array
-import crocoddyl_class.MPC_crocoddyl as MPC_crocoddyl
-import crocoddyl_class.MPC_crocoddyl_planner as MPC_crocoddyl_planner
-import pinocchio as pin
from time import time
from enum import Enum
-import quadruped_reactive_walking as qrw
import ctypes
-from ctypes import Structure, c_double
+from ctypes import Structure
+
+import Crocoddyl.MPC_crocoddyl as MPC_crocoddyl
+import Crocoddyl.MPC_crocoddyl_planner as MPC_crocoddyl_planner
+import quadruped_reactive_walking as qrw
class MPC_type(Enum):
@@ -221,7 +217,7 @@ class MPC_Wrapper:
self.f_applied = self.mpc.get_latest_result(oRh, oTh)
else:
self.f_applied = self.mpc.get_latest_result()
-
+
if self.mpc_type == MPC_type.OSQP:
self.last_cost = self.mpc.retrieve_cost()
diff --git a/scripts/__init__.py b/scripts/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/scripts/bauzil_stairs.stl b/scripts/bauzil_stairs.stl
deleted file mode 100644
index c75c11b9c75eccda0502c24f645bec841a864c69..0000000000000000000000000000000000000000
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diff --git a/scripts/config_solo12_bis.yaml b/scripts/config_solo12_bis.yaml
deleted file mode 100644
index 11144139..00000000
--- a/scripts/config_solo12_bis.yaml
+++ /dev/null
@@ -1,58 +0,0 @@
-robot:
- interface: enp2s0
- joint_modules:
- motor_numbers: [0, 3, 2, 1, 5, 4, 6, 9, 8, 7, 11, 10]
- motor_constants: 0.025
- gear_ratios: 9.
- max_currents: 12.
- reverse_polarities: [
- false, true, true, true, false, false,
- false, true, true, true, false, false
- ]
- lower_joint_limits: [
- -1.2, -1.7, -3.4, -1.2, -1.7, -3.4,
- -1.2, -1.7, -3.4, -1.2, -1.7, -3.4
- ]
- upper_joint_limits: [
- 1.2, 1.7, +3.4, +1.2, +1.7, +3.4,
- 1.2, 1.7, +3.4, +1.2, +1.7, +3.4
- ]
- max_joint_velocities: 80.
- safety_damping: 0.5
- imu:
- rotate_vector: [1, 2, 3]
- orientation_vector: [-4, 3, -2, 1]
-joint_calibrator:
- # Can be either POS, NEG, ALT or AUTO
- search_methods: [
- AUTO, AUTO, AUTO, AUTO, AUTO, AUTO,
- AUTO, AUTO, AUTO, AUTO, AUTO, AUTO
- ]
- position_offsets: [
- 0.17135490311516655,
- -0.2380975882212321,
- -0.27943841616312665,
- 0.04930730660756429,
- 0.6213272942437066,
- -0.42891038788689506,
- 0.44484631220499676,
- 0.1533108287387424,
- 0.02831502093209161,
- -0.32956356472439235,
- -0.15770453876919216,
- -0.25019770198398167
- ]
- calib_order: [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]
- calib_pos: [
- 0.0, 1.2, -2.4,
- 0.0, 1.2, -2.4,
- 0.0, -1.2, 2.4,
- 0.0, -1.2, 2.4
- ]
- Kp: 1.
- Kd: 0.05
- T: 1.
- dt: 0.001
-
-# - np.array([-1.542194128036499, 0.4437657594680786, -2.5149457454681396, -2.142878293991089, 3.8601934909820557, -5.591945648193359,
-# -4.003616809844971, -2.9660720825195312, 0.25483518838882446, 1.3797974586486816, 2.251779317855835, 1.4193408489227295])
diff --git a/scripts/main_mcapi_solo8.py b/scripts/main_mcapi_solo8.py
deleted file mode 100644
index e96fa489..00000000
--- a/scripts/main_mcapi_solo8.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# coding: utf8
-import numpy as np
-import argparse
-import math
-from time import perf_counter as clock
-from time import sleep
-from solo12 import Solo12
-from pynput import keyboard
-
-from utils.logger import Logger
-from utils.qualisysClient import QualisysClient
-import matplotlib.pyplot as plt
-from math import ceil
-import curves
-from multicontact_api import ContactSequence
-curves.switchToNumpyArray()
-DT = 0.001
-KP = 4.
-KD = 0.05
-KT = 1.
-tau_max = 3. * np.ones(8)
-
-key_pressed = False
-
-# Variables for motion range test of Solo12
-t_switch = np.array([0.0, 3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0, 27.0, 33.0, 36.0, 39.0, 42.0])
-q1 = 0.66 * np.pi
-q_switch = np.array([[0.0, 0.66 * np.pi, 0.0, 0.66 * np.pi, 0.0, 0.5 * np.pi, 0.5 * np.pi, 0.5 * np.pi, 0.0, -0.1 * np.pi, 0.0, 0.22 * np.pi, 0.0, 0.0],
- [0.8, 0.8, -0.8, -0.8, 0.8, 0.8, 0.0, -0.8, -0.8, -1.4, np.pi*0.5, np.pi*0.5, np.pi*0.5, 0.0],
- [-1.6, -1.6, 1.6, 1.6, -1.6, -1.6, +1.0, -2.0, -2.0, 2.0, -np.pi, -np.pi*1.4, -np.pi, 0.0]])
-
-def handle_q_v_switch(t):
-
- i = 1
- while (i < t_switch.shape[0]) and (t_switch[i] <= t):
- i += 1
-
- if (i != t_switch.shape[0]):
- return apply_q_v_change(t, i)
- else:
- N = q_switch.shape[1]
- return np.tile(q_switch[:, (N-1):N], (4, 1)) * np.array([[1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1]]).transpose(), np.zeros((12,))
-
-
-def apply_q_v_change(t, i):
-
- # Position
- ev = t - t_switch[i-1]
- t1 = t_switch[i] - t_switch[i-1]
- A3 = 2 * (q_switch[:, (i-1):i] - q_switch[:, i:(i+1)]) / t1**3
- A2 = (-3/2) * t1 * A3
- q = q_switch[:, (i-1):i] + A2*ev**2 + A3*ev**3
- v = 2 * A2 * ev + 3 * A3 * ev**2
-
- q = np.tile(q, (4, 1)) * np.array([[1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1]]).transpose()
- v = np.tile(v, (4, 1)) * np.array([[1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1]]).transpose()
-
- return q, v
-
-def test_solo12(t):
-
- q = np.array([0, 0.8, -1.6, 0, 0.8, -1.6, 0, -0.8, 1.6, 0, -0.8, 1.6])
- v = np.zeros((12,))
-
- q, v = handle_q_v_switch(t)
-
- return q, v
-
-def config_12_to_8(q12):
- """
- Remove the shoulder roll joint from the vector
- """
- assert q12.shape[0] == 12
- return np.hstack((q12[1:3], q12[4:6], q12[7:9], q12[10:12]))
-
-
-def compute_pd(q_desired, v_desired, tau_desired, device):
- pos_error = q_desired - device.q_mes
- vel_error = v_desired - device.v_mes
- tau = KP * pos_error + KD * vel_error + KT * tau_desired
- tau = np.maximum(np.minimum(tau, tau_max), -tau_max)
- return tau
-
-def on_press(key):
- global key_pressed
- try:
- if key == keyboard.Key.enter:
- key_pressed = True
- # Stop listener
- return False
- except AttributeError:
- print('Unknown key {0} pressed'.format(key))
-
-def put_on_the_floor(device, q_init):
- global key_pressed
- key_pressed = False
- Kp_pos = 3.
- Kd_pos = 0.01
- imax = 3.0
- pos = np.zeros(device.nb_motors)
- for motor in range(device.nb_motors):
- pos[motor] = q_init[device.motorToUrdf[motor]] * device.gearRatioSigned[motor]
- listener = keyboard.Listener(on_press=on_press)
- listener.start()
- print("Put the robot on the floor and press Enter")
- while not key_pressed:
- device.UpdateMeasurment()
- for motor in range(device.nb_motors):
- ref = Kp_pos*(pos[motor] - device.hardware.GetMotor(motor).GetPosition() - Kd_pos*device.hardware.GetMotor(motor).GetVelocity())
- ref = min(imax, max(-imax, ref))
- device.hardware.GetMotor(motor).SetCurrentReference(ref)
- device.SendCommand(WaitEndOfCycle=True)
-
- print("Start the motion.")
-
-def plot_mes_des_curve(times, mes_t, des_t = None, title = None, y_label = None):
- colors = ['r', 'b']
- labels = ["Shoulder" , "Knee"]
- legs_order = ["front left", "front right", "hind left", "hind right"]
- # joint positions des/mes
- fig, ax = plt.subplots(2, 2)
- fig.canvas.set_window_title(title)
- fig.suptitle(title, fontsize=20)
- for i in range(2): # line (front/back)
- for j in range(2): # column (left/right)
- ax_sub = ax[i, j]
- for k in range(2): # joint
- ax_sub.plot(times, mes_t[i*4 + j*2 + k, :].T, color=colors[k], label=labels[k])
- if des_t is not None:
- ax_sub.plot(times, des_t[i*4 + j*2 + k, :].T, color=colors[k], label=labels[k] + "(ref)", linestyle="dashed")
- ax_sub.set_xlabel('time (s)')
- ax_sub.set_ylabel(y_label)
- ax_sub.set_title(legs_order[i*2 + j])
- ax_sub.legend()
-
-
-
-def mcapi_playback(name_interface, filename):
- device = Solo12(name_interface,dt=DT)
- qc = QualisysClient(ip="140.93.16.160", body_id=0)
- logger = Logger(device, qualisys=qc)
- nb_motors = device.nb_motors
-
- q_viewer = np.array((7 + nb_motors) * [0.,])
-
- # Load contactsequence from file:
- cs = ContactSequence(0)
- cs.loadFromBinary(filename)
- # extract (q, dq, tau) trajectories:
- q_t = cs.concatenateQtrajectories() # with the freeflyer configuration
- dq_t = cs.concatenateDQtrajectories() # with the freeflyer configuration
- ddq_t = cs.concatenateDDQtrajectories() # with the freeflyer configuration
- tau_t = cs.concatenateTauTrajectories() # joints torques
- # Get time interval from planning:
- t_min = q_t.min()
- t_max = q_t.max()
- print("## Complete duration of the motion loaded: ", t_max - t_min)
- # Sanity checks:
- assert t_min < t_max
- assert dq_t.min() == t_min
- assert ddq_t.min() == t_min
- assert tau_t.min() == t_min
- assert dq_t.max() == t_max
- assert ddq_t.max() == t_max
- assert tau_t.max() == t_max
- assert q_t.dim() == 19
- assert dq_t.dim() == 18
- assert ddq_t.dim() == 18
- assert tau_t.dim() == 12
-
- num_steps = ceil((t_max - t_min) / DT) + 1
- q_mes_t = np.zeros([8, num_steps])
- v_mes_t = np.zeros([8, num_steps])
- q_des_t = np.zeros([8, num_steps])
- v_des_t = np.zeros([8, num_steps])
- tau_des_t = np.zeros([8, num_steps])
- tau_send_t = np.zeros([8, num_steps])
- tau_mesured_t = np.zeros([8, num_steps])
- q_init = config_12_to_8(q_t(t_min)[7:])
- device.Init(calibrateEncoders=True, q_init=q_init)
- t = t_min
- put_on_the_floor(device, q_init)
- #CONTROL LOOP ***************************************************
- t_id = 0
- while ((not device.hardware.IsTimeout()) and (t < t_max)):
- # q_desired = config_12_to_8(q_t(t)[7:]) # remove freeflyer
- # dq_desired = config_12_to_8(dq_t(t)[6:]) # remove freeflyer
- # tau_desired = config_12_to_8(tau_t(t))
- device.UpdateMeasurment()
- # tau = compute_pd(q_desired, dq_desired, tau_desired, device)
-
- #Â Parameters of the PD controller
- KP = 4.
- KD = 0.05
- KT = 1.
- tau_max = 3. * np.ones(12)
-
- # Desired position and velocity for this loop and resulting torques
- q_desired, v_desired = test_solo12(t)
- pos_error = q_desired.ravel() - actuators_pos[:]
- vel_error = v_desired.ravel() - actuators_vel[:]
- tau = KP * pos_error + KD * vel_error
- tau = 0.0 * np.maximum(np.minimum(tau, tau_max), -tau_max)
-
- #Â Send desired torques to the robot
- device.SetDesiredJointTorque(tau)
-
- # store desired and mesured data for plotting
- q_des_t[:, t_id] = q_desired
- v_des_t[:, t_id] = dq_desired
- q_mes_t[:, t_id] = device.q_mes
- v_mes_t[:, t_id] = device.v_mes
- tau_des_t[:, t_id] = tau_desired
- tau_send_t[:, t_id] = tau
- tau_mesured_t[: , t_id] = device.torquesFromCurrentMeasurment
-
- # call logger
- # logger.sample(device, qualisys=qc)
-
- device.SendCommand(WaitEndOfCycle=True)
- if ((device.cpt % 100) == 0):
- device.Print()
-
- q_viewer[3:7] = device.baseOrientation # IMU Attitude
- q_viewer[7:] = device.q_mes # Encoders
- t += DT
- t_id += 1
- #****************************************************************
-
- # Whatever happened we send 0 torques to the motors.
- device.SetDesiredJointTorque([0]*nb_motors)
- device.SendCommand(WaitEndOfCycle=True)
-
- if device.hardware.IsTimeout():
- print("Masterboard timeout detected.")
- print("Either the masterboard has been shut down or there has been a connection issue with the cable/wifi.")
- device.hardware.Stop() # Shut down the interface between the computer and the master board
-
- # Save the logs of the Logger object
- # logger.saveAll()
-
- # Plot the results
- times = np.arange(t_min, t_max + DT, DT)
- plot_mes_des_curve(times, q_mes_t, q_des_t, "Joints positions", "joint position")
- plot_mes_des_curve(times, v_mes_t, v_des_t, "Joints velocities", "joint velocity")
- plot_mes_des_curve(times, tau_send_t, tau_des_t, "Joints torque", "Nm")
- current_t = np.zeros([8, num_steps])
- for motor in range(device.nb_motors):
- current_t[device.motorToUrdf[motor], :] = tau_send_t[device.motorToUrdf[motor], :] / device.jointKtSigned[motor]
- plot_mes_des_curve(times, current_t, title="Motor current", y_label="A")
-
- tracking_pos_error = q_des_t - q_mes_t
- plot_mes_des_curve(times, tracking_pos_error, title="Tracking error")
-
- plot_mes_des_curve(times, tau_mesured_t, title="Torque mesured from current", y_label="nM")
- current_mesured_t = np.zeros([8, num_steps])
- for motor in range(device.nb_motors):
- current_mesured_t[device.motorToUrdf[motor], :] = tau_mesured_t[device.motorToUrdf[motor], :] / device.jointKtSigned[motor]
- plot_mes_des_curve(times, current_mesured_t, title="Measured motor current", y_label="A")
-
- plt.show()
-
-def main():
- parser = argparse.ArgumentParser(description='Playback trajectory stored in a multicontact-api file.')
- parser.add_argument('-i',
- '--interface',
- required=True,
- help='Name of the interface (use ifconfig in a terminal), for instance "enp1s0"')
-
- parser.add_argument('-f',
- '--filename',
- type=str,
- required=True,
- help="The absolute path of a multicontact_api.ContactSequence serialized file")
-
- mcapi_playback(parser.parse_args().interface, parser.parse_args().filename)
-
-
-if __name__ == "__main__":
- main()
diff --git a/scripts/main_minimal_controler.py b/scripts/main_minimal_controler.py
deleted file mode 100644
index 83b545ce..00000000
--- a/scripts/main_minimal_controler.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# coding: utf8
-"""from utils.logger import Logger
-import tsid as tsid
-import pinocchio as pin
-import argparse
-import numpy as np
-import mpctsid.test_bug
-# from mpctsid.Estimator import Estimator
-#from mpctsid.Controller import Controller
-from utils.viewerClient import viewerClient, NonBlockingViewerFromRobot"""
-"""import os
-import sys
-sys.path.insert(0, './mpctsid')"""
-"""SIMULATION = True
-LOGGING = False
-
-if SIMULATION:
- from mpctsid.utils_mpc import PyBulletSimulator
-else:
- from pynput import keyboard
- from solo12 import Solo12
- from utils.qualisysClient import QualisysClient"""
-#from matplotlib import pyplot as plt
-# from utils_mpc import PyBulletSimulator
-import numpy as np
-import argparse
-from example_robot_data.path import EXAMPLE_ROBOT_DATA_MODEL_DIR
-# from solo12 import Solo12
-# from pynput import keyboard
-
-# from utils.logger import Logger
-# from utils.qualisysClient import QualisysClient
-"""import os
-import sys
-sys.path.insert(0, './mpctsid')"""
-
-DT = 0.002
-
-key_pressed = False
-
-
-def on_press(key):
- """Wait for a specific key press on the keyboard
-
- Args:
- key (keyboard.Key): the key we want to wait for
- """
- global key_pressed
- try:
- if key == keyboard.Key.enter:
- key_pressed = True
- # Stop listener
- return False
- except AttributeError:
- print('Unknown key {0} pressed'.format(key))
-
-
-def put_on_the_floor(device, q_init):
- """Make the robot go to the default initial position and wait for the user
- to press the Enter key to start the main control loop
-
- Args:
- device (robot wrapper): a wrapper to communicate with the robot
- q_init (array): the default position of the robot
- """
- global key_pressed
- key_pressed = False
- Kp_pos = 3.
- Kd_pos = 0.01
- imax = 3.0
- pos = np.zeros(device.nb_motors)
- for motor in range(device.nb_motors):
- pos[motor] = q_init[device.motorToUrdf[motor]] * \
- device.gearRatioSigned[motor]
- listener = keyboard.Listener(on_press=on_press)
- listener.start()
- print("Put the robot on the floor and press Enter")
- while not key_pressed:
- device.UpdateMeasurment()
- for motor in range(device.nb_motors):
- ref = Kp_pos * (pos[motor] - device.hardware.GetMotor(motor).GetPosition() -
- Kd_pos * device.hardware.GetMotor(motor).GetVelocity())
- ref = min(imax, max(-imax, ref))
- device.hardware.GetMotor(motor).SetCurrentReference(ref)
- device.SendCommand(WaitEndOfCycle=True)
-
- print("Start the motion.")
-
-
-def mcapi_playback(name_interface):
- """Main function that calibrates the robot, get it into a default waiting position then launch
- the main control loop once the user has pressed the Enter key
-
- Args:
- name_interface (string): name of the interface that is used to communicate with the robot
- """
- from IPython import embed
- import pybullet as pyb
- import pybullet_data
-
- # Start the client for PyBullet
- pyb.connect(pyb.GUI)
- pyb.setAdditionalSearchPath(pybullet_data.getDataPath())
- robotStartPos = [0, 0, 0.235 + 0.0045] # +0.2]
- robotStartOrientation = pyb.getQuaternionFromEuler([0.0, 0.0, 0.0]) # -np.pi/2
- pyb.setAdditionalSearchPath(EXAMPLE_ROBOT_DATA_MODEL_DIR + "/solo_description/robots")
- robotId = pyb.loadURDF("solo12.urdf", robotStartPos, robotStartOrientation)
-
- print("PASS")
- embed()
-
- #########################################
- # PARAMETERS OF THE MPC-TSID CONTROLLER #
- #########################################
-
- envID = 0 # Identifier of the environment to choose in which one the simulation will happen
- velID = 0 # Identifier of the reference velocity profile to choose which one will be sent to the robot
-
- dt_tsid = 0.0020 # Time step of TSID
- dt_mpc = 0.02 # Time step of the MPC
- # dt is dt_tsid, defined in the TSID controller script
- k_mpc = int(dt_mpc / dt_tsid)
- t = 0.0 # Time
- n_periods = 1 # Number of periods in the prediction horizon
- T_gait = 0.64 # Duration of one gait period
- N_SIMULATION = 20000 # number of simulated TSID time steps
-
- # If True the ground is flat, otherwise it has bumps
- use_flat_plane = True
-
- # Enable or disable PyBullet GUI
- enable_pyb_GUI = True
-
- # Default position after calibration
- q_init = np.array([0.0, 0.8, -1.6, 0, 0.8, -1.6, 0, -0.8, 1.6, 0, -0.8, 1.6])
-
- ####
-
- # Create device object
- # device = Solo12(name_interface, dt=DT)
- device = PyBulletSimulator()
-
- # qc = QualisysClient(ip="140.93.16.160", body_id=0) # QualisysClient
- # logger = Logger(device, qualisys=qc) # Logger object
- nb_motors = device.nb_motors
-
- # Calibrate encoders
- #device.Init(calibrateEncoders=True, q_init=q_init)
- device.Init(calibrateEncoders=True,
- q_init=q_init,
- envID=envID,
- use_flat_plane=use_flat_plane,
- enable_pyb_GUI=enable_pyb_GUI,
- dt=dt_tsid)
-
- # Wait for Enter input before starting the control loop
- # put_on_the_floor(device, q_init)
-
- # CONTROL LOOP ***************************************************
- t = 0.0
- t_max = (N_SIMULATION - 2) * dt_tsid
- while ((not device.hardware.IsTimeout()) and (t < t_max)):
-
- device.UpdateMeasurment() # Retrieve data from IMU and Motion capture
-
- # Desired torques
- tau = np.zeros(12)
-
- # Set desired torques for the actuators
- device.SetDesiredJointTorque(tau)
-
- # Call logger
- # logger.sample(device, qualisys=qc)
-
- # Send command to the robot
- device.SendCommand(WaitEndOfCycle=True)
- if ((device.cpt % 1000) == 0):
- device.Print()
-
- t += DT
-
- # ****************************************************************
-
- # Whatever happened we send 0 torques to the motors.
- device.SetDesiredJointTorque([0] * nb_motors)
- device.SendCommand(WaitEndOfCycle=True)
-
- if device.hardware.IsTimeout():
- print("Masterboard timeout detected.")
- print("Either the masterboard has been shut down or there has been a connection issue with the cable/wifi.")
- # Shut down the interface between the computer and the master board
- device.hardware.Stop()
-
- # Save the logs of the Logger object
- # logger.saveAll()
-
-
-def main():
- """Main function
- """
-
- parser = argparse.ArgumentParser(description='Playback trajectory to show the extent of solo12 workspace.')
- parser.add_argument('-i',
- '--interface',
- required=True,
- help='Name of the interface (use ifconfig in a terminal), for instance "enp1s0"')
-
- mcapi_playback(parser.parse_args().interface)
-
-
-if __name__ == "__main__":
- main()
diff --git a/scripts/main_solo12_control.py b/scripts/main_solo12_control.py
index 311ee36d..1450b12c 100644
--- a/scripts/main_solo12_control.py
+++ b/scripts/main_solo12_control.py
@@ -1,22 +1,20 @@
-# coding: utf8
-
-import os
+import numpy as np
import threading
+import time
+
from Controller import Controller
-import numpy as np
-import argparse
-from LoggerSensors import LoggerSensors
-from LoggerControl import LoggerControl
+from tools.LoggerSensors import LoggerSensors
+from tools.LoggerControl import LoggerControl
+
import quadruped_reactive_walking as qrw
-import time
params = qrw.Params() # Object that holds all controller parameters
if params.SIMULATION:
- from PyBulletSimulator import PyBulletSimulator
+ from tools.PyBulletSimulator import PyBulletSimulator
else:
import libodri_control_interface_pywrap as oci
- from solopython.utils.qualisysClient import QualisysClient
+ from tools.qualisysClient import QualisysClient
def get_input():
@@ -84,44 +82,6 @@ def check_position_error(device, controller):
return False
-def clone_movements(name_interface_clone, q_init, cloneP, cloneD, cloneQdes, cloneDQdes, cloneRunning, cloneResult):
-
- print("-- Launching clone interface --")
-
- print(name_interface_clone, params.dt_wbc)
- clone = Solo12(name_interface_clone, dt=params.dt_wbc)
- clone.Init(calibrateEncoders=True, q_init=q_init)
-
- while cloneRunning.value and not clone.hardware.IsTimeout():
- if cloneResult.value:
- clone.SetDesiredJointPDgains(cloneP[:], cloneD[:])
- clone.SetDesiredJointPosition(cloneQdes[:])
- clone.SetDesiredJointVelocity(cloneDQdes[:])
- clone.SetDesiredJointTorque([0.0] * 12)
-
- clone.SendCommand(WaitEndOfCycle=True)
-
- cloneResult.value = False
- return 0
-
-
-def launch_clone_process(name_interface_clone, q_init):
- print("PASS")
- from multiprocessing import Process, Array, Value
- cloneP = Array('d', [0] * 12)
- cloneD = Array('d', [0] * 12)
- cloneQdes = Array('d', [0] * 12)
- cloneDQdes = Array('d', [0] * 12)
- cloneRunning = Value('b', True)
- cloneResult = Value('b', True)
- clone = Process(target=clone_movements, args=(name_interface_clone, q_init, cloneP,
- cloneD, cloneQdes, cloneDQdes, cloneRunning, cloneResult))
- clone.start()
- print(cloneResult.value)
-
- return cloneResult
-
-
def damp_control(device, nb_motors):
"""
Damp the control during 2.5 seconds
@@ -153,13 +113,13 @@ def damp_control(device, nb_motors):
t += params.dt_wbc
-def control_loop(name_interface_clone=None, des_vel_analysis=None):
+def control_loop(des_vel_analysis=None):
"""
Main function that calibrates the robot, get it into a default waiting position then launch
the main control loop once the user has pressed the Enter key
Args:
- name_interface_clone (string): name of the interface that will mimic the movements of the first
+ des_vel_analysis (string)
"""
if not params.SIMULATION:
params.enable_pyb_GUI = False
@@ -186,9 +146,6 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
device = oci.robot_from_yaml_file(params.config_file)
qc = QualisysClient(ip="140.93.16.160", body_id=0)
- if name_interface_clone is not None:
- cloneResult = launch_clone_process(name_interface_clone, q_init)
-
if params.LOGGING or params.PLOTTING:
loggerSensors = LoggerSensors(device, qualisys=qc, logSize=params.N_SIMULATION-3)
loggerControl = LoggerControl(params, logSize=params.N_SIMULATION-3)
@@ -205,7 +162,8 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
put_on_the_floor(device, q_init)
# CONTROL LOOP ***************************************************
- t = 0.0 # Time variable to keep track of time
+
+ t = 0.0
t_max = (params.N_SIMULATION-2) * params.dt_wbc
t_log_whole = np.zeros((params.N_SIMULATION))
@@ -267,9 +225,6 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
finished = (t >= t_max)
damp_control(device, 12)
- if not params.SIMULATION and name_interface_clone is not None:
- cloneResult.value = False # Stop clone interface running in parallel process
-
if params.enable_multiprocessing:
print("Stopping parallel process MPC")
controller.mpc_wrapper.stop_parallel_loop()
@@ -302,14 +257,7 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='Playback trajectory to show the extent of solo12 workspace.')
- parser.add_argument('-c',
- '--clone',
- required=False,
- help='Name of the clone interface that will reproduce the movement of the first one \
- (use ifconfig in a terminal), for instance "enp1s0"')
-
# os.nice(-20) # Â Set the process to highest priority (from -20 highest to +20 lowest)
- f, v = control_loop(parser.parse_args().clone) # , np.array([1.5, 0.0, 0.0, 0.0, 0.0, 0.0]))
+ f, v = control_loop() # , np.array([1.5, 0.0, 0.0, 0.0, 0.0, 0.0]))
print(f, v)
quit()
diff --git a/scripts/main_solo12_demo_estimator.py b/scripts/main_solo12_demo_estimator.py
deleted file mode 100644
index 5f5b1745..00000000
--- a/scripts/main_solo12_demo_estimator.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# coding: utf8
-
-from utils.logger import Logger
-import tsid as tsid
-import pinocchio as pin
-import argparse
-import numpy as np
-from mpctsid.Estimator import Estimator
-from utils.viewerClient import viewerClient, NonBlockingViewerFromRobot
-import os
-import sys
-from example_robot_data.path import EXAMPLE_ROBOT_DATA_MODEL_DIR as modelPath
-
-sys.path.insert(0, './mpctsid')
-
-SIMULATION = True
-LOGGING = False
-
-if SIMULATION:
- from mpctsid.utils_mpc import PyBulletSimulator
-else:
- from pynput import keyboard
- from solo12 import Solo12
- from utils.qualisysClient import QualisysClient
-
-DT = 0.002
-
-key_pressed = False
-
-
-def on_press(key):
- """Wait for a specific key press on the keyboard
-
- Args:
- key (keyboard.Key): the key we want to wait for
- """
- global key_pressed
- try:
- if key == keyboard.Key.enter:
- key_pressed = True
- # Stop listener
- return False
- except AttributeError:
- print('Unknown key {0} pressed'.format(key))
-
-
-def put_on_the_floor(device, q_init):
- """Make the robot go to the default initial position and wait for the user
- to press the Enter key to start the main control loop
-
- Args:
- device (robot wrapper): a wrapper to communicate with the robot
- q_init (array): the default position of the robot
- """
- global key_pressed
- key_pressed = False
- Kp_pos = 3.
- Kd_pos = 0.01
- imax = 3.0
- pos = np.zeros(device.nb_motors)
- for motor in range(device.nb_motors):
- pos[motor] = q_init[device.motorToUrdf[motor]] * \
- device.gearRatioSigned[motor]
- listener = keyboard.Listener(on_press=on_press)
- listener.start()
- print("Put the robot on the floor and press Enter")
- while not key_pressed:
- device.UpdateMeasurment()
- for motor in range(device.nb_motors):
- ref = Kp_pos * (pos[motor] - device.hardware.GetMotor(motor).GetPosition() -
- Kd_pos * device.hardware.GetMotor(motor).GetVelocity())
- ref = min(imax, max(-imax, ref))
- device.hardware.GetMotor(motor).SetCurrentReference(ref)
- device.SendCommand(WaitEndOfCycle=True)
-
- print("Start the motion.")
-
-
-def mcapi_playback(name_interface):
- """Main function that calibrates the robot, get it into a default waiting position then launch
- the main control loop once the user has pressed the Enter key
-
- Args:
- name_interface (string): name of the interface that is used to communicate with the robot
- """
-
- if SIMULATION:
- device = PyBulletSimulator()
- qc = None
- else:
- device = Solo12(name_interface, dt=DT)
- qc = QualisysClient(ip="140.93.16.160", body_id=0)
-
- if LOGGING:
- logger = Logger(device, qualisys=qc)
-
- # Number of motors
- nb_motors = device.nb_motors
- q_viewer = np.array((7 + nb_motors) * [
- 0.,
- ])
-
- # Gepetto-gui
- v = viewerClient()
- v.display(q_viewer)
-
- # PyBullet GUI
- enable_pyb_GUI = True
-
- # Maximum duration of the demonstration
- t_max = 300.0
-
- # Default position after calibration
- q_init = np.array([0, 0.8, -1.6, 0, 0.8, -1.6, 0, -0.8, 1.6, 0, -0.8, 1.6])
-
- # Create Estimator object
- estimator = Estimator(DT, np.int(t_max / DT))
-
- # Set the paths where the urdf and srdf file of the robot are registered
- modelPath = "/opt/openrobots/share/example-robot-data/robots"
- urdf = modelPath + "/solo_description/robots/solo12.urdf"
- vector = pin.StdVec_StdString()
- vector.extend(item for item in modelPath)
-
- # Create the robot wrapper from the urdf model (which has no free flyer) and add a free flyer
- robot = tsid.RobotWrapper(urdf, vector, pin.JointModelFreeFlyer(), False)
- model = robot.model()
-
- # Creation of the Invverse Dynamics HQP problem using the robot
- # accelerations (base + joints) and the contact forces
- invdyn = tsid.InverseDynamicsFormulationAccForce("tsid", robot, False)
-
- # Compute the problem data with a solver based on EiQuadProg
- invdyn.computeProblemData(0.0, np.hstack((np.zeros(7), q_init)), np.zeros(18))
-
- # Initiate communication with the device and calibrate encoders
- if SIMULATION:
- device.Init(calibrateEncoders=True,
- q_init=q_init,
- envID=0,
- use_flat_plane=True,
- enable_pyb_GUI=enable_pyb_GUI,
- dt=DT)
- else:
- device.Init(calibrateEncoders=True, q_init=q_init)
-
- # Wait for Enter input before starting the control loop
- put_on_the_floor(device, q_init)
-
- # CONTROL LOOP ***************************************************
- t = 0.0
- k = 0
-
- while ((not device.hardware.IsTimeout()) and (t < t_max)):
-
- device.UpdateMeasurment() # Retrieve data from IMU and Motion capture
-
- # Run estimator with hind left leg touching the ground
- estimator.run_filter(k, np.array([0, 0, 1, 0]), device, invdyn.data(), model)
-
- # Zero desired torques
- tau = np.zeros(12)
-
- # Set desired torques for the actuators
- device.SetDesiredJointTorque(tau)
-
- # Call logger
- if LOGGING:
- logger.sample(device, qualisys=qc, estimator=estimator)
-
- # Send command to the robot
- device.SendCommand(WaitEndOfCycle=True)
- if ((device.cpt % 100) == 0):
- device.Print()
-
- # Gepetto GUI
- if k > 0:
- pos = np.array(estimator.data.oMf[26].translation).ravel()
- q_viewer[0:3] = np.array([-pos[0], -pos[1], estimator.FK_h]) # Position
- q_viewer[3:7] = estimator.q_FK[3:7, 0] # Orientation
- q_viewer[7:] = estimator.q_FK[7:, 0] # Encoders
- v.display(q_viewer)
-
- t += DT
- k += 1
-
- # ****************************************************************
-
- # Whatever happened we send 0 torques to the motors.
- device.SetDesiredJointTorque([0] * nb_motors)
- device.SendCommand(WaitEndOfCycle=True)
-
- if device.hardware.IsTimeout():
- print("Masterboard timeout detected.")
- print("Either the masterboard has been shut down or there has been a connection issue with the cable/wifi.")
- # Shut down the interface between the computer and the master board
- device.hardware.Stop()
-
- # Save the logs of the Logger object
- if LOGGING:
- logger.saveAll()
-
- if SIMULATION and enable_pyb_GUI:
- # Disconnect the PyBullet server (also close the GUI)
- device.Stop()
-
- print("End of script")
- quit()
-
-
-def main():
- """Main function
- """
-
- parser = argparse.ArgumentParser(description='Playback trajectory to show the extent of solo12 workspace.')
- parser.add_argument('-i',
- '--interface',
- required=True,
- help='Name of the interface (use ifconfig in a terminal), for instance "enp1s0"')
-
- mcapi_playback(parser.parse_args().interface)
-
-
-if __name__ == "__main__":
- main()
diff --git a/scripts/main_solo12_replay.py b/scripts/main_solo12_replay.py
index 0c3ebebe..73dccc57 100644
--- a/scripts/main_solo12_replay.py
+++ b/scripts/main_solo12_replay.py
@@ -1,25 +1,23 @@
-# coding: utf8
-
import os
import threading
import numpy as np
import argparse
-from LoggerSensors import LoggerSensors
+from tools.LoggerSensors import LoggerSensors
import quadruped_reactive_walking as qrw
import time
params = qrw.Params() # Object that holds all controller parameters
if params.SIMULATION:
- from PyBulletSimulator import PyBulletSimulator
+ from tools.PyBulletSimulator import PyBulletSimulator
else:
import libodri_control_interface_pywrap as oci
- from solopython.utils.qualisysClient import QualisysClient
+ from tools.qualisysClient import QualisysClient
+
def get_input():
- keystrk = input()
- # thread doesn't continue until key is pressed
- # and so it remains alive
+ input()
+
def put_on_the_floor(device, q_init):
"""Make the robot go to the default initial position and wait for the user
@@ -29,7 +27,6 @@ def put_on_the_floor(device, q_init):
device (robot wrapper): a wrapper to communicate with the robot
q_init (array): the default position of the robot
"""
-
print("PUT ON THE FLOOR.")
Kp_pos = 3.
@@ -54,36 +51,61 @@ def put_on_the_floor(device, q_init):
print("Start the motion.")
-def control_loop(name_interface_clone=None, des_vel_analysis=None):
- """Main function that calibrates the robot, get it into a default waiting position then launch
- the main control loop once the user has pressed the Enter key
+
+def damp_control(device, nb_motors):
+ """
+ Damp the control during 2.5 seconds
Args:
- name_interface_clone (string): name of the interface that will mimic the movements of the first
+ device (robot wrapper): a wrapper to communicate with the robot
+ nb_motors (int): number of motors
"""
+ t = 0.0
+ t_max = 2.5
+ while (not device.is_timeout) and (t < t_max):
+ device.parse_sensor_data() # Retrieve data from IMU and Motion capture
- # Check .yaml file for parameters of the controller
+ # Set desired quantities for the actuators
+ device.joints.set_position_gains(np.zeros(nb_motors))
+ device.joints.set_velocity_gains(0.1 * np.ones(nb_motors))
+ device.joints.set_desired_positions(np.zeros(nb_motors))
+ device.joints.set_desired_velocities(np.zeros(nb_motors))
+ device.joints.set_torques(np.zeros(nb_motors))
+ # Send command to the robot
+ device.send_command_and_wait_end_of_cycle(params.dt_wbc)
+ if (t % 1) < 5e-5:
+ print('IMU attitude:', device.imu.attitude_euler)
+ print('joint pos :', device.joints.positions)
+ print('joint vel :', device.joints.velocities)
+ device.robot_interface.PrintStats()
+
+ t += params.dt_wbc
+
+
+def control_loop(des_vel_analysis=None):
+ """
+ Main function that calibrates the robot, get it into a default waiting position then launch
+ the main control loop once the user has pressed the Enter key
+ """
# Read replay data
# replay = np.load("/home/odri/git/abonnefoy/Motion/Logs/push_up.npz")
- # replay = np.load("/home/odri/git/abonnefoy/Motion/Logs/one_leg_position.npz")
+ # replay = np.load("/home/odri/git/abonnefoy/Motion/Logs/one_leg_position.npz")
# replay = np.load("/home/odri/git/abonnefoy/Motion/Logs/push_up_position.npz")
replay = np.load("/home/odri/git/abonnefoy/Motion/Logs/full_push_up.npz")
- replay_q = replay['q'][7:,1:].transpose().copy()
- replay_v = replay['v'][6:,1:].transpose().copy()
+ replay_q = replay['q'][7:, 1:].transpose().copy()
+ replay_v = replay['v'][6:, 1:].transpose().copy()
replay_tau = replay['tau'].transpose().copy()
params.N_SIMULATION = replay_q.shape[0]
N = replay_q.shape[0]
- replay_P = 6.0 * np.ones((N, 12)) # replay["P"]
- replay_D = 0.3 * np.ones((N, 12)) # replay["D"]
+ replay_P = 6.0 * np.ones((N, 12)) # replay["P"]
+ replay_D = 0.3 * np.ones((N, 12)) # replay["D"]
# 0.09547498, 1.25215899, -2.01927128, -0.09552912, 1.25175677,
# -2.01855657, 0.52255345, 1.34166507, 0.11203987, -0.52311626,
# 1.34141582, 0.11178296]
- # replay_q[0, :] = np.array([0.0, 0.3, 0.0] * 4)
-
# Enable or disable PyBullet GUI
if not params.SIMULATION:
params.enable_pyb_GUI = False
@@ -95,12 +117,6 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
q_init = (replay_q[0, :]).copy()
params.q_init = q_init.tolist()
- """from IPython import embed
- from matplotlib import pyplot as plt
- embed()"""
-
- ####
-
if params.SIMULATION:
device = PyBulletSimulator()
qc = None
@@ -111,9 +127,6 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
if params.LOGGING or params.PLOTTING:
loggerSensors = LoggerSensors(device, qualisys=qc, logSize=params.N_SIMULATION-3)
- # Number of motors
- nb_motors = 12
-
# Initiate communication with the device and calibrate encoders
if params.SIMULATION:
device.Init(calibrateEncoders=True, q_init=q_init, envID=params.envID,
@@ -157,19 +170,14 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
device.joints.set_desired_velocities(replay_v[k_log_whole, :])
device.joints.set_torques(1.0 * replay_tau[k_log_whole, :])
- """print("--- ", k_log_whole)
- print(replay_q[k_log_whole, :])
- print(replay_v[k_log_whole, :])"""
-
# Call logger
if params.LOGGING or params.PLOTTING:
loggerSensors.sample(device, qc)
# Send command to the robot
- for i in range(1):
- device.send_command_and_wait_end_of_cycle(params.dt_wbc)
+ device.send_command_and_wait_end_of_cycle(params.dt_wbc)
- t += params.dt_wbc # Increment loop time
+ t += params.dt_wbc
dT_whole = T_whole
T_whole = time.time()
@@ -178,75 +186,29 @@ def control_loop(name_interface_clone=None, des_vel_analysis=None):
# ****************************************************************
- if (t >= t_max):
- finished = True
- else:
- finished = False
-
- # DAMPING TO GET ON THE GROUND PROGRESSIVELY *********************
- t = 0.0
- t_max = 2.5
- while ((not device.is_timeout) and (t < t_max)):
-
- device.parse_sensor_data() # Retrieve data from IMU and Motion capture
-
- # Set desired quantities for the actuators
- device.joints.set_position_gains(np.zeros(nb_motors))
- device.joints.set_velocity_gains(0.1 * np.ones(nb_motors))
- device.joints.set_desired_positions(np.zeros(nb_motors))
- device.joints.set_desired_velocities(np.zeros(nb_motors))
- device.joints.set_torques(np.zeros(nb_motors))
-
- # Send command to the robot
- device.send_command_and_wait_end_of_cycle(params.dt_wbc)
- if (t % 1) < 5e-5:
- print('IMU attitude:', device.imu.attitude_euler)
- print('joint pos :', device.joints.positions)
- print('joint vel :', device.joints.velocities)
- device.robot_interface.PrintStats()
-
- t += params.dt_wbc
-
- # FINAL SHUTDOWN *************************************************
-
- # Whatever happened we send 0 torques to the motors.
- device.joints.set_torques(np.zeros(nb_motors))
+ finished = (t >= t_max)
+ damp_control(device, 12)
+
+ device.joints.set_torques(np.zeros(12))
device.send_command_and_wait_end_of_cycle(params.dt_wbc)
if device.is_timeout:
print("Masterboard timeout detected.")
print("Either the masterboard has been shut down or there has been a connection issue with the cable/wifi.")
- # Save the logs of the Logger object
if params.LOGGING:
loggerSensors.saveAll()
print("Log saved")
if params.SIMULATION and params.enable_pyb_GUI:
- # Disconnect the PyBullet server (also close the GUI)
device.Stop()
- print("End of script")
-
return finished, des_vel_analysis
-def main():
- """Main function
- """
-
- parser = argparse.ArgumentParser(description='Playback trajectory to show the extent of solo12 workspace.')
- parser.add_argument('-c',
- '--clone',
- required=False,
- help='Name of the clone interface that will reproduce the movement of the first one \
- (use ifconfig in a terminal), for instance "enp1s0"')
-
+if __name__ == "__main__":
os.nice(-20) # Â Set the process to highest priority (from -20 highest to +20 lowest)
- f, v = control_loop(parser.parse_args().clone) # , np.array([1.5, 0.0, 0.0, 0.0, 0.0, 0.0]))
+ f, v = control_loop() # , np.array([1.5, 0.0, 0.0, 0.0, 0.0, 0.0]))
+ print("End of script")
print(f, v)
quit()
-
-
-if __name__ == "__main__":
- main()
diff --git a/scripts/plot_IMU_mocap_result.py b/scripts/plot_IMU_mocap_result.py
deleted file mode 100644
index 32739a81..00000000
--- a/scripts/plot_IMU_mocap_result.py
+++ /dev/null
@@ -1,788 +0,0 @@
-import glob
-import numpy as np
-from matplotlib import pyplot as plt
-import pinocchio as pin
-import tsid as tsid
-from example_robot_data.path import EXAMPLE_ROBOT_DATA_MODEL_DIR as modelPath
-from IPython import embed
-
-import plot_utils
-"""import matplotlib as matplotlib
-matplotlib.rcParams['pdf.fonttype'] = 42
-matplotlib.rcParams['ps.fonttype'] = 42"""
-
-# Transform between the base frame and the IMU frame
-_1Mi = pin.SE3(pin.Quaternion(np.array([[0.0, 0.0, 0.0, 1.0]]).transpose()), np.array([0.1163, 0.0, 0.02]))
-
-
-def EulerToQuaternion(roll_pitch_yaw):
- roll, pitch, yaw = roll_pitch_yaw
- sr = np.sin(roll / 2.)
- cr = np.cos(roll / 2.)
- sp = np.sin(pitch / 2.)
- cp = np.cos(pitch / 2.)
- sy = np.sin(yaw / 2.)
- cy = np.cos(yaw / 2.)
- qx = sr * cp * cy - cr * sp * sy
- qy = cr * sp * cy + sr * cp * sy
- qz = cr * cp * sy - sr * sp * cy
- qw = cr * cp * cy + sr * sp * sy
- return [qx, qy, qz, qw]
-
-
-def quaternionToRPY(quat):
- qx = quat[0]
- qy = quat[1]
- qz = quat[2]
- qw = quat[3]
-
- rotateXa0 = 2.0 * (qy * qz + qw * qx)
- rotateXa1 = qw * qw - qx * qx - qy * qy + qz * qz
- rotateX = 0.0
-
- if (rotateXa0 != 0.0) and (rotateXa1 != 0.0):
- rotateX = np.arctan2(rotateXa0, rotateXa1)
-
- rotateYa0 = -2.0 * (qx * qz - qw * qy)
- rotateY = 0.0
- if (rotateYa0 >= 1.0):
- rotateY = np.pi / 2.0
- elif (rotateYa0 <= -1.0):
- rotateY = -np.pi / 2.0
- else:
- rotateY = np.arcsin(rotateYa0)
-
- rotateZa0 = 2.0 * (qx * qy + qw * qz)
- rotateZa1 = qw * qw + qx * qx - qy * qy - qz * qz
- rotateZ = 0.0
- if (rotateZa0 != 0.0) and (rotateZa1 != 0.0):
- rotateZ = np.arctan2(rotateZa0, rotateZa1)
-
- return np.array([[rotateX], [rotateY], [rotateZ]])
-
-
-def linearly_interpolate_nans(y):
- # Fit a linear regression to the non-nan y values
-
- # Create X matrix for linreg with an intercept and an index
- X = np.vstack((np.ones(len(y)), np.arange(len(y))))
-
- # Get the non-NaN values of X and y
- X_fit = X[:, ~np.isnan(y)]
- y_fit = y[~np.isnan(y)].reshape(-1, 1)
-
- # Estimate the coefficients of the linear regression
- beta = np.linalg.lstsq(X_fit.T, y_fit)[0]
-
- # Fill in all the nan values using the predicted coefficients
- y.flat[np.isnan(y)] = np.dot(X[:, np.isnan(y)].T, beta)
- return y
-
-
-def cross3(left, right):
- """Numpy is inefficient for this
-
- Args:
- left (3x0 array): left term of the cross product
- right (3x0 array): right term of the cross product
- """
- return np.array([[left[1] * right[2] - left[2] * right[1]], [left[2] * right[0] - left[0] * right[2]],
- [left[0] * right[1] - left[1] * right[0]]])
-
-
-def BaseVelocityFromKinAndIMU(contactFrameId, model, data, IMU_ang_vel):
- """Estimate the velocity of the base with forward kinematics using a contact point
- that is supposed immobile in world frame
-
- Args:
- contactFrameId (int): ID of the contact point frame (foot frame)
- """
-
- frameVelocity = pin.getFrameVelocity(model, data, contactFrameId, pin.ReferenceFrame.LOCAL)
- framePlacement = pin.updateFramePlacement(model, data, contactFrameId)
-
- # Angular velocity of the base wrt the world in the base frame (Gyroscope)
- _1w01 = IMU_ang_vel.reshape((3, 1))
- # Linear velocity of the foot wrt the base in the base frame
- _Fv1F = frameVelocity.linear
- # Level arm between the base and the foot
- _1F = np.array(framePlacement.translation)
- # Orientation of the foot wrt the base
- _1RF = framePlacement.rotation
- # Linear velocity of the base from wrt world in the base frame
- _1v01 = cross3(_1F.ravel(), _1w01.ravel()) - (_1RF @ _Fv1F.reshape((3, 1)))
-
- # IMU and base frames have the same orientation
- _iv0i = _1v01 + cross3(_1Mi.translation.ravel(), _1w01.ravel())
-
- return _1v01, np.array(_iv0i)
-
-
-#########
-# START #
-#########
-
-on_solo8 = False
-"""for name in np.sort(glob.glob('./*.npz')):
- print(name)"""
-last_date = np.sort(glob.glob('./*.npz'))[-1][-20:]
-print(last_date)
-last_date = "2020_11_02_13_25.npz"
-# last_date = "2020_11_02_13_25.npz"
-# Load data file
-data = np.load("data_" + last_date)
-
-# Store content of data in variables
-
-# From Mocap
-mocapPosition = data['mocapPosition'] # Position
-mocapOrientationQuat = data['mocapOrientationQuat'] # Orientation as quat
-mocapOrientationMat9 = data['mocapOrientationMat9'] # as 3 by 3 matrix
-mocapVelocity = data['mocapVelocity'] # Linear velocity
-mocapAngularVelocity = data['mocapAngularVelocity'] # Angular velocity
-
-cheatLinearVelocity = data["baseLinearVelocity"]
-cheatForce = data["appliedForce"]
-cheatPos = data["dummyPos"]
-
-# Fill NaN mocap values with linear interpolation
-for i in range(3):
- mocapPosition[:, i] = linearly_interpolate_nans(mocapPosition[:, i])
- mocapVelocity[:, i] = linearly_interpolate_nans(mocapVelocity[:, i])
- mocapAngularVelocity[:, i] = linearly_interpolate_nans(mocapAngularVelocity[:, i])
-
-# From IMU
-baseOrientation = data['baseOrientation'] # Orientation as quat
-baseLinearAcceleration = data['baseLinearAcceleration'] # Linear acceleration
-baseAngularVelocity = data['baseAngularVelocity'] # Angular Vel
-# Acceleration with gravity vector
-baseAccelerometer = data['baseAccelerometer']
-print(baseAngularVelocity)
-# From actuators
-torquesFromCurrentMeasurment = data['torquesFromCurrentMeasurment'] # Torques
-q_mes = data['q_mes'] # Angular positions
-v_mes = data['v_mes'] # Angular velocities
-
-data_control = np.load("data_control_" + last_date)
-
-log_tau_ff = data_control['log_tau_ff'] # Position
-log_qdes = data_control['log_qdes'] # Orientation as quat
-log_vdes = data_control['log_vdes'] # as 3 by 3 matrix
-log_q = data_control['log_q']
-log_dq = data_control['log_dq']
-
-# From estimator
-if data['estimatorVelocity'] is not None:
- estimatorHeight = data['estimatorHeight']
- estimatorVelocity = data['estimatorVelocity']
- contactStatus = data['contactStatus']
- referenceVelocity = np.round(data['referenceVelocity'], 3)
- log_xfmpc = data['logXFMPC']
-
-# Creating time vector
-Nlist = np.where(mocapPosition[:, 0] == 0.0)[0]
-if len(Nlist) > 0:
- N = Nlist[0]
-else:
- N = mocapPosition.shape[0]
-if N == 0:
- N = baseOrientation.shape[0]
-N = baseOrientation.shape[0]
-Tend = N * 0.002
-t = np.linspace(0, Tend, N + 1, endpoint=True)
-t = t[:-1]
-
-# Parameters
-dt = 0.0020
-lwdth = 2
-
-#######
-#######
-
-imuRPY = np.zeros((N, 3))
-vx_ref = np.zeros((N, 1))
-vy_ref = np.zeros((N, 1))
-vx_est = np.zeros((N, 1))
-vy_est = np.zeros((N, 1))
-for i in range(N):
- imuRPY[i, :] = pin.rpy.matrixToRpy(pin.Quaternion(baseOrientation[i:(i + 1), :].transpose()).toRotationMatrix())
-
- c = np.cos(imuRPY[i, 2])
- s = np.sin(imuRPY[i, 2])
- R = np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])
- v = R.transpose() @ log_xfmpc[i:(i + 1), 6:9].transpose()
- # v = pin.Quaternion(baseOrientation[i:(i+1), :].transpose()).toRotationMatrix().transpose() @ log_xfmpc[i:(i+1), 6:9].transpose()
- vx_ref[i] = v[0]
- vy_ref[i] = v[1]
-
- v_est = log_dq[0:3, i:(i + 1)]
- vx_est[i] = v_est[0]
- vy_est[i] = v_est[1]
-
-plot_forces = True
-
-c = ["royalblue", "forestgreen"]
-lwdth = 1
-velID = 4
-# embed()
-# HEIGHT / ROLL / PITCH FIGURE
-fig1 = plt.figure(figsize=(7, 4))
-offset_h = cheatPos[0, 2] - log_xfmpc[0, 2]
-# Height subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t[:-1], cheatPos[:-1, 2] - offset_h, color=c[0], linewidth=lwdth)
-plt.plot(t[:-1], log_q[2, :-1], color="darkgreen", linewidth=lwdth)
-plt.plot(t[:-1], log_xfmpc[:-1, 2], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("Height [m]", fontsize=14)
-plt.legend(["Ground truth", "Estimated", "Command"], prop={'size': 10}, loc=2)
-
-# Roll subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t[:-1], imuRPY[:-1, 0], color=c[0], linewidth=lwdth)
-plt.plot(t[:-1], log_q[3, :-1], color="darkgreen", linewidth=lwdth)
-plt.plot(t[:-1], log_xfmpc[:-1, 3], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("Roll [rad]", fontsize=14)
-
-if plot_forces:
- ax1bis = ax1.twinx()
- ax1bis.set_ylabel("$F_y$ [N]", color='k', fontsize=14)
- ax1bis.plot(t[:-1], cheatForce[:-1, 1], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax1bis.tick_params(axis='y', labelcolor='k')
- ax1bis.legend(["Force"], prop={'size': 10}, loc=1)
-
-# Pitch subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t[:-1], imuRPY[:-1, 1], color=c[0], linewidth=lwdth)
-plt.plot(t[:-1], log_q[4, :-1], color="darkgreen", linewidth=lwdth)
-plt.plot(t[:-1], log_xfmpc[:-1, 4], "darkorange", linewidth=lwdth, linestyle="--")
-plt.xlabel("Time [s]", fontsize=16)
-plt.ylabel("Pitch [rad]", fontsize=14)
-
-if plot_forces:
- ax2bis = ax2.twinx()
- ax2bis.set_ylabel("$F_x$ [N]", color='k', fontsize=14)
- ax2bis.plot(t[:-1], cheatForce[:-1, 0], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax2bis.tick_params(axis='y', labelcolor='k')
- ax2bis.legend(["Force"], prop={'size': 10}, loc=1)
-
-for ax in [ax0, ax1, ax2]:
- ax.tick_params(axis='both', which='major', labelsize=10)
- ax.tick_params(axis='both', which='minor', labelsize=10)
-
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/H_R_P_vele" + str(velID) +
- last_date[:-4] + ".eps",
- dpi="figure",
- bbox_inches="tight")
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/H_R_P_vele" + str(velID) +
- last_date[:-4] + ".png",
- dpi=800,
- bbox_inches="tight")
-
-# VX / VY / WYAW FIGURE
-fig2 = plt.figure(figsize=(7, 4))
-# Forward velocity subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t[:-1], cheatLinearVelocity[:-1, 0], color=c[0], linewidth=lwdth)
-plt.plot(t[:-1], vx_est[:-1], color="darkgreen", linewidth=lwdth)
-plt.plot(t[:-1], vx_ref[:-1], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("$\dot x$ [m/s]", fontsize=14)
-ax0.legend(["Ground truth", "Estimated", "Command"], prop={'size': 10}, loc=2)
-
-if plot_forces:
- ax0bis = ax0.twinx()
- ax0bis.set_ylabel("$F_x$ [N]", color='k', fontsize=14)
- ax0bis.plot(t[:-1], cheatForce[:-1, 0], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax0bis.tick_params(axis='y', labelcolor='k')
- ax0bis.legend(["Force"], prop={'size': 10}, loc=1)
-
-# Lateral velocity subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t[:-1], cheatLinearVelocity[:-1, 1], color=c[0], linewidth=lwdth)
-plt.plot(t[:-1], vy_est[:-1], color="darkgreen", linewidth=lwdth)
-plt.plot(t[:-1], vy_ref[:-1], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("$\dot y$ [m/s]", fontsize=14)
-#ax1.legend(["Ground truth", "Estimated", "Command"], prop={'size': 10}, loc=2)
-
-if plot_forces:
- ax1bis = ax1.twinx()
- ax1bis.set_ylabel("$F_y$ [N]", color='k', fontsize=14)
- ax1bis.plot(t[:-1], cheatForce[:-1, 1], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax1bis.tick_params(axis='y', labelcolor='k')
- ax1bis.legend(["Force"], prop={'size': 10}, loc=1)
-
-# Angular velocity yaw subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t[:-1], baseAngularVelocity[:-1, 2], color=c[0], linewidth=lwdth)
-plt.plot(t[:-1], log_dq[5, :-1], color="darkgreen", linewidth=lwdth)
-plt.plot(t[:-1], log_xfmpc[:-1, 11], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("$\dot \omega_z$ [rad/s]", fontsize=16)
-plt.xlabel("Time [s]", fontsize=14)
-#ax2.legend(["Ground truth", "Estimated", "Command"], prop={'size': 10}, loc=2)
-
-for ax in [ax0, ax1, ax2]:
- ax.tick_params(axis='both', which='major', labelsize=10)
- ax.tick_params(axis='both', which='minor', labelsize=10)
-
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/Vx_Vy_Wyaw_vele" +
- str(velID) + last_date[:-4] + ".eps",
- dpi="figure",
- bbox_inches="tight")
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/Vx_Vy_Wyaw_vele" +
- str(velID) + last_date[:-4] + ".png",
- dpi=800,
- bbox_inches="tight")
-
-plt.show(block=True)
-
-########
-########
-
-# embed()
-# X_F_MPC
-index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
-lgd1 = ["Ctct force X", "Ctct force Y", "Ctct force Z"]
-lgd2 = ["FL", "FR", "HL", "HR"]
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index[i])
- else:
- plt.subplot(3, 4, index[i], sharex=ax0)
- plt.plot(log_xfmpc[:, i], "b", linewidth=2)
- # plt.ylabel(lgd[i])
-plt.suptitle("b_xfmpc")
-
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index[i])
- else:
- plt.subplot(3, 4, index[i], sharex=ax0)
-
- h1, = plt.plot(log_xfmpc[:, 12 + i], "b", linewidth=5)
-
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3] + " " + lgd2[int(i / 3)])
-
- if (i % 3) == 2:
- plt.ylim([-1.0, 15.0])
- else:
- plt.ylim([-1.5, 1.5])
-
-plt.suptitle("b_xfmpc forces")
-
-# plt.show(block=True)
-
-###############
-# ORIENTATION #
-###############
-
-mocapRPY = np.zeros((N, 3))
-imuRPY = np.zeros((N, 3))
-for i in range(N):
- mocapRPY[i, :] = pin.rpy.matrixToRpy(mocapOrientationMat9[i, :, :])
- imuRPY[i, :] = pin.rpy.matrixToRpy(pin.Quaternion(baseOrientation[i:(i + 1), :].transpose()).toRotationMatrix())
-
-fig = plt.figure()
-# Roll orientation
-ax0 = plt.subplot(2, 1, 1)
-plt.plot(t, mocapRPY[:N, 0], "darkorange", linewidth=lwdth)
-plt.plot(t, imuRPY[:N, 0], "royalblue", linewidth=lwdth)
-plt.ylabel("Roll")
-plt.legend(["Mocap", "IMU"], prop={'size': 8})
-# Pitch orientation
-ax1 = plt.subplot(2, 1, 2, sharex=ax0)
-plt.plot(t, mocapRPY[:N, 1], "darkorange", linewidth=lwdth)
-plt.plot(t, imuRPY[:N, 1], "royalblue", linewidth=lwdth)
-plt.ylabel("Pitch")
-plt.xlabel("Time [s]")
-
-# embed()
-
-###################
-# LINEAR VELOCITY #
-###################
-mocapBaseLinearVelocity = np.zeros((N, 3))
-imuBaseLinearVelocity = np.zeros((N, 3))
-for i in range(N):
- mocapBaseLinearVelocity[i, :] = (
- (mocapOrientationMat9[i, :, :]) @ (mocapVelocity[i:(i + 1), :]).transpose()).ravel()
- if i == 0:
- imuBaseLinearVelocity[i, :] = mocapBaseLinearVelocity[0, :]
- else:
- imuBaseLinearVelocity[i, :] = imuBaseLinearVelocity[i - 1, :] + dt * baseLinearAcceleration[i - 1, :]
-
-fig = plt.figure()
-# X linear velocity
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, mocapBaseLinearVelocity[:N, 0], "darkorange", linewidth=lwdth)
-plt.plot(t, imuBaseLinearVelocity[:N, 0], "royalblue", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 0], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 0], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot x$ [m/s]")
-plt.legend(["Mocap", "IMU", "Estimator", "Reference"], prop={'size': 8})
-# Y linear velocity
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, mocapBaseLinearVelocity[:N, 1], "darkorange", linewidth=lwdth)
-plt.plot(t, imuBaseLinearVelocity[:N, 1], "royalblue", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 1], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 1], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot y$ [m/s]")
-# Z linear velocity
-ax1 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, mocapBaseLinearVelocity[:N, 2], "darkorange", linewidth=lwdth)
-plt.plot(t, imuBaseLinearVelocity[:N, 2], "royalblue", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 2], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 2], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot z$ [m/s]")
-plt.xlabel("Time [s]")
-
-######################
-# ANGULAR VELOCITIES #
-######################
-mocapBaseAngularVelocity = np.zeros(mocapAngularVelocity.shape)
-for i in range(N):
- #mocapBaseAngularVelocity[i, :] = ((mocapOrientationMat9[i, :, :]) @ (mocapAngularVelocity[i:(i+1), :]).transpose()).ravel()
- mocapBaseAngularVelocity[i, :] = (mocapAngularVelocity[i:(i + 1), :]).transpose().ravel()
-
-fig = plt.figure()
-# Angular velocity X subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, mocapBaseAngularVelocity[:N, 0], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 0], "royalblue", linewidth=lwdth * 2)
-plt.plot(t, referenceVelocity[:N, 3], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \phi$ [rad/s]")
-plt.legend(["Mocap", "IMU", "Reference"], prop={'size': 8})
-# Angular velocity Y subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, mocapBaseAngularVelocity[:N, 1], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 1], "royalblue", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 4], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \\theta$ [rad/s]")
-# Angular velocity Z subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, mocapBaseAngularVelocity[:N, 2], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 2], "royalblue", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 5], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \psi$ [rad/s]")
-plt.xlabel("Time [s]")
-
-#######################
-# LINEAR ACCELERATION #
-#######################
-
-fig = plt.figure()
-# X linear acc
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, baseLinearAcceleration[:N, 0], "royalblue", linewidth=lwdth)
-plt.ylabel("$\ddot x$ [m/s^2]")
-plt.legend(["IMU"], prop={'size': 8})
-# Y linear acc
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, baseLinearAcceleration[:N, 1], "royalblue", linewidth=lwdth)
-plt.ylabel("$\ddot y$ [m/s^2]")
-# Z linear acc
-ax1 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, baseLinearAcceleration[:N, 2], "royalblue", linewidth=lwdth)
-plt.ylabel("$\ddot z$ [m/s^2]")
-plt.xlabel("Time [s]")
-
-####################
-# JOYSTICK CONTROL #
-####################
-
-fig = plt.figure()
-# X linear velocity
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, mocapBaseLinearVelocity[:N, 0], "darkorange", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 0], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 0], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot x$ [m/s]")
-plt.legend(["Mocap", "Estimator", "Reference"], prop={'size': 8})
-# Y linear velocity
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, mocapBaseLinearVelocity[:N, 1], "darkorange", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 1], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 1], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot y$ [m/s]")
-# Z linear velocity
-ax1 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, mocapBaseAngularVelocity[:N, 2], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 2], "royalblue", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 5], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \psi$ [rad/s]")
-plt.xlabel("Time [s]")
-plt.suptitle("Tracking of the velocity command sent to the robot")
-
-#############
-# ACTUATORS #
-#############
-
-index = [1, 5, 2, 6, 3, 7, 4, 8]
-plt.figure()
-for i in range(8):
- if i == 0:
- ax0 = plt.subplot(2, 4, index[i])
- else:
- plt.subplot(2, 4, index[i], sharex=ax0)
- plt.plot(t, torquesFromCurrentMeasurment[:N, i], "forestgreen", linewidth=lwdth)
-
- if (i % 2 == 1):
- plt.xlabel("Time [s]")
- if i <= 1:
- plt.ylabel("Torques [N.m]")
-
-contact_state = np.zeros((N, 4))
-margin = 25
-treshold = 0.4
-for i in range(4):
- state = 0
- front_up = 0
- front_down = 0
- for j in range(N):
- if (state == 0) and (np.abs(torquesFromCurrentMeasurment[j, 2 * i + 1]) >= treshold):
- state = 1
- front_up = j
- if (state == 1) and (np.abs(torquesFromCurrentMeasurment[j, 2 * i + 1]) < treshold):
- state = 0
- front_down = j
- l = np.min((front_up + margin, N))
- u = np.max((front_down - margin, 0))
- contact_state[l:u, i] = 1
-
-plt.figure()
-for i in range(4):
- if i == 0:
- ax0 = plt.subplot(1, 4, i + 1)
- else:
- plt.subplot(1, 4, i + 1, sharex=ax0)
- plt.plot(t, torquesFromCurrentMeasurment[:N, 2 * i + 1])
- plt.plot(t, contact_state[:N, i])
- plt.legend(["Torque", "Estimated contact state"], prop={'size': 8})
-"""fig = plt.figure()
-# Angular velocity X subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, torquesFromCurrentMeasurment[:N,
- 0], "forestgreen", linewidth=lwdth)
-plt.ylabel("Torques [N.m]")
-# Angular velocity Y subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, q_mes[:N, 0], "forestgreen", linewidth=lwdth)
-plt.ylabel("Angular position [rad]")
-# Angular velocity Z subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, v_mes[:N, 2], "forestgreen", linewidth=lwdth)
-plt.ylabel("Angular velocity [rad/s]")
-plt.xlabel("Time [s]")"""
-
-# Set the paths where the urdf and srdf file of the robot are registered
-if on_solo8:
- urdf = modelPath + "/solo_description/robots/solo.urdf"
-else:
- urdf = modelPath + "/solo_description/robots/solo12.urdf"
-srdf = modelPath + "/solo_description/srdf/solo.srdf"
-vector = pin.StdVec_StdString()
-vector.extend(item for item in modelPath)
-
-# Create the robot wrapper from the urdf model (which has no free flyer) and add a free flyer
-robot = tsid.RobotWrapper(urdf, vector, pin.JointModelFreeFlyer(), False)
-model = robot.model()
-
-# Creation of the Invverse Dynamics HQP problem using the robot
-# accelerations (base + joints) and the contact forces
-invdyn = tsid.InverseDynamicsFormulationAccForce("tsid", robot, False)
-
-# Compute the problem data with a solver based on EiQuadProg
-t0 = 0.0
-if on_solo8:
- q_FK = np.zeros((15, 1))
-else:
- q_FK = np.zeros((19, 1))
-q_FK[:7, 0] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0])
-RPY = quaternionToRPY(baseOrientation.ravel())
-IMU_ang_pos = np.zeros(4)
-IMU_ang_pos[:] = EulerToQuaternion([RPY[0], RPY[1], 0.0])
-q_FK[3:7, 0] = IMU_ang_pos
-
-v_FK = np.zeros((q_FK.shape[0] - 1, 1))
-invdyn.computeProblemData(t0, q_FK, v_FK)
-data = invdyn.data()
-
-# Feet indexes
-if on_solo8:
- indexes = [8, 14, 20, 26] # solo8
-else:
- indexes = [10, 18, 26, 34] # solo12
-alpha = 0.98
-filteredLinearVelocity = np.zeros((N, 3))
-"""for a in range(len(model.frames)):
- print(a)
- print(model.frames[a])"""
-FK_lin_vel_log = np.nan * np.zeros((N, 3))
-iFK_lin_vel_log = np.nan * np.zeros((N, 3))
-rms_x = []
-rms_y = []
-rms_z = []
-irms_x = []
-irms_y = []
-irms_z = []
-alphas = [0.97] # [0.01*i for i in range(100)]
-i_not_nan = np.where(np.logical_not(np.isnan(mocapBaseLinearVelocity[:, 0])))
-i_not_nan = (i_not_nan[0])[(i_not_nan[0] < 9600)]
-for alpha in alphas:
- print(alpha)
- filteredLinearVelocity = np.zeros((N, 3))
- ifilteredLinearVelocity = np.zeros((N, 3))
- for i in range(N):
- # Update estimator FK model
- q_FK[7:, 0] = q_mes[i, :] # Position of actuators
- v_FK[6:, 0] = v_mes[i, :] # Velocity of actuators
-
- pin.forwardKinematics(model, data, q_FK, v_FK)
-
- # Get estimated velocity from updated model
- cpt = 0
- vel_est = np.zeros((3, ))
- ivel_est = np.zeros((3, ))
- for j in (np.where(contactStatus[i, :] == 1))[0]:
- vel_estimated_baseframe, _iv0i = BaseVelocityFromKinAndIMU(indexes[j], model, data,
- baseAngularVelocity[i, :])
-
- cpt += 1
- vel_est += vel_estimated_baseframe[:, 0]
- ivel_est = ivel_est + _iv0i.ravel()
- if cpt > 0:
- FK_lin_vel = vel_est / cpt # average of all feet in contact
- iFK_lin_vel = ivel_est / cpt
-
- filteredLinearVelocity[i, :] = alpha * (filteredLinearVelocity[i - 1, :] +
- baseLinearAcceleration[i, :] * dt) + (1 - alpha) * FK_lin_vel
- FK_lin_vel_log[i, :] = FK_lin_vel
-
- # Get previous base vel wrt world in base frame into IMU frame
- i_filt_lin_vel = ifilteredLinearVelocity[i-1, :] + \
- cross3(_1Mi.translation.ravel(),
- baseAngularVelocity[i, :]).ravel()
-
- # Merge IMU base vel wrt world in IMU frame with FK base vel wrt world in IMU frame
- i_merged_lin_vel = alpha * \
- (i_filt_lin_vel +
- baseLinearAcceleration[i, :] * dt) + (1 - alpha) * iFK_lin_vel.ravel()
- """print("##")
- print(filteredLinearVelocity[i, :])
- print(i_merged_lin_vel)"""
- # Get merged base vel wrt world in IMU frame into base frame
- ifilteredLinearVelocity[i, :] = np.array(
- i_merged_lin_vel + cross3(-_1Mi.translation.ravel(), baseAngularVelocity[i, :]).ravel())
- #print(ifilteredLinearVelocity[i, :])
- """if np.array_equal(filteredLinearVelocity[i, :], ifilteredLinearVelocity[i, :]):
- print("Same values")
-
- else:
- print("Different")
- print(filteredLinearVelocity[i, :])
- print(ifilteredLinearVelocity[i, :])"""
-
- else:
- filteredLinearVelocity[i, :] = filteredLinearVelocity[i - 1, :] + baseLinearAcceleration[i, :] * dt
-
- # Get previous base vel wrt world in base frame into IMU frame
- i_filt_lin_vel = ifilteredLinearVelocity[i-1, :] + \
- cross3(_1Mi.translation.ravel(),
- baseAngularVelocity[i, :]).ravel()
- # Merge IMU base vel wrt world in IMU frame with FK base vel wrt world in IMU frame
- i_merged_lin_vel = i_filt_lin_vel + \
- baseLinearAcceleration[i, :] * dt
- # Get merged base vel wrt world in IMU frame into base frame
- ifilteredLinearVelocity[i, :] = i_merged_lin_vel + \
- cross3(-_1Mi.translation.ravel(),
- baseAngularVelocity[i, :]).ravel()
-
- rms_x.append(
- np.sqrt(np.mean(np.square(filteredLinearVelocity[i_not_nan, 0] - mocapBaseLinearVelocity[i_not_nan, 0]))))
- rms_y.append(
- np.sqrt(np.mean(np.square(filteredLinearVelocity[i_not_nan, 1] - mocapBaseLinearVelocity[i_not_nan, 1]))))
- rms_z.append(
- np.sqrt(np.mean(np.square(filteredLinearVelocity[i_not_nan, 2] - mocapBaseLinearVelocity[i_not_nan, 2]))))
- irms_x.append(
- np.sqrt(np.mean(np.square(ifilteredLinearVelocity[i_not_nan, 0] - mocapBaseLinearVelocity[i_not_nan, 0]))))
- irms_y.append(
- np.sqrt(np.mean(np.square(ifilteredLinearVelocity[i_not_nan, 1] - mocapBaseLinearVelocity[i_not_nan, 1]))))
- irms_z.append(
- np.sqrt(np.mean(np.square(ifilteredLinearVelocity[i_not_nan, 2] - mocapBaseLinearVelocity[i_not_nan, 2]))))
-
-plt.figure()
-plt.plot(alphas, rms_x)
-plt.plot(alphas, rms_y)
-plt.plot(alphas, rms_z)
-plt.plot(alphas, irms_x)
-plt.plot(alphas, irms_y)
-plt.plot(alphas, irms_z)
-plt.legend(["RMS X", "RMS Y", "RMS Z", "New RMS X", "New RMS Y", "New RMS Z"], prop={'size': 8})
-plt.xlabel("Alpha")
-plt.ylabel("RMS erreur en vitesse")
-
-fc = 10
-y = 1 - np.cos(2 * np.pi * fc * dt)
-alpha_v = -y + np.sqrt(y * y + 2 * y)
-lowpass_ifilteredLinearVelocity = np.zeros(ifilteredLinearVelocity.shape)
-lowpass_ifilteredLinearVelocity[0, :] = ifilteredLinearVelocity[0, :]
-for k in range(1, N):
- lowpass_ifilteredLinearVelocity[
- k, :] = (1 - alpha_v) * lowpass_ifilteredLinearVelocity[k - 1, :] + alpha_v * ifilteredLinearVelocity[k, :]
-
-plt.figure()
-plt.plot(t, filteredLinearVelocity[:N, 0], linewidth=3)
-plt.plot(t, ifilteredLinearVelocity[:N, 0], linewidth=3, linestyle="--")
-plt.plot(t, mocapBaseLinearVelocity[:N, 0], linewidth=3)
-plt.plot(t, lowpass_ifilteredLinearVelocity[:N, 0], color="darkviolet", linewidth=3)
-# plt.plot(t, FK_lin_vel_log[:N, 0], color="darkviolet", linestyle="--")
-"""plt.plot(t, baseLinearAcceleration[:N, 0], linestyle="--")"""
-plt.legend(["Filtered", "New Filtered", "Mocap"], prop={'size': 8})
-# plt.show()
-
-data_control = np.load("data_control_" + last_date)
-
-log_tau_ff = data_control['log_tau_ff'] # Position
-log_qdes = data_control['log_qdes'] # Orientation as quat
-log_vdes = data_control['log_vdes'] # as 3 by 3 matrix
-log_q = data_control['log_q']
-
-index12 = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- plt.plot(t, log_qdes[i, :], color='r', linewidth=lwdth)
- plt.plot(t, q_mes[:, i], color='b', linewidth=lwdth)
- plt.legend(["Qdes", "Qmes"], prop={'size': 8})
-
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- plt.plot(t, log_vdes[i, :], color='r', linewidth=lwdth)
- plt.plot(t, v_mes[:, i], color='b', linewidth=lwdth)
- plt.legend(["Vdes", "Vmes"], prop={'size': 8})
-
-# Z linear velocity
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- plt.plot(t, log_tau_ff[i, :], color='r', linewidth=lwdth)
- plt.plot(t, torquesFromCurrentMeasurment[:, i], color='b', linewidth=lwdth)
- plt.legend(["Tau_FF", "TAU"], prop={'size': 8})
-
-plt.show(block=True)
diff --git a/scripts/plot_IMU_mocap_result_bis.py b/scripts/plot_IMU_mocap_result_bis.py
deleted file mode 100644
index 985c69d0..00000000
--- a/scripts/plot_IMU_mocap_result_bis.py
+++ /dev/null
@@ -1,802 +0,0 @@
-import glob
-import numpy as np
-from matplotlib import pyplot as plt
-import pinocchio as pin
-import tsid as tsid
-from example_robot_data.path import EXAMPLE_ROBOT_DATA_MODEL_DIR as modelPath
-from IPython import embed
-
-import plot_utils
-"""import matplotlib as matplotlib
-matplotlib.rcParams['pdf.fonttype'] = 42
-matplotlib.rcParams['ps.fonttype'] = 42
-matplotlib.rcParams['text.usetex'] = True"""
-
-# Transform between the base frame and the IMU frame
-_1Mi = pin.SE3(pin.Quaternion(np.array([[0.0, 0.0, 0.0, 1.0]]).transpose()), np.array([0.1163, 0.0, 0.02]))
-
-
-def EulerToQuaternion(roll_pitch_yaw):
- roll, pitch, yaw = roll_pitch_yaw
- sr = np.sin(roll / 2.)
- cr = np.cos(roll / 2.)
- sp = np.sin(pitch / 2.)
- cp = np.cos(pitch / 2.)
- sy = np.sin(yaw / 2.)
- cy = np.cos(yaw / 2.)
- qx = sr * cp * cy - cr * sp * sy
- qy = cr * sp * cy + sr * cp * sy
- qz = cr * cp * sy - sr * sp * cy
- qw = cr * cp * cy + sr * sp * sy
- return [qx, qy, qz, qw]
-
-
-def quaternionToRPY(quat):
- qx = quat[0]
- qy = quat[1]
- qz = quat[2]
- qw = quat[3]
-
- rotateXa0 = 2.0 * (qy * qz + qw * qx)
- rotateXa1 = qw * qw - qx * qx - qy * qy + qz * qz
- rotateX = 0.0
-
- if (rotateXa0 != 0.0) and (rotateXa1 != 0.0):
- rotateX = np.arctan2(rotateXa0, rotateXa1)
-
- rotateYa0 = -2.0 * (qx * qz - qw * qy)
- rotateY = 0.0
- if (rotateYa0 >= 1.0):
- rotateY = np.pi / 2.0
- elif (rotateYa0 <= -1.0):
- rotateY = -np.pi / 2.0
- else:
- rotateY = np.arcsin(rotateYa0)
-
- rotateZa0 = 2.0 * (qx * qy + qw * qz)
- rotateZa1 = qw * qw + qx * qx - qy * qy - qz * qz
- rotateZ = 0.0
- if (rotateZa0 != 0.0) and (rotateZa1 != 0.0):
- rotateZ = np.arctan2(rotateZa0, rotateZa1)
-
- return np.array([[rotateX], [rotateY], [rotateZ]])
-
-
-def linearly_interpolate_nans(y):
- # Fit a linear regression to the non-nan y values
-
- # Create X matrix for linreg with an intercept and an index
- X = np.vstack((np.ones(len(y)), np.arange(len(y))))
-
- # Get the non-NaN values of X and y
- X_fit = X[:, ~np.isnan(y)]
- y_fit = y[~np.isnan(y)].reshape(-1, 1)
-
- # Estimate the coefficients of the linear regression
- beta = np.linalg.lstsq(X_fit.T, y_fit)[0]
-
- # Fill in all the nan values using the predicted coefficients
- y.flat[np.isnan(y)] = np.dot(X[:, np.isnan(y)].T, beta)
- return y
-
-
-def cross3(left, right):
- """Numpy is inefficient for this
-
- Args:
- left (3x0 array): left term of the cross product
- right (3x0 array): right term of the cross product
- """
- return np.array([[left[1] * right[2] - left[2] * right[1]], [left[2] * right[0] - left[0] * right[2]],
- [left[0] * right[1] - left[1] * right[0]]])
-
-
-def BaseVelocityFromKinAndIMU(contactFrameId, model, data, IMU_ang_vel):
- """Estimate the velocity of the base with forward kinematics using a contact point
- that is supposed immobile in world frame
-
- Args:
- contactFrameId (int): ID of the contact point frame (foot frame)
- """
-
- frameVelocity = pin.getFrameVelocity(model, data, contactFrameId, pin.ReferenceFrame.LOCAL)
- framePlacement = pin.updateFramePlacement(model, data, contactFrameId)
-
- # Angular velocity of the base wrt the world in the base frame (Gyroscope)
- _1w01 = IMU_ang_vel.reshape((3, 1))
- # Linear velocity of the foot wrt the base in the base frame
- _Fv1F = frameVelocity.linear
- # Level arm between the base and the foot
- _1F = np.array(framePlacement.translation)
- # Orientation of the foot wrt the base
- _1RF = framePlacement.rotation
- # Linear velocity of the base from wrt world in the base frame
- _1v01 = cross3(_1F.ravel(), _1w01.ravel()) - (_1RF @ _Fv1F.reshape((3, 1)))
-
- # IMU and base frames have the same orientation
- _iv0i = _1v01 + cross3(_1Mi.translation.ravel(), _1w01.ravel())
-
- return _1v01, np.array(_iv0i)
-
-
-#########
-# START #
-#########
-
-on_solo8 = False
-"""for name in np.sort(glob.glob('./*.npz')):
- print(name)"""
-last_date = np.sort(glob.glob('./*.npz'))[-1][-20:]
-print(last_date)
-# last_date = "2020_11_02_13_25.npz"
-# Load data file
-data = np.load("data_" + last_date)
-
-# Store content of data in variables
-
-# From Mocap
-mocapPosition = data['mocapPosition'] # Position
-mocapOrientationQuat = data['mocapOrientationQuat'] # Orientation as quat
-mocapOrientationMat9 = data['mocapOrientationMat9'] # as 3 by 3 matrix
-mocapVelocity = data['mocapVelocity'] # Linear velocity
-mocapAngularVelocity = data['mocapAngularVelocity'] # Angular velocity
-
-cheatLinearVelocity = data["baseLinearVelocity"]
-cheatForce = data["appliedForce"]
-cheatPos = data["dummyPos"]
-
-# Fill NaN mocap values with linear interpolation
-for i in range(3):
- mocapPosition[:, i] = linearly_interpolate_nans(mocapPosition[:, i])
- mocapVelocity[:, i] = linearly_interpolate_nans(mocapVelocity[:, i])
- mocapAngularVelocity[:, i] = linearly_interpolate_nans(mocapAngularVelocity[:, i])
-
-# From IMU
-baseOrientation = data['baseOrientation'] # Orientation as quat
-baseLinearAcceleration = data['baseLinearAcceleration'] # Linear acceleration
-baseAngularVelocity = data['baseAngularVelocity'] # Angular Vel
-# Acceleration with gravity vector
-baseAccelerometer = data['baseAccelerometer']
-print(baseAngularVelocity)
-# From actuators
-torquesFromCurrentMeasurment = data['torquesFromCurrentMeasurment'] # Torques
-q_mes = data['q_mes'] # Angular positions
-v_mes = data['v_mes'] # Angular velocities
-
-data_control = np.load("data_control_" + last_date)
-
-log_tau_ff = data_control['log_tau_ff'] # Position
-log_qdes = data_control['log_qdes'] # Orientation as quat
-log_vdes = data_control['log_vdes'] # as 3 by 3 matrix
-log_q = data_control['log_q']
-log_dq = data_control['log_dq']
-
-# From estimator
-if data['estimatorVelocity'] is not None:
- estimatorHeight = data['estimatorHeight']
- estimatorVelocity = data['estimatorVelocity']
- contactStatus = data['contactStatus']
- referenceVelocity = np.round(data['referenceVelocity'], 3)
- log_xfmpc = data['logXFMPC']
-
-# Cut before end
-S = 3000
-E = -16000
-
-# Creating time vector
-Nlist = np.where(mocapPosition[:, 0] == 0.0)[0]
-if len(Nlist) > 0:
- N = Nlist[0]
-else:
- N = mocapPosition.shape[0]
-if N == 0:
- N = baseOrientation.shape[0]
-N = baseOrientation.shape[0]
-Tend = N * 0.002
-t = np.linspace(0, Tend, N + 1, endpoint=True)
-t = t[:-1]
-
-# Parameters
-dt = 0.0020
-lwdth = 2
-
-#######
-#######
-
-b_v_mocap = np.zeros((N, 3))
-imuRPY = np.zeros((N, 3))
-vx_ref = np.zeros((N, 1))
-vy_ref = np.zeros((N, 1))
-vx_est = np.zeros((N, 1))
-vy_est = np.zeros((N, 1))
-for i in range(N):
- imuRPY[i, :] = log_q[3:6, i]
-
- c = np.cos(imuRPY[i, 2])
- s = np.sin(imuRPY[i, 2])
- R = np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])
- v = R.transpose() @ log_xfmpc[i:(i + 1), 6:9].transpose()
- # v = pin.Quaternion(baseOrientation[i:(i+1), :].transpose()).toRotationMatrix().transpose() @ log_xfmpc[i:(i+1), 6:9].transpose()
- vx_ref[i] = v[0]
- vy_ref[i] = v[1]
-
- v_est = log_dq[0:3, i:(i + 1)]
- vx_est[i] = v_est[0]
- vy_est[i] = v_est[1]
-
- b_v_mocap[i, :] = (mocapOrientationMat9[i, :, :].transpose() @ mocapVelocity[i:(i + 1), :].transpose()).ravel()
-
-mocapRPY = np.zeros((N, 3))
-for i in range(N):
- mocapRPY[i, :] = pin.rpy.matrixToRpy(mocapOrientationMat9[i, :, :])
-
-plot_forces = False
-
-c = ["royalblue", "forestgreen"]
-lwdth = 1
-velID = 4
-
-# HEIGHT / ROLL / PITCH FIGURE
-fig1 = plt.figure(figsize=(6, 4))
-
-offset_h = np.mean(mocapPosition[S:E, 2]) - np.mean(log_q[2, S:E])
-
-mocapPosition[int(23.43 * 500):int(23.84 * 500), :] = np.nan
-mocapRPY[int(23.43 * 500):int(23.84 * 500), :] = np.nan
-b_v_mocap[int(23.43 * 500):int(23.84 * 500), :] = np.nan
-mocapAngularVelocity[int(23.43 * 500):int(23.84 * 500), :] = np.nan
-
-# Height subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t[S:E], mocapPosition[S:E, 2] - offset_h, color=c[0], linewidth=lwdth)
-plt.plot(t[S:E], log_q[2, S:E], color="darkgreen", linewidth=lwdth)
-plt.plot(t[S:E], log_xfmpc[S:E, 2], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("Height [m]", fontsize=14)
-
-# Roll subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t[S:E], mocapRPY[S:E, 0], color=c[0], linewidth=lwdth)
-plt.plot(t[S:E], log_q[3, S:E], color="darkgreen", linewidth=lwdth)
-plt.plot(t[S:E], log_xfmpc[S:E, 3], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("Roll [rad]", fontsize=14)
-plt.legend(["Ground truth", "Estimated", "Command"], prop={'size': 10}, loc=8)
-
-if plot_forces:
- ax1bis = ax1.twinx()
- ax1bis.set_ylabel("$F_y$ [N]", color='k')
- ax1bis.plot(t[S:E], cheatForce[S:E, 1], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax1bis.tick_params(axis='y', labelcolor='k')
- ax1bis.legend(["Force"], prop={'size': 16}, loc=1)
-
-# Pitch subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t[S:E], mocapRPY[S:E, 1], color=c[0], linewidth=lwdth)
-plt.plot(t[S:E], log_q[4, S:E], color="darkgreen", linewidth=lwdth)
-plt.plot(t[S:E], log_xfmpc[S:E, 4], "darkorange", linewidth=lwdth, linestyle="--")
-plt.xlabel("Time [s]", fontsize=16)
-plt.ylabel("Pitch [rad]", fontsize=14)
-
-if plot_forces:
- ax2bis = ax2.twinx()
- ax2bis.set_ylabel("$F_x$ [N]", color='k')
- ax2bis.plot(t[S:E], cheatForce[S:E, 0], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax2bis.tick_params(axis='y', labelcolor='k')
- ax2bis.legend(["Force"], prop={'size': 6}, loc=1)
-
-for ax in [ax0, ax1, ax2]:
- ax.tick_params(axis='both', which='major', labelsize=10)
- ax.tick_params(axis='both', which='minor', labelsize=10)
-
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/H_R_P_expe.eps",
- dpi=150,
- bbox_inches="tight")
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/H_R_P_expe.png",
- dpi=800,
- bbox_inches="tight")
-
-# VX / VY / WYAW FIGURE
-fig2 = plt.figure(figsize=(15, 4))
-# Forward velocity subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t[S:E], b_v_mocap[S:E, 0], color=c[0], linewidth=lwdth)
-plt.plot(t[S:E], vx_est[S:E], color="darkgreen", linewidth=lwdth)
-plt.plot(t[S:E], vx_ref[S:E], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("$\dot x$ [m/s]", fontsize=14)
-ax0.legend(["Ground truth", "Estimated", "Command"], prop={'size': 12}, loc=2)
-
-if plot_forces:
- ax0bis = ax0.twinx()
- ax0bis.set_ylabel("$F_x$ [N]", color='k')
- ax0bis.plot(t[S:E], cheatForce[S:E, 0], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax0bis.tick_params(axis='y', labelcolor='k')
- ax0bis.legend(["Force"], prop={'size': 16}, loc=1)
-
-# Lateral velocity subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t[S:E], b_v_mocap[S:E, 1], color=c[0], linewidth=lwdth)
-plt.plot(t[S:E], vy_est[S:E], color="darkgreen", linewidth=lwdth)
-plt.plot(t[S:E], vy_ref[S:E], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("$\dot y$ [m/s]", fontsize=14)
-#ax1.legend(["Ground truth", "Estimated", "Command"], prop={'size': 10}, loc=2)
-
-if plot_forces:
- ax1bis = ax1.twinx()
- ax1bis.set_ylabel("$F_y$ [N]", color='k')
- ax1bis.plot(t[S:E], cheatForce[S:E, 1], color="darkviolet", linewidth=lwdth, linestyle="--")
- ax1bis.tick_params(axis='y', labelcolor='k')
- ax1bis.legend(["Force"], prop={'size': 16}, loc=1)
-
-# Angular velocity yaw subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t[S:E], mocapAngularVelocity[S:E, 2], color=c[0], linewidth=lwdth)
-plt.plot(t[S:E], log_dq[5, S:E], color="darkgreen", linewidth=lwdth)
-plt.plot(t[S:E], log_xfmpc[S:E, 11], "darkorange", linewidth=lwdth, linestyle="--")
-plt.ylabel("$\dot \omega_z$ [rad/s]", fontsize=14)
-plt.xlabel("Time [s]", fontsize=16)
-#ax2.legend(["Ground truth", "Estimated", "Command"], prop={'size': 10}, loc=2)
-
-for ax in [ax0, ax1, ax2]:
- ax.tick_params(axis='both', which='major', labelsize=10)
- ax.tick_params(axis='both', which='minor', labelsize=10)
-
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/Vx_Vy_Wyaw_expe.eps",
- dpi=150,
- bbox_inches="tight")
-plt.savefig("/home/palex/Documents/Travail/Article_10_2020/solopython_02_11_2020ter/Figures/Vx_Vy_Wyaw_expe.png",
- dpi=800,
- bbox_inches="tight")
-
-plt.show(block=True)
-
-########
-########
-
-# embed()
-# X_F_MPC
-index = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
-lgd1 = ["Ctct force X", "Ctct force Y", "Ctct force Z"]
-lgd2 = ["FL", "FR", "HL", "HR"]
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index[i])
- else:
- plt.subplot(3, 4, index[i], sharex=ax0)
- plt.plot(log_xfmpc[:, i], "b", linewidth=2)
- # plt.ylabel(lgd[i])
-plt.suptitle("b_xfmpc")
-
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index[i])
- else:
- plt.subplot(3, 4, index[i], sharex=ax0)
-
- h1, = plt.plot(log_xfmpc[:, 12 + i], "b", linewidth=5)
-
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3] + " " + lgd2[int(i / 3)])
-
- if (i % 3) == 2:
- plt.ylim([-1.0, 15.0])
- else:
- plt.ylim([-1.5, 1.5])
-
-plt.suptitle("b_xfmpc forces")
-
-# plt.show(block=True)
-
-###############
-# ORIENTATION #
-###############
-
-mocapRPY = np.zeros((N, 3))
-imuRPY = np.zeros((N, 3))
-for i in range(N):
- mocapRPY[i, :] = pin.rpy.matrixToRpy(mocapOrientationMat9[i, :, :])
- imuRPY[i, :] = pin.rpy.matrixToRpy(pin.Quaternion(baseOrientation[i:(i + 1), :].transpose()).toRotationMatrix())
-
-fig = plt.figure()
-# Roll orientation
-ax0 = plt.subplot(2, 1, 1)
-plt.plot(t, mocapRPY[:N, 0], "darkorange", linewidth=lwdth)
-plt.plot(t, imuRPY[:N, 0], "royalblue", linewidth=lwdth)
-plt.ylabel("Roll")
-plt.legend(["Mocap", "IMU"], prop={'size': 8})
-# Pitch orientation
-ax1 = plt.subplot(2, 1, 2, sharex=ax0)
-plt.plot(t, mocapRPY[:N, 1], "darkorange", linewidth=lwdth)
-plt.plot(t, imuRPY[:N, 1], "royalblue", linewidth=lwdth)
-plt.ylabel("Pitch")
-plt.xlabel("Time [s]")
-
-# embed()
-
-###################
-# LINEAR VELOCITY #
-###################
-mocapBaseLinearVelocity = np.zeros((N, 3))
-imuBaseLinearVelocity = np.zeros((N, 3))
-for i in range(N):
- mocapBaseLinearVelocity[i, :] = (
- (mocapOrientationMat9[i, :, :]) @ (mocapVelocity[i:(i + 1), :]).transpose()).ravel()
- if i == 0:
- imuBaseLinearVelocity[i, :] = mocapBaseLinearVelocity[0, :]
- else:
- imuBaseLinearVelocity[i, :] = imuBaseLinearVelocity[i - 1, :] + dt * baseLinearAcceleration[i - 1, :]
-
-fig = plt.figure()
-# X linear velocity
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, mocapBaseLinearVelocity[:N, 0], "darkorange", linewidth=lwdth)
-plt.plot(t, imuBaseLinearVelocity[:N, 0], "royalblue", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 0], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 0], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot x$ [m/s]")
-plt.legend(["Mocap", "IMU", "Estimator", "Reference"], prop={'size': 8})
-# Y linear velocity
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, mocapBaseLinearVelocity[:N, 1], "darkorange", linewidth=lwdth)
-plt.plot(t, imuBaseLinearVelocity[:N, 1], "royalblue", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 1], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 1], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot y$ [m/s]")
-# Z linear velocity
-ax1 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, mocapBaseLinearVelocity[:N, 2], "darkorange", linewidth=lwdth)
-plt.plot(t, imuBaseLinearVelocity[:N, 2], "royalblue", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 2], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 2], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot z$ [m/s]")
-plt.xlabel("Time [s]")
-
-######################
-# ANGULAR VELOCITIES #
-######################
-mocapBaseAngularVelocity = np.zeros(mocapAngularVelocity.shape)
-for i in range(N):
- #mocapBaseAngularVelocity[i, :] = ((mocapOrientationMat9[i, :, :]) @ (mocapAngularVelocity[i:(i+1), :]).transpose()).ravel()
- mocapBaseAngularVelocity[i, :] = (mocapAngularVelocity[i:(i + 1), :]).transpose().ravel()
-
-fig = plt.figure()
-# Angular velocity X subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, mocapBaseAngularVelocity[:N, 0], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 0], "royalblue", linewidth=lwdth * 2)
-plt.plot(t, referenceVelocity[:N, 3], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \phi$ [rad/s]")
-plt.legend(["Mocap", "IMU", "Reference"], prop={'size': 8})
-# Angular velocity Y subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, mocapBaseAngularVelocity[:N, 1], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 1], "royalblue", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 4], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \\theta$ [rad/s]")
-# Angular velocity Z subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, mocapBaseAngularVelocity[:N, 2], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 2], "royalblue", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 5], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \psi$ [rad/s]")
-plt.xlabel("Time [s]")
-
-#######################
-# LINEAR ACCELERATION #
-#######################
-
-fig = plt.figure()
-# X linear acc
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, baseLinearAcceleration[:N, 0], "royalblue", linewidth=lwdth)
-plt.ylabel("$\ddot x$ [m/s^2]")
-plt.legend(["IMU"], prop={'size': 8})
-# Y linear acc
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, baseLinearAcceleration[:N, 1], "royalblue", linewidth=lwdth)
-plt.ylabel("$\ddot y$ [m/s^2]")
-# Z linear acc
-ax1 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, baseLinearAcceleration[:N, 2], "royalblue", linewidth=lwdth)
-plt.ylabel("$\ddot z$ [m/s^2]")
-plt.xlabel("Time [s]")
-
-####################
-# JOYSTICK CONTROL #
-####################
-
-fig = plt.figure()
-# X linear velocity
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, mocapBaseLinearVelocity[:N, 0], "darkorange", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 0], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 0], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot x$ [m/s]")
-plt.legend(["Mocap", "Estimator", "Reference"], prop={'size': 8})
-# Y linear velocity
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, mocapBaseLinearVelocity[:N, 1], "darkorange", linewidth=lwdth)
-if data['estimatorVelocity'] is not None:
- plt.plot(t, estimatorVelocity[:N, 1], "darkgreen", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 1], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot y$ [m/s]")
-# Z linear velocity
-ax1 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, mocapBaseAngularVelocity[:N, 2], "darkorange", linewidth=lwdth)
-plt.plot(t, baseAngularVelocity[:N, 2], "royalblue", linewidth=lwdth)
-plt.plot(t, referenceVelocity[:N, 5], color="darkviolet", linewidth=lwdth)
-plt.ylabel("$\dot \psi$ [rad/s]")
-plt.xlabel("Time [s]")
-plt.suptitle("Tracking of the velocity command sent to the robot")
-
-#############
-# ACTUATORS #
-#############
-
-index = [1, 5, 2, 6, 3, 7, 4, 8]
-plt.figure()
-for i in range(8):
- if i == 0:
- ax0 = plt.subplot(2, 4, index[i])
- else:
- plt.subplot(2, 4, index[i], sharex=ax0)
- plt.plot(t, torquesFromCurrentMeasurment[:N, i], "forestgreen", linewidth=lwdth)
-
- if (i % 2 == 1):
- plt.xlabel("Time [s]")
- if i <= 1:
- plt.ylabel("Torques [N.m]")
-
-contact_state = np.zeros((N, 4))
-margin = 25
-treshold = 0.4
-for i in range(4):
- state = 0
- front_up = 0
- front_down = 0
- for j in range(N):
- if (state == 0) and (np.abs(torquesFromCurrentMeasurment[j, 2 * i + 1]) >= treshold):
- state = 1
- front_up = j
- if (state == 1) and (np.abs(torquesFromCurrentMeasurment[j, 2 * i + 1]) < treshold):
- state = 0
- front_down = j
- l = np.min((front_up + margin, N))
- u = np.max((front_down - margin, 0))
- contact_state[l:u, i] = 1
-
-plt.figure()
-for i in range(4):
- if i == 0:
- ax0 = plt.subplot(1, 4, i + 1)
- else:
- plt.subplot(1, 4, i + 1, sharex=ax0)
- plt.plot(t, torquesFromCurrentMeasurment[:N, 2 * i + 1])
- plt.plot(t, contact_state[:N, i])
- plt.legend(["Torque", "Estimated contact state"], prop={'size': 8})
-"""fig = plt.figure()
-# Angular velocity X subplot
-ax0 = plt.subplot(3, 1, 1)
-plt.plot(t, torquesFromCurrentMeasurment[:N,
- 0], "forestgreen", linewidth=lwdth)
-plt.ylabel("Torques [N.m]")
-# Angular velocity Y subplot
-ax1 = plt.subplot(3, 1, 2, sharex=ax0)
-plt.plot(t, q_mes[:N, 0], "forestgreen", linewidth=lwdth)
-plt.ylabel("Angular position [rad]")
-# Angular velocity Z subplot
-ax2 = plt.subplot(3, 1, 3, sharex=ax0)
-plt.plot(t, v_mes[:N, 2], "forestgreen", linewidth=lwdth)
-plt.ylabel("Angular velocity [rad/s]")
-plt.xlabel("Time [s]")"""
-
-# Set the paths where the urdf and srdf file of the robot are registered
-if on_solo8:
- urdf = modelPath + "/solo_description/robots/solo.urdf"
-else:
- urdf = modelPath + "/solo_description/robots/solo12.urdf"
-srdf = modelPath + "/solo_description/srdf/solo.srdf"
-vector = pin.StdVec_StdString()
-vector.extend(item for item in modelPath)
-
-# Create the robot wrapper from the urdf model (which has no free flyer) and add a free flyer
-robot = tsid.RobotWrapper(urdf, vector, pin.JointModelFreeFlyer(), False)
-model = robot.model()
-
-# Creation of the Invverse Dynamics HQP problem using the robot
-# accelerations (base + joints) and the contact forces
-invdyn = tsid.InverseDynamicsFormulationAccForce("tsid", robot, False)
-
-# Compute the problem data with a solver based on EiQuadProg
-t0 = 0.0
-if on_solo8:
- q_FK = np.zeros((15, 1))
-else:
- q_FK = np.zeros((19, 1))
-q_FK[:7, 0] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0])
-RPY = quaternionToRPY(baseOrientation.ravel())
-IMU_ang_pos = np.zeros(4)
-IMU_ang_pos[:] = EulerToQuaternion([RPY[0], RPY[1], 0.0])
-q_FK[3:7, 0] = IMU_ang_pos
-
-v_FK = np.zeros((q_FK.shape[0] - 1, 1))
-invdyn.computeProblemData(t0, q_FK, v_FK)
-data = invdyn.data()
-
-# Feet indexes
-if on_solo8:
- indexes = [8, 14, 20, 26] # solo8
-else:
- indexes = [10, 18, 26, 34] # solo12
-alpha = 0.98
-filteredLinearVelocity = np.zeros((N, 3))
-"""for a in range(len(model.frames)):
- print(a)
- print(model.frames[a])"""
-FK_lin_vel_log = np.nan * np.zeros((N, 3))
-iFK_lin_vel_log = np.nan * np.zeros((N, 3))
-rms_x = []
-rms_y = []
-rms_z = []
-irms_x = []
-irms_y = []
-irms_z = []
-alphas = [0.97] # [0.01*i for i in range(100)]
-i_not_nan = np.where(np.logical_not(np.isnan(mocapBaseLinearVelocity[:, 0])))
-i_not_nan = (i_not_nan[0])[(i_not_nan[0] < 9600)]
-for alpha in alphas:
- print(alpha)
- filteredLinearVelocity = np.zeros((N, 3))
- ifilteredLinearVelocity = np.zeros((N, 3))
- for i in range(N):
- # Update estimator FK model
- q_FK[7:, 0] = q_mes[i, :] # Position of actuators
- v_FK[6:, 0] = v_mes[i, :] # Velocity of actuators
-
- pin.forwardKinematics(model, data, q_FK, v_FK)
-
- # Get estimated velocity from updated model
- cpt = 0
- vel_est = np.zeros((3, ))
- ivel_est = np.zeros((3, ))
- for j in (np.where(contactStatus[i, :] == 1))[0]:
- vel_estimated_baseframe, _iv0i = BaseVelocityFromKinAndIMU(indexes[j], model, data,
- baseAngularVelocity[i, :])
-
- cpt += 1
- vel_est += vel_estimated_baseframe[:, 0]
- ivel_est = ivel_est + _iv0i.ravel()
- if cpt > 0:
- FK_lin_vel = vel_est / cpt # average of all feet in contact
- iFK_lin_vel = ivel_est / cpt
-
- filteredLinearVelocity[i, :] = alpha * (filteredLinearVelocity[i - 1, :] +
- baseLinearAcceleration[i, :] * dt) + (1 - alpha) * FK_lin_vel
- FK_lin_vel_log[i, :] = FK_lin_vel
-
- # Get previous base vel wrt world in base frame into IMU frame
- i_filt_lin_vel = ifilteredLinearVelocity[i-1, :] + \
- cross3(_1Mi.translation.ravel(),
- baseAngularVelocity[i, :]).ravel()
-
- # Merge IMU base vel wrt world in IMU frame with FK base vel wrt world in IMU frame
- i_merged_lin_vel = alpha * \
- (i_filt_lin_vel +
- baseLinearAcceleration[i, :] * dt) + (1 - alpha) * iFK_lin_vel.ravel()
- """print("##")
- print(filteredLinearVelocity[i, :])
- print(i_merged_lin_vel)"""
- # Get merged base vel wrt world in IMU frame into base frame
- ifilteredLinearVelocity[i, :] = np.array(
- i_merged_lin_vel + cross3(-_1Mi.translation.ravel(), baseAngularVelocity[i, :]).ravel())
- #print(ifilteredLinearVelocity[i, :])
- """if np.array_equal(filteredLinearVelocity[i, :], ifilteredLinearVelocity[i, :]):
- print("Same values")
-
- else:
- print("Different")
- print(filteredLinearVelocity[i, :])
- print(ifilteredLinearVelocity[i, :])"""
-
- else:
- filteredLinearVelocity[i, :] = filteredLinearVelocity[i - 1, :] + baseLinearAcceleration[i, :] * dt
-
- # Get previous base vel wrt world in base frame into IMU frame
- i_filt_lin_vel = ifilteredLinearVelocity[i-1, :] + \
- cross3(_1Mi.translation.ravel(),
- baseAngularVelocity[i, :]).ravel()
- # Merge IMU base vel wrt world in IMU frame with FK base vel wrt world in IMU frame
- i_merged_lin_vel = i_filt_lin_vel + \
- baseLinearAcceleration[i, :] * dt
- # Get merged base vel wrt world in IMU frame into base frame
- ifilteredLinearVelocity[i, :] = i_merged_lin_vel + \
- cross3(-_1Mi.translation.ravel(),
- baseAngularVelocity[i, :]).ravel()
-
- rms_x.append(
- np.sqrt(np.mean(np.square(filteredLinearVelocity[i_not_nan, 0] - mocapBaseLinearVelocity[i_not_nan, 0]))))
- rms_y.append(
- np.sqrt(np.mean(np.square(filteredLinearVelocity[i_not_nan, 1] - mocapBaseLinearVelocity[i_not_nan, 1]))))
- rms_z.append(
- np.sqrt(np.mean(np.square(filteredLinearVelocity[i_not_nan, 2] - mocapBaseLinearVelocity[i_not_nan, 2]))))
- irms_x.append(
- np.sqrt(np.mean(np.square(ifilteredLinearVelocity[i_not_nan, 0] - mocapBaseLinearVelocity[i_not_nan, 0]))))
- irms_y.append(
- np.sqrt(np.mean(np.square(ifilteredLinearVelocity[i_not_nan, 1] - mocapBaseLinearVelocity[i_not_nan, 1]))))
- irms_z.append(
- np.sqrt(np.mean(np.square(ifilteredLinearVelocity[i_not_nan, 2] - mocapBaseLinearVelocity[i_not_nan, 2]))))
-
-plt.figure()
-plt.plot(alphas, rms_x)
-plt.plot(alphas, rms_y)
-plt.plot(alphas, rms_z)
-plt.plot(alphas, irms_x)
-plt.plot(alphas, irms_y)
-plt.plot(alphas, irms_z)
-plt.legend(["RMS X", "RMS Y", "RMS Z", "New RMS X", "New RMS Y", "New RMS Z"], prop={'size': 8})
-plt.xlabel("Alpha")
-plt.ylabel("RMS erreur en vitesse")
-
-fc = 10
-y = 1 - np.cos(2 * np.pi * fc * dt)
-alpha_v = -y + np.sqrt(y * y + 2 * y)
-lowpass_ifilteredLinearVelocity = np.zeros(ifilteredLinearVelocity.shape)
-lowpass_ifilteredLinearVelocity[0, :] = ifilteredLinearVelocity[0, :]
-for k in range(1, N):
- lowpass_ifilteredLinearVelocity[
- k, :] = (1 - alpha_v) * lowpass_ifilteredLinearVelocity[k - 1, :] + alpha_v * ifilteredLinearVelocity[k, :]
-
-plt.figure()
-plt.plot(t, filteredLinearVelocity[:N, 0], linewidth=3)
-plt.plot(t, ifilteredLinearVelocity[:N, 0], linewidth=3, linestyle="--")
-plt.plot(t, mocapBaseLinearVelocity[:N, 0], linewidth=3)
-plt.plot(t, lowpass_ifilteredLinearVelocity[:N, 0], color="darkviolet", linewidth=3)
-# plt.plot(t, FK_lin_vel_log[:N, 0], color="darkviolet", linestyle="--")
-"""plt.plot(t, baseLinearAcceleration[:N, 0], linestyle="--")"""
-plt.legend(["Filtered", "New Filtered", "Mocap"], prop={'size': 8})
-# plt.show()
-
-data_control = np.load("data_control_" + last_date)
-
-log_tau_ff = data_control['log_tau_ff'] # Position
-log_qdes = data_control['log_qdes'] # Orientation as quat
-log_vdes = data_control['log_vdes'] # as 3 by 3 matrix
-log_q = data_control['log_q']
-
-index12 = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- plt.plot(t, log_qdes[i, :], color='r', linewidth=lwdth)
- plt.plot(t, q_mes[:, i], color='b', linewidth=lwdth)
- plt.legend(["Qdes", "Qmes"], prop={'size': 8})
-
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- plt.plot(t, log_vdes[i, :], color='r', linewidth=lwdth)
- plt.plot(t, v_mes[:, i], color='b', linewidth=lwdth)
- plt.legend(["Vdes", "Vmes"], prop={'size': 8})
-
-# Z linear velocity
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- plt.plot(t, log_tau_ff[i, :], color='r', linewidth=lwdth)
- plt.plot(t, torquesFromCurrentMeasurment[:, i], color='b', linewidth=lwdth)
- plt.legend(["Tau_FF", "TAU"], prop={'size': 8})
-
-plt.show(block=True)
diff --git a/scripts/plot_comparison_fb.py b/scripts/plot_comparison_fb.py
deleted file mode 100644
index 5bf77894..00000000
--- a/scripts/plot_comparison_fb.py
+++ /dev/null
@@ -1,171 +0,0 @@
-
-import numpy as np
-from matplotlib import pyplot as plt
-import pinocchio as pin
-import tsid as tsid
-from IPython import embed
-
-# For storage
-log_feet_pos = []
-log_feet_pos_target = []
-log_feet_vel_target = []
-log_feet_acc_target = []
-log_x_cmd = []
-log_x = []
-log_q = []
-log_dq = []
-log_x_ref_invkin = []
-log_x_invkin = []
-log_dx_ref_invkin = []
-log_dx_invkin = []
-log_tau_ff = []
-log_qdes = []
-log_vdes = []
-log_q_pyb = []
-log_v_pyb = []
-
-files = ["push_no_ff.npz", "push_with_ff.npz", "push_pyb_no_ff.npz", "push_pyb_with_ff.npz"]
-for file in files:
- # Load data file
- data = np.load(file)
- # Store content of data in variables
- log_feet_pos.append(data["log_feet_pos"])
- log_feet_pos_target.append(data["log_feet_pos_target"])
- log_feet_vel_target.append(data["log_feet_vel_target"])
- log_feet_acc_target.append(data["log_feet_acc_target"])
- log_x_cmd.append(data["log_x_cmd"])
- log_x.append(data["log_x"])
- log_q.append(data["log_q"])
- log_dq.append(data["log_dq"])
- log_x_ref_invkin.append(data["log_x_ref_invkin"])
- log_x_invkin.append(data["log_x_invkin"])
- log_dx_ref_invkin.append(data["log_dx_ref_invkin"])
- log_dx_invkin.append(data["log_dx_invkin"])
- log_tau_ff.append(data["log_tau_ff"])
- log_qdes.append(data["log_qdes"])
- log_vdes.append(data["log_vdes"])
- log_q_pyb.append(data["log_q_pyb"])
- log_v_pyb.append(data["log_v_pyb"])
-
-# Creating time vector
-N = log_tau_ff[0].shape[1]
-Tend = N * 0.001
-t = np.linspace(0, Tend, N+1, endpoint=True)
-t_range = t[:-1]
-
-# Parameters
-dt = 0.0020
-lwdth = 2
-
-##########
-# GRAPHS #
-##########
-
-k_lgd = ["No FF"]
-index6 = [1, 3, 5, 2, 4, 6]
-index12 = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
-
-lgd_X = ["FL", "FR", "HL", "HR"]
-lgd_Y = ["Pos X", "Pos Y", "Pos Z"]
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- for k in range(len(log_tau_ff)):
- plt.plot(t_range, log_feet_pos[k][i % 3, np.int(i/3), :], color='b', linewidth=3, marker='')
- plt.plot(t_range, log_feet_pos_target[k][i % 3, np.int(i/3), :], color='r', linewidth=3, marker='')
- plt.legend([lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+"", lgd_Y[i %
- 3] + " " + lgd_X[np.int(i/3)]+" Ref"])
-plt.suptitle("Reference positions of feet (world frame)")
-
-lgd_X = ["FL", "FR", "HL", "HR"]
-lgd_Y = ["Vel X", "Vel Y", "Vel Z"]
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- for k in range(len(log_tau_ff)):
- plt.plot(t_range, log_feet_vel[k][i % 3, np.int(i/3), :], color='b', linewidth=3, marker='')
- plt.plot(t_range, log_feet_vel_target[k][i % 3, np.int(i/3), :], color='r', linewidth=3, marker='')
- plt.legend([lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+" Ref"])
-plt.suptitle("Current and reference velocities of feet (world frame)")
-
-lgd_X = ["FL", "FR", "HL", "HR"]
-lgd_Y = ["Acc X", "Acc Y", "Acc Z"]
-plt.figure()
-for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- for k in range(len(log_tau_ff)):
- plt.plot(t_range, log_feet_acc_target[k][i % 3, np.int(i/3), :], color='r', linewidth=3, marker='')
- plt.legend([lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+" Ref"])
-plt.suptitle("Current and reference accelerations of feet (world frame)")
-
-# LOG_Q
-lgd = ["Position X", "Position Y", "Position Z", "Position Roll", "Position Pitch", "Position Yaw"]
-plt.figure()
-for i in range(6):
- if i == 0:
- ax0 = plt.subplot(3, 2, index6[i])
- else:
- plt.subplot(3, 2, index6[i], sharex=ax0)
- for k in range(len(log_tau_ff)):
- plt.plot(t_range[:-2], log_x[k][i, :-2], "b", linewidth=2)
- plt.plot(t_range[:-2], log_x_cmd[k][i, :-2], "r", linewidth=3)
- # plt.plot(t_range, log_q[i, :], "g", linewidth=2)
- plt.plot(t_range[:-2], log_x_invkin[k][i, :-2], "g", linewidth=2)
- plt.plot(t_range[:-2], log_x_ref_invkin[k][i, :-2], "violet", linewidth=2, linestyle="--")
- plt.legend(["WBC integrated output state", "Robot reference state",
- "Task current state", "Task reference state"])
- plt.ylabel(lgd[i])
-
-# LOG_V
-lgd = ["Linear vel X", "Linear vel Y", "Linear vel Z",
- "Angular vel Roll", "Angular vel Pitch", "Angular vel Yaw"]
-plt.figure()
-for i in range(6):
- if i == 0:
- ax0 = plt.subplot(3, 2, index6[i])
- else:
- plt.subplot(3, 2, index6[i], sharex=ax0)
- for k in range(len(log_tau_ff)):
- plt.plot(t_range[:-2], log_x[k][i+6, :-2], "b", linewidth=2)
- plt.plot(t_range[:-2], log_x_cmd[k][i+6, :-2], "r", linewidth=3)
- # plt.plot(t_range, log_dq[i, :], "g", linewidth=2)
- plt.plot(t_range[:-2], log_dx_invkin[k][i, :-2], "g", linewidth=2)
- plt.plot(t_range[:-2], log_dx_ref_invkin[k][i, :-2], "violet", linewidth=2, linestyle="--")
- plt.legend(["WBC integrated output state", "Robot reference state",
- "Task current state", "Task reference state"])
- plt.ylabel(lgd[i])
-
-plt.figure()
-for k in range(len(log_tau_ff)):
- plt.plot(t_range[:-2], log_x[k][6, :-2], "b", linewidth=2)
- plt.plot(t_range[:-2], log_x_cmd[k][6, :-2], "r", linewidth=2)
- plt.plot(t_range[:-2], log_dx_invkin[k][0, :-2], "g", linewidth=2)
- plt.plot(t_range[:-2], log_dx_ref_invkin[k][0, :-2], "violet", linewidth=2)
-plt.legend(["WBC integrated output state", "Robot reference state",
- "Task current state", "Task reference state"])
-
-k_c = ["r", "g", "b", "violet"]
-plt.figure()
-plt.plot(t_range[:-2], log_x_cmd[0][6, :-2], "k", linewidth=2)
-for k in range(len(log_tau_ff)):
- plt.plot(t_range[:-2], log_v_pyb[k][1, :-2], color=k_c[k], linewidth=2) # (1+len(log_tau_ff))-k)
-plt.legend(["Reference", "Inv Kin FB no FF", "Inv Kin FB with FF", "PyB FB no FF", "PyB FB with FF"])
-
-k_c = ["r", "g", "b", "violet"]
-plt.figure()
-plt.plot(t_range[:-2], log_x_cmd[0][1, :-2], "k", linewidth=2)
-for k in range(len(log_tau_ff)):
- plt.plot(t_range[:-2], log_q_pyb[k][1, :-2], color=k_c[k], linewidth=2) # (1+len(log_tau_ff))-k)
-plt.xlabel("Time [s]")
-plt.ylabel("Position [m]")
-plt.legend(["Reference", "Inv Kin FB no FF", "Inv Kin FB with FF", "PyB FB no FF", "PyB FB with FF"])
-plt.show(block=True)
diff --git a/scripts/solo3D/SurfacePlanner.py b/scripts/solo3D/SurfacePlanner.py
index bdb87adf..9e7d8bf3 100644
--- a/scripts/solo3D/SurfacePlanner.py
+++ b/scripts/solo3D/SurfacePlanner.py
@@ -10,7 +10,7 @@ from solo_rbprm.solo_abstract import Robot as SoloAbstract
from hpp.corbaserver.affordance.affordance import AffordanceTool
from hpp.corbaserver.rbprm.tools.surfaces_from_path import getAllSurfacesDict
-from solo3D.tools.utils import getAllSurfacesDict_inner
+from solo3D.utils import getAllSurfacesDict_inner
from hpp.corbaserver.problem_solver import ProblemSolver
from hpp.gepetto import ViewerFactory
diff --git a/scripts/solo3D/SurfacePlannerWrapper.py b/scripts/solo3D/SurfacePlannerWrapper.py
index a0d3c5cb..14dd62ba 100644
--- a/scripts/solo3D/SurfacePlannerWrapper.py
+++ b/scripts/solo3D/SurfacePlannerWrapper.py
@@ -15,7 +15,6 @@ N_SURFACE_MAX = 10
N_POTENTIAL_SURFACE = 7
N_FEET = 4
-
class SurfaceDataCtype(ctypes.Structure):
''' ctype data structure for the shared memory between processes, for surfaces
Ax <= b
diff --git a/scripts/heightmap_generator.py b/scripts/solo3D/heightmap_generator.py
similarity index 97%
rename from scripts/heightmap_generator.py
rename to scripts/solo3D/heightmap_generator.py
index 3931128d..3e49a4f4 100644
--- a/scripts/heightmap_generator.py
+++ b/scripts/solo3D/heightmap_generator.py
@@ -2,8 +2,8 @@ import matplotlib.pyplot as plt
import os
import numpy as np
-from solo3D.tools.heightmap_tools import Heightmap
-from solo3D.tools.utils import getAllSurfacesDict_inner
+from solo3D.heightmap_tools import Heightmap
+from solo3D.utils import getAllSurfacesDict_inner
from solo_rbprm.solo_abstract import Robot
diff --git a/scripts/solo3D/tools/heightmap_tools.py b/scripts/solo3D/heightmap_tools.py
similarity index 99%
rename from scripts/solo3D/tools/heightmap_tools.py
rename to scripts/solo3D/heightmap_tools.py
index bb1f3593..3f715206 100644
--- a/scripts/solo3D/tools/heightmap_tools.py
+++ b/scripts/solo3D/heightmap_tools.py
@@ -2,8 +2,6 @@ import numpy as np
import hppfcl
import pickle
import ctypes
-import quadruped_reactive_walking as qrw
-
COLORS = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
diff --git a/scripts/solo3D/tools/pyb_environment_3D.py b/scripts/solo3D/pyb_environment_3D.py
similarity index 100%
rename from scripts/solo3D/tools/pyb_environment_3D.py
rename to scripts/solo3D/pyb_environment_3D.py
diff --git a/scripts/solo3D/tools/utils.py b/scripts/solo3D/utils.py
similarity index 100%
rename from scripts/solo3D/tools/utils.py
rename to scripts/solo3D/utils.py
diff --git a/scripts/test_convergence_mpc.py b/scripts/test_convergence_mpc.py
deleted file mode 100644
index a4c8d515..00000000
--- a/scripts/test_convergence_mpc.py
+++ /dev/null
@@ -1,563 +0,0 @@
-import numpy as np
-from matplotlib import pyplot as plt
-import quadruped_reactive_walking as qrw
-import MPC_Wrapper
-import utils_mpc
-
-"""dt_mpc = 0.02
-T_mpc = 0.32
-
-k_mpc = 20
-N_gait = 100"""
-h_ref = 0.2447495
-
-def test_1():
- # Position of base in world frame
- q = np.zeros((18, 1))
- q[0:6, 0] = np.array([0.0, 0.0, h_ref, 0.0, 0.0, 0.0])
- q[6:, 0] = np.array([0.0, 0.7, -1.4, -0.0, 0.7, -1.4, 0.0, -0.7, +1.4, -0.0, -0.7, +1.4])
-
- # Velocity of base in horizontal frame
- h_v = np.zeros((18, 1))
- h_v[0, 0] = 0.1
- h_v[1, 0] = 0.1
- #h_v[1, 0] = 0.1
-
- # Velocity reference of base in horizontal frame
- v_ref = np.zeros((18, 1))
- v_ref[0, 0] = 0.1
-
- # Params
- params = qrw.Params() # Object that holds all controller parameters
-
- """params.dt_mpc = dt_mpc
- params.T_mpc = T_mpc
- params.h_ref = h_ref
- params.enable_multiprocessing = False"""
-
- # Initialisation of the solo model/data and of the Gepetto viewer
- utils_mpc.init_robot(q[6:, 0], params)
-
- # State planner
- statePlanner = qrw.StatePlanner()
- statePlanner.initialize(params)
-
- gait = qrw.Gait()
- gait.initialize(params)
-
- footstepPlanner = qrw.FootstepPlanner()
- footstepPlanner.initialize(params, gait)
-
- # MPC wrapper
- params.type_MPC = 0
- mpc_wrapper_classic = MPC_Wrapper.MPC_Wrapper(params, q)
- params.type_MPC = 1
- mpc_wrapper_croco = MPC_Wrapper.MPC_Wrapper(params, q)
-
- # Update gait
- for i in range(5):
- gait.updateGait(i*20, 20, 0)
- cgait = gait.getCurrentGait()
-
- # Compute target footstep based on current and reference velocities
- footstepPlanner.updateFootsteps(False, 20, q[:, 0:1], h_v[0:6, 0:1].copy(), v_ref[0:6, 0])
- fsteps = footstepPlanner.getFootsteps()
-
- fsteps[:12, 0] += h_v[0, 0] * params.T_gait * 0.25
- fsteps[:12, 1] += h_v[1, 0] * params.T_gait * 0.25
- fsteps[:12, 9] += h_v[0, 0] * params.T_gait * 0.25
- fsteps[:12, 10] += h_v[1, 0] * params.T_gait * 0.25
-
- """from IPython import embed
- embed()"""
-
-
- # Run state planner (outputs the reference trajectory of the base)
- statePlanner.computeReferenceStates(q[0:6, 0:1].copy(), h_v[0:6, 0:1].copy(), v_ref[0:6, 0:1].copy())
- xref = statePlanner.getReferenceStates()
-
- # Create fsteps matrix
- shoulders = np.zeros((3, 4))
- shoulders[0, :] = [0.1946, 0.1946, -0.1946, -0.1946]
- shoulders[1, :] = [0.14695, -0.14695, 0.14695, -0.14695]
- shoulders[2, :] = np.zeros(4)
-
- # Solve MPC problem once every k_mpc iterations of the main loop
- mpc_wrapper_classic.solve(0, xref.copy(), fsteps.copy(), cgait.copy(), np.zeros((3,4)))
- mpc_wrapper_croco.solve(0, xref.copy(), fsteps.copy(), cgait.copy(), shoulders.copy())
-
- # Retrieve reference contact forces in horizontal frame
- x_f_mpc_classic = mpc_wrapper_classic.get_latest_result()
- x_f_mpc_croco = mpc_wrapper_croco.get_latest_result()
-
- # Solve MPC problem once every k_mpc iterations of the main loop
- mpc_wrapper_classic.solve(1, xref, fsteps, cgait, np.zeros((3,4)))
- mpc_wrapper_croco.solve(1, xref, fsteps, cgait, shoulders)
-
- # Retrieve reference contact forces in horizontal frame
- x_f_mpc_classic = mpc_wrapper_classic.get_latest_result()
- x_f_mpc_croco_32 = mpc_wrapper_croco.get_latest_result()
-
- x_f_mpc_classic = np.hstack((np.vstack((xref[:, 0:1], np.zeros((12, 1)))), x_f_mpc_classic))
- x_f_mpc_croco = np.hstack((np.vstack((xref[:, 0:1], np.zeros((12, 1)))), x_f_mpc_croco_32[:24, :]))
-
- # print(xref)
-
- index6 = [1, 3, 5, 2, 4, 6]
- index12 = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- titles = ["X", "Y", "Z", "Roll", "Pitch", "Yaw"]
- N = x_f_mpc_classic.shape[1]
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_classic[i, :N], "b", linewidth=2)
- h2, = plt.plot(x_f_mpc_croco[i, :N], "r", linewidth=2)
- h3, = plt.plot(xref[i, :N], "b", linestyle="--", marker='x', color="g", linewidth=2)
- plt.xlabel("Time [s]")
- # plt.legend([h1, h2, h3], ["Output trajectory of classic MPC", "Output trajectory of croco MPC", "Input trajectory of planner"])
- plt.legend([h1, h2, h3], ["OSQP", "Croco", "Ref"])
- if i in [0, 1]:
- m1 = np.min(x_f_mpc_classic[0:2, :N])
- M1 = np.max(x_f_mpc_classic[0:2, :N])
- m2 = np.min(x_f_mpc_croco[0:2, :N])
- M2 = np.max(x_f_mpc_croco[0:2, :N])
- plt.ylim([np.min([m1, m2]) - 0.01, np.max([M1, M2]) + 0.01])
- elif i in [3, 4]:
- m1 = np.min(x_f_mpc_classic[3:5, :N])
- M1 = np.max(x_f_mpc_classic[3:5, :N])
- m2 = np.min(x_f_mpc_croco[3:5, :N])
- M2 = np.max(x_f_mpc_croco[3:5, :N])
- plt.ylim([np.min([m1, m2]) - 0.01, np.max([M1, M2]) + 0.01])
- plt.title("Predicted trajectory for " + titles[i])
- plt.suptitle("Analysis of trajectories in position and orientation computed by the MPC")
-
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_classic[i+6, :N], "b", linewidth=2)
- h2, = plt.plot(x_f_mpc_croco[i+6, :N], "r", linewidth=2)
- h3, = plt.plot(xref[i+6, :N], "b", linestyle="--", marker='x', color="g", linewidth=2)
- plt.xlabel("Time [s]")
- if i in [0, 1]:
- m1 = np.min(x_f_mpc_classic[6:8, :N])
- M1 = np.max(x_f_mpc_classic[6:8, :N])
- m2 = np.min(x_f_mpc_croco[6:8, :N])
- M2 = np.max(x_f_mpc_croco[6:8, :N])
- plt.ylim([np.min([m1, m2]) - 0.01, np.max([M1, M2]) + 0.01])
- elif i in [3, 4]:
- m1 = np.min(x_f_mpc_classic[9:11, :N])
- M1 = np.max(x_f_mpc_classic[9:11, :N])
- m2 = np.min(x_f_mpc_croco[9:11, :N])
- M2 = np.max(x_f_mpc_croco[9:11, :N])
- plt.ylim([np.min([m1, m2]) - 0.01, np.max([M1, M2]) + 0.01])
- # plt.legend([h1, h2, h3], ["Output trajectory of classic MPC", "Output trajectory of croco MPC", "Input trajectory of planner"])
- plt.title("Predicted trajectory for velocity in " + titles[i])
- plt.suptitle("Analysis of trajectories of linear and angular velocities computed by the MPC")
-
- lgd1 = ["Ctct force X", "Ctct force Y", "Ctct force Z"]
- lgd2 = ["FL", "FR", "HL", "HR"]
- plt.figure()
- for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- h1, = plt.plot(x_f_mpc_classic[i+12, 1:(N+1)], "b", linewidth=3)
- h2, = plt.plot(x_f_mpc_croco[i+12, 1:(N+1)], "r", linewidth=3)
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [N]")
- """if (i % 3) == 2:
- plt.ylim([-0.0, 26.0])
- else:
- plt.ylim([-26.0, 26.0])"""
-
- if i % 3 != 2:
- row = int(int(i/3)*3+14)
- plt.plot(0.9 * np.abs(x_f_mpc_croco[row, 1:(N+1)]), "b--", linewidth=3)
- plt.plot(-0.9 * np.abs(x_f_mpc_croco[row, 1:(N+1)]), "b--", linewidth=3)
- plt.plot(0.9 * np.abs(x_f_mpc_croco[row, 1:(N+1)]), "r--", linewidth=3)
- plt.plot(-0.9 * np.abs(x_f_mpc_croco[row, 1:(N+1)]), "r--", linewidth=3)
-
- if i % 3 != 2:
- idx = np.array([0, 1, 3, 4, 6, 7, 9, 10]) + 12
- else:
- idx = np.array([2, 5, 8, 11]) + 12
- m1 = np.min(x_f_mpc_classic[idx, :N])
- M1 = np.max(x_f_mpc_classic[idx, :N])
- m2 = np.min(x_f_mpc_croco[idx, :N])
- M2 = np.max(x_f_mpc_croco[idx, :N])
- plt.ylim([np.min([m1, m2]) - 0.2, np.max([M1, M2]) + 0.2])
-
- plt.legend([h1, h2], ["Classic", "Croco"])
- plt.suptitle("Contact forces (MPC command)")
-
- """index8 = [1, 5, 2, 6, 3, 7, 4, 8]
- lgd_x = ["FL", "FR", "HL", "HR"]
- lgd_y = [" x", " y"]
- plt.figure()
- for i in range(8):
- plt.subplot(2, 4, index8[i])
- plt.plot(shoulders[int(i % 2), int(i/2)] * np.ones(x_f_mpc_croco_32.shape[1]), "b", linewidth=3)
- plt.plot(x_f_mpc_croco_32[24+i, :], "r", linewidth=3)
- plt.legend([lgd_x[int(i/2)] + lgd_y[i % 2] + " classic", lgd_x[int(i/2)] + lgd_y[i % 2] + " croco"])
- plt.suptitle("Footsteps positions")"""
-
- plt.show(block=True)
-
-
-def test_2():
- # Position of base in world frame
- q = np.zeros((19, 1))
- q[0:7, 0] = np.array([0.0, 0.0, h_ref, 0.0, 0.0, 0.0, 1.0])
- q[7:, 0] = np.array([0.0, 0.7, -1.4, -0.0, 0.7, -1.4, 0.0, -0.7, +1.4, -0.0, -0.7, +1.4])
-
- # Velocity of base in horizontal frame
- h_v_1 = np.zeros((18, 1))
- h_v_2 = np.zeros((18, 1))
- h_v_1[0, 0] = 0.1
- h_v_1[1, 0] = 0.0
- h_v_2[0, 0] = - h_v_1[0, 0]
- h_v_2[1, 0] = - h_v_1[1, 0]
-
- # Velocity reference of base in horizontal frame
- v_ref_1 = np.zeros((18, 1))
- v_ref_2 = np.zeros((18, 1))
- v_ref_1[0, 0] = 0.1
- v_ref_2[0, 0] = - v_ref_1[0, 0]
-
- # Params
- params = qrw.Params() # Object that holds all controller parameters
-
- # Initialisation of the solo model/data and of the Gepetto viewer
- utils_mpc.init_robot(q[7:, 0], params)
-
- # State planner
- statePlanner_1 = qrw.StatePlanner()
- statePlanner_1.initialize(params)
- statePlanner_2 = qrw.StatePlanner()
- statePlanner_2.initialize(params)
-
- gait = qrw.Gait()
- gait.initialize(params)
-
- footstepPlanner_1 = qrw.FootstepPlanner()
- footstepPlanner_1.initialize(params, gait)
- footstepPlanner_2 = qrw.FootstepPlanner()
- footstepPlanner_2.initialize(params, gait)
-
- # MPC wrapper
- enable_multiprocessing = False
- mpc_wrapper_1 = MPC_Wrapper.MPC_Wrapper(params, q)
- mpc_wrapper_2 = MPC_Wrapper.MPC_Wrapper(params, q)
-
- # Update gait
- gait.updateGait(0, 20, q[0:7, 0:1], 0)
-
- # Compute target footstep based on current and reference velocities
- footstepPlanner_1.updateFootsteps(False, 20, q[:, 0:1], h_v_1[0:6, 0:1].copy(), v_ref_1[0:6, 0])
- footstepPlanner_2.updateFootsteps(False, 20, q[:, 0:1], h_v_2[0:6, 0:1].copy(), v_ref_2[0:6, 0])
-
- # Run state planner (outputs the reference trajectory of the base)
- statePlanner_1.computeReferenceStates(q[0:7, 0:1].copy(), h_v_1[0:6, 0:1].copy(), v_ref_1[0:6, 0:1].copy())
- xref_1 = statePlanner_1.getReferenceStates()
- statePlanner_2.computeReferenceStates(q[0:7, 0:1].copy(), h_v_2[0:6, 0:1].copy(), v_ref_2[0:6, 0:1].copy())
- xref_2 = statePlanner_2.getReferenceStates()
-
- fsteps_1 = footstepPlanner_1.getFootsteps()
- fsteps_2 = footstepPlanner_2.getFootsteps()
- cgait = gait.getCurrentGait()
-
- xref_test = xref_2.copy()
- xref_test[0, :] *= -1.0
- xref_test[1, :] *= -1.0
- xref_test[5, :] *= -1.0
- xref_test[6, :] *= -1.0
- xref_test[7, :] *= -1.0
- xref_test[11, :] *= -1.0
- print(np.allclose(xref_1, xref_test))
- # print(xref_1 - xref_test)
-
- # Create gait matrix
- """cgait = np.zeros((params.N_gait, 4))
- for i in range(7):
- cgait[i, :] = np.array([1.0, 0.0, 0.0, 1.0])
- for i in range(8):
- cgait[7+i, :] = np.array([0.0, 1.0, 1.0, 0.0])
- cgait[15, :] = np.array([1.0, 0.0, 0.0, 1.0])
- for j in range(1, 10):
- for i in range(7):
- cgait[16*j+i, :] = np.array([1.0, 0.0, 0.0, 1.0])
- for i in range(8):
- cgait[16*j+7+i, :] = np.array([0.0, 1.0, 1.0, 0.0])
- cgait[16*j+15, :] = np.array([1.0, 0.0, 0.0, 1.0])"""
-
- # Create fsteps matrix
- """shoulders = np.zeros((3, 4))
- shoulders[0, :] = [0.1946, 0.1946, -0.1946, -0.1946]
- shoulders[1, :] = [0.14695, -0.14695, 0.14695, -0.14695]
- shoulders[2, :] = np.zeros(4) # np.ones(4) * h_ref
- fsteps = np.zeros((params.N_gait, 12))
- for i in range(params.N_gait):
- for j in range(4):
- if cgait[i, j] == 1:
- fsteps[i, (3*j):(3*(j+1))] = shoulders[:, j]"""
-
- from IPython import embed
- embed()
-
- # Solve MPC problem once every k_mpc iterations of the main loop
- try:
- mpc_wrapper_1.solve(0, xref_1.copy(), fsteps_1.copy(), cgait.copy(), np.zeros((3, 4)))
- mpc_wrapper_2.solve(0, xref_2.copy(), fsteps_2.copy(), cgait.copy(), np.zeros((3, 4)))
- except ValueError:
- print("MPC Problem")
-
- # Retrieve reference contact forces in horizontal frame
- x_f_mpc_1 = mpc_wrapper_1.get_latest_result()
- x_f_mpc_2 = mpc_wrapper_2.get_latest_result()
-
- """from IPython import embed
- embed()"""
-
- # Solve MPC problem once every k_mpc iterations of the main loop
- try:
- mpc_wrapper_1.solve(1, xref_1.copy(), fsteps_1.copy(), cgait.copy(), np.zeros((3, 4)))
- mpc_wrapper_2.solve(1, xref_2.copy(), fsteps_2.copy(), cgait.copy(), np.zeros((3, 4)))
- except ValueError:
- print("MPC Problem")
-
- # Retrieve reference contact forces in horizontal frame
- x_f_mpc_1 = mpc_wrapper_1.get_latest_result()
- x_f_mpc_2 = mpc_wrapper_2.get_latest_result()
-
- """from IPython import embed
- embed()"""
-
- x_f_mpc_1 = np.hstack((np.vstack((xref_1[:, 0:1].copy(), np.zeros((12, 1)))), x_f_mpc_1.copy()))
- x_f_mpc_2 = np.hstack((np.vstack((xref_2[:, 0:1].copy(), np.zeros((12, 1)))), x_f_mpc_2.copy()))
-
- index6 = [1, 3, 5, 2, 4, 6]
- index12 = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- coeff = [-1.0, -1.0, 1.0, -1.0, -1.0, -1.0]
- titles = ["X", "Y", "Z", "Roll", "Pitch", "Yaw"]
- N = x_f_mpc_1.shape[1]
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_1[i, :N], "b", linewidth=2)
- h2, = plt.plot(coeff[i] * x_f_mpc_2[i, :N], "r", linewidth=2)
- h3, = plt.plot(xref_1[i, :N], "forestgreen", linestyle="--", marker='x', color="g", linewidth=2)
- plt.xlabel("Time [s]")
- # plt.legend([h1, h2, h3], ["Output trajectory of classic MPC", "Output trajectory of croco MPC", "Input trajectory of planner"])
- plt.title("Predicted trajectory for " + titles[i])
- plt.suptitle("Analysis of trajectories in position and orientation computed by the MPC")
-
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_1[i+6, :N], "b", linewidth=2)
- h2, = plt.plot(coeff[i] * x_f_mpc_2[i+6, :N], "r", linewidth=2)
- h3, = plt.plot(xref_1[i+6, :N], "forestgreen", linestyle="--", marker='x', color="g", linewidth=2)
- plt.xlabel("Time [s]")
- #plt.legend([h1, h2, h3], ["Output trajectory of classic MPC", "Output trajectory of croco MPC", "Input trajectory of planner"])
- plt.title("Predicted trajectory for velocity in " + titles[i])
- plt.suptitle("Analysis of trajectories of linear and angular velocities computed by the MPC")
-
- lgd1 = ["Ctct force X", "Ctct force Y", "Ctct force Z"]
- lgd2 = ["FL", "FR", "HL", "HR"]
- reorder = [9, 10, 11, 6, 7, 8, 3, 4, 5, 0, 1, 2]
- plt.figure()
- for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- h1, = plt.plot(x_f_mpc_1[i+12, 1:(N+1)], "b", linewidth=3)
- h2, = plt.plot(coeff[i % 3] * x_f_mpc_2[reorder[i]+12, 1:(N+1)], "r", linewidth=3)
- # h3, = plt.plot(x_f_mpc_1[i+12, 1:(N+1)] - coeff[i % 3] * x_f_mpc_2[reorder[i]+12, 1:(N+1)], "g", linewidth=3)
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [N]")
- """if (i % 3) == 2:
- plt.ylim([-0.0, 26.0])
- else:
- plt.ylim([-26.0, 26.0])"""
- #plt.legend([h1, h2], ["Classic", "Croco"])
- plt.suptitle("Contact forces (MPC command)")
-
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_1[i, :N] - coeff[i] * x_f_mpc_2[i, :N], "g", linewidth=2)
- plt.xlabel("Time [s]")
- plt.suptitle("Analysis of trajectories in position and orientation computed by the MPC")
-
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_1[i+6, :N] - coeff[i] * x_f_mpc_2[i+6, :N], "g", linewidth=2)
- plt.xlabel("Time [s]")
- plt.suptitle("Analysis of trajectories of linear and angular velocities computed by the MPC")
-
- plt.figure()
- for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- h3, = plt.plot(x_f_mpc_1[i+12, 1:(N+1)] - coeff[i % 3] * x_f_mpc_2[reorder[i]+12, 1:(N+1)], "g", linewidth=3)
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [N]")
- plt.suptitle("Contact forces (MPC command)")
-
- plt.figure()
- off = [9, 9, 9, 3, 3, 3]
- for i in range(6):
- if i == 0:
- ax0 = plt.subplot(3, 2, index6[i])
- else:
- plt.subplot(3, 2, index6[i], sharex=ax0)
- plt.plot(x_f_mpc_1[i+12, 1:(N+1)] - coeff[i % 3] * x_f_mpc_2[reorder[i]+12, 1:(N+1)] + (x_f_mpc_1[i+off[i]+12, 1:(N+1)] - coeff[i % 3] * x_f_mpc_2[reorder[i+off[i]]+12, 1:(N+1)]), "g", linewidth=3)
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [N]")
- plt.suptitle("Contact forces (MPC command)")
-
- plt.show(block=True)
-
-def test_3(typeMPC=True):
- # Position of base in world frame
- q = np.zeros((19, 1))
- q[0:7, 0] = np.array([0.0, 0.0, h_ref, 0.0, 0.0, 0.0, 1.0])
- q[7:, 0] = np.array([0.0, 0.7, -1.4, -0.0, 0.7, -1.4, 0.0, -0.7, +1.4, -0.0, -0.7, +1.4])
-
- # Velocity of base in horizontal frame
- h_v_1 = np.zeros((18, 1))
- h_v_2 = np.zeros((18, 1))
- h_v_1[0, 0] = 0.1
- h_v_2[0, 0] = - h_v_1[0, 0]
-
- # Velocity reference of base in horizontal frame
- v_ref_1 = np.zeros((18, 1))
- v_ref_2 = np.zeros((18, 1))
- v_ref_1[0, 0] = 0.1
- v_ref_2[0, 0] = - v_ref_1[0, 0]
-
- # Params
- params = qrw.Params() # Object that holds all controller parameters
- params.dt_mpc = dt_mpc
- params.T_mpc = T_mpc
- params.h_ref = h_ref
- params.enable_multiprocessing = False
-
- # State planner
- statePlanner_1 = qrw.StatePlanner()
- statePlanner_1.initialize(params)
- statePlanner_2 = qrw.StatePlanner()
- statePlanner_2.initialize(params)
-
- # MPC wrapper
- mpc_wrapper_1 = MPC_Wrapper.MPC_Wrapper(params, q)
- mpc_wrapper_2 = MPC_Wrapper.MPC_Wrapper(params, q)
-
- # Run state planner (outputs the reference trajectory of the base)
- statePlanner_1.computeReferenceStates(q[0:7, 0:1].copy(), h_v_1[0:6, 0:1].copy(), v_ref_1[0:6, 0:1].copy())
- xref_1 = statePlanner_1.getReferenceStates()
- statePlanner_2.computeReferenceStates(q[0:7, 0:1].copy(), h_v_2[0:6, 0:1].copy(), v_ref_2[0:6, 0:1].copy())
- xref_2 = statePlanner_2.getReferenceStates()
-
- # Create gait matrix
- cgait = np.zeros((N_gait, 4))
- for i in range(7):
- cgait[i, :] = np.array([1.0, 0.0, 0.0, 1.0])
- for i in range(8):
- cgait[7+i, :] = np.array([0.0, 1.0, 1.0, 0.0])
- cgait[15, :] = np.array([1.0, 0.0, 0.0, 1.0])
-
- # Create fsteps matrix
- shoulders = np.zeros((3, 4))
- shoulders[0, :] = [0.1946, 0.1946, -0.1946, -0.1946]
- shoulders[1, :] = [0.14695, -0.14695, 0.14695, -0.14695]
- shoulders[2, :] = np.zeros(4) # np.ones(4) * h_ref
- fsteps = np.zeros((N_gait, 12))
- for i in range(N_gait):
- for j in range(4):
- if cgait[i, j] == 1:
- fsteps[i, (3*j):(3*(j+1))] = shoulders[:, j]
-
-
- # Solve MPC problem once every k_mpc iterations of the main loop
- try:
- mpc_wrapper_1.solve(0, xref_1.copy(), fsteps.copy(), cgait.copy(), np.zeros((3,4)))
- mpc_wrapper_2.solve(0, xref_2.copy(), fsteps.copy(), cgait.copy(), np.zeros((3,4)))
- except ValueError:
- print("MPC Problem")
-
- # Retrieve reference contact forces in horizontal frame
- x_f_mpc_1 = mpc_wrapper_1.get_latest_result()
- x_f_mpc_2 = mpc_wrapper_2.get_latest_result()
-
- # Solve MPC problem once every k_mpc iterations of the main loop
- try:
- mpc_wrapper_1.solve(0, xref_1.copy(), fsteps.copy(), cgait.copy(), np.zeros((3,4)))
- mpc_wrapper_2.solve(0, xref_2.copy(), fsteps.copy(), cgait.copy(), np.zeros((3,4)))
- except ValueError:
- print("MPC Problem")
-
- # Retrieve reference contact forces in horizontal frame
- x_f_mpc_1 = mpc_wrapper_1.get_latest_result()
- x_f_mpc_2 = mpc_wrapper_2.get_latest_result()
- x_f_mpc_1 = np.hstack((np.vstack((xref_1[:, 0:1].copy(), np.zeros((12, 1)))), x_f_mpc_1.copy()))
- x_f_mpc_2 = np.hstack((np.vstack((xref_2[:, 0:1].copy(), np.zeros((12, 1)))), x_f_mpc_2.copy()))
-
- index6 = [1, 3, 5, 2, 4, 6]
- index12 = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
- coeff = [-1.0, -1.0, 1.0, -1.0, -1.0, -1.0]
- titles = ["X", "Y", "Z", "Roll", "Pitch", "Yaw"]
- N = -1
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_1[i, :N], "b", linewidth=2)
- h2, = plt.plot(coeff[i] * x_f_mpc_2[i, :N], "r", linewidth=2)
- h3, = plt.plot(xref_1[i, :N], "b", linestyle="--", marker='x', color="g", linewidth=2)
- plt.xlabel("Time [s]")
- # plt.legend([h1, h2, h3], ["Output trajectory of classic MPC", "Output trajectory of croco MPC", "Input trajectory of planner"])
- plt.title("Predicted trajectory for " + titles[i])
- plt.suptitle("Analysis of trajectories in position and orientation computed by the MPC")
-
- plt.figure()
- for i in range(6):
- plt.subplot(3, 2, index6[i])
- h1, = plt.plot(x_f_mpc_1[i+6, :N], "b", linewidth=2)
- h2, = plt.plot(coeff[i] * x_f_mpc_2[i+6, :N], "r", linewidth=2)
- h3, = plt.plot(xref_1[i+6, :N], "b", linestyle="--", marker='x', color="g", linewidth=2)
- plt.xlabel("Time [s]")
- #plt.legend([h1, h2, h3], ["Output trajectory of classic MPC", "Output trajectory of croco MPC", "Input trajectory of planner"])
- plt.title("Predicted trajectory for velocity in " + titles[i])
- plt.suptitle("Analysis of trajectories of linear and angular velocities computed by the MPC")
-
- lgd1 = ["Ctct force X", "Ctct force Y", "Ctct force Z"]
- lgd2 = ["FL", "FR", "HL", "HR"]
- reorder = [9, 10, 11, 6, 7, 8, 3, 4, 5, 0, 1, 2]
- plt.figure()
- for i in range(12):
- if i == 0:
- ax0 = plt.subplot(3, 4, index12[i])
- else:
- plt.subplot(3, 4, index12[i], sharex=ax0)
- h1, = plt.plot(x_f_mpc_1[i+12, 1:(N+1)], "b", linewidth=3)
- h2, = plt.plot(coeff[i % 3] * x_f_mpc_2[reorder[i]+12, 1:(N+1)], "r", linewidth=3)
- # h3, = plt.plot(x_f_mpc_1[i+12, 1:(N+1)] - coeff[i % 3] * x_f_mpc_2[reorder[i]+12, 1:(N+1)], "g", linewidth=3)
- plt.xlabel("Time [s]")
- plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [N]")
- """if (i % 3) == 2:
- plt.ylim([-0.0, 26.0])
- else:
- plt.ylim([-26.0, 26.0])"""
- #plt.legend([h1, h2], ["Classic", "Croco"])
- plt.suptitle("Contact forces (MPC command)")
-
- plt.show()
-
-test_1()
-# test_2()
-#test_3()
\ No newline at end of file
diff --git a/scripts/test_mpc.py b/scripts/test_mpc.py
deleted file mode 100644
index b53d3092..00000000
--- a/scripts/test_mpc.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import unittest
-import numpy as np
-import time
-
-# Import classes to test
-import MPC_Wrapper
-np.set_printoptions(precision=3, linewidth=300)
-
-
-class TestStringMethods(unittest.TestCase):
-
- def test_upper(self):
- self.assertEqual('foo'.upper(), 'FOO')
-
- def test_isupper(self):
- self.assertTrue('FOO'.isupper())
- self.assertFalse('Foo'.isupper())
-
- def test_split(self):
- s = 'hello world'
- self.assertEqual(s.split(), ['hello', 'world'])
- # check that s.split fails when the separator is not a string
- with self.assertRaises(TypeError):
- s.split(2)
-
-
-class TestMPC(unittest.TestCase):
-
- def setUp(self):
-
- self.mass = 2.50000279
- type_MPC = 0
- dt_wbc = 0.002
- dt_mpc = 0.02
- k_mpc = int(dt_mpc / dt_wbc)
- T_mpc = 0.32
- T_gait = 0.32
- q_init = np.zeros((19, 1))
- q_init[0:7, 0] = np.array([0.0, 0.0, 0.24474949993103629, 0.0, 0.0, 0.0, 1.0])
- q_init[7:, 0] = np.array([0.0, 0.7, -1.4, -0.0, 0.7, -1.4, 0.0, -0.7, +1.4, -0.0, -0.7, +1.4])
- enable_multiprocessing = True
- self.mpc_wrapper = MPC_Wrapper.MPC_Wrapper(type_MPC, dt_mpc, np.int(T_mpc/dt_mpc),
- k_mpc, T_mpc, q_init, enable_multiprocessing)
-
- self.planner = MPC_Wrapper.Dummy()
- n_steps = np.int(T_gait/dt_mpc)
- self.planner.xref = np.zeros((12, 1 + n_steps))
- self.planner.gait = np.zeros((20, 5))
- self.planner.fsteps = np.zeros((20, 13))
-
- def tearDown(self):
- self.mpc_wrapper.stop_parallel_loop() # Stop MPC running in parallel process
-
- def test_fourstance_immobile(self):
-
- self.planner.xref[2, :] = 0.24474949993103629
- self.planner.gait[0, :] = np.array([16, 1, 1, 1, 1])
- self.planner.fsteps[0, :] = np.array([16, 0.195, 0.147, 0., 0.195, -0.147, 0., -0.195, 0.147, 0., -0.195, -0.147, 0.])
- """planner.fsteps[0, :] = np.array([7, 0.195, 0.147, 0., 0., 0., 0., 0., 0., 0., -0.195, -0.147, 0.])
- planner.fsteps[1, :] = np.array([8, 0., 0., 0., 0.195, -0.147, 0., -0.195, 0.147, 0., 0., 0., 0.])
- planner.fsteps[2, :] = np.array([1, 0.195, 0.147, 0., 0., 0., 0., 0., 0., 0., -0.195, -0.147, 0.])"""
-
- # First iteration returns [0.0, 0.0, 8.0] for all feet in contact
- self.mpc_wrapper.solve(0, self.planner)
- while not self.mpc_wrapper.newResult.value:
- time.sleep(0.002)
- x_f_mpc = self.mpc_wrapper.get_latest_result()
-
- # Second iteration should return [0.0, 0.0, mass/4] for all feet
- for i in range(1, 100):
- self.mpc_wrapper.solve(i, self.planner)
- while not self.mpc_wrapper.newResult.value:
- time.sleep(0.002)
- x_f_mpc = self.mpc_wrapper.get_latest_result() # Retrieve last result of MPC
- # print("X: ", x_f_mpc[:12, 0:6])
- # print("F: ", x_f_mpc[12:, 0:6])
-
- self.planner.xref[:, 0] = x_f_mpc[:12, 0].copy() # Update current state of the robot
-
- # print(9.81*self.mass/4)
- # print(x_f_mpc[12:, 0])
- # print(x_f_mpc[:12, 0] - self.planner.xref[:, 1])
- self.assertTrue(np.allclose(x_f_mpc[12:, 0], np.array([x_f_mpc[12, 0], x_f_mpc[13, 0], x_f_mpc[14, 0]] * 4)), "All feet forces are equal.")
- self.assertTrue(np.allclose(x_f_mpc[:12, 0], self.planner.xref[:, 1], atol=1e-3), "Close to reference state")
- # self.assertTrue(np.allclose(x_f_mpc[12:, 0], np.array([0.0, 0.0, 9.81*self.mass/4] * 4)), "Feet forces are equal to theorical value.")
-
- def test_fourstance_not_centered(self):
- self.planner.xref[2, :] = 0.24474949993103629
- self.planner.gait[0, :] = np.array([16, 1, 1, 1, 1])
- self.planner.fsteps[0, :] = np.array([16, 0.195, 0.147, 0., 0.195, -0.147, 0., -0.195, 0.147, 0., -0.195, -0.147, 0.])
-
- # Non centered state
- self.planner.xref[:, 0] = np.array([0.05, 0.05, 0.2, 0.1, 0.1, 0.1, 0.01, 0.01, 0.04, 0.4, 0.4, 0.4])
-
- # First iteration
- self.mpc_wrapper.solve(0, self.planner)
- while not self.mpc_wrapper.newResult.value:
- time.sleep(0.002)
- x_f_mpc = self.mpc_wrapper.get_latest_result()
-
- # Run the mpc during 500 iterations
- for i in range(1,500):
- self.mpc_wrapper.solve(i, self.planner)
- while not self.mpc_wrapper.newResult.value:
- time.sleep(0.002)
- x_f_mpc = self.mpc_wrapper.get_latest_result() # Retrieve last result of MPC
- self.planner.xref[:, 0] = x_f_mpc[:12, 0].copy() # Update current state of the robot
-
- self.assertTrue(np.allclose(x_f_mpc[12:, 0], np.array([x_f_mpc[12, 0], x_f_mpc[13, 0], x_f_mpc[14, 0]] * 4)), "All feet forces are equal.")
- self.assertTrue(np.allclose(x_f_mpc[:12, 0], self.planner.xref[:, 1], atol=1e-3), "Close to reference state")
-
- def roll(self, gait, fsteps):
- if(gait[0, 0]==1):
- gait[2, 0] += 1
- fsteps[2, 0] += 1
- gait[0, :] = gait[1, :].copy()
- gait[1, :] = gait[2, :].copy()
- fsteps[0, :] = fsteps[1, :].copy()
- fsteps[1, :] = fsteps[2, :].copy()
- gait[2, :] *= 0.0
- fsteps[2, :] *= 0.0
- elif(gait[0, 0]==8):
- gait[0, 0] -= 1
- fsteps[0, 0] -= 1
- gait[2, 0] = 1
- fsteps[2, 0] = 1
- gait[2, 1:] = gait[0, 1:].copy()
- fsteps[2, 1:] = fsteps[0, 1:].copy()
- else:
- gait[0, 0] -= 1
- fsteps[0, 0] -= 1
- gait[2, 0] += 1
- fsteps[2, 0] += 1
-
- # Two feet in stance phase at a given time (FL-HR and FR-HL) with a 0.32s gait period. Check if robot stays centered.
- def test_twostance_centered(self):
- pair_1 = np.array([0.195, 0.147, 0., 0., 0., 0., 0., 0., 0., -0.195, -0.147, 0.])
- pair_2 = np.array([0., 0., 0., 0.195, -0.147, 0., -0.195, 0.147, 0., 0., 0., 0.])
-
- self.planner.xref[2, :] = 0.24474949993103629
- self.planner.gait[0, :] = np.array([7, 1, 0, 0, 1])
- self.planner.gait[1, :] = np.array([8, 0, 1, 1, 0])
- self.planner.gait[2, :] = np.array([1, 1, 0, 0, 1])
- self.planner.fsteps[0:3, 0] = np.array([7, 8, 1])
- self.planner.fsteps[0, 1:] = pair_1
- self.planner.fsteps[1, 1:] = pair_2
- self.planner.fsteps[2, 1:] = pair_1
-
- # Run the mpc during 500 iterations
- for i in range(0, 500):
- self.mpc_wrapper.solve(i, self.planner)
- while not self.mpc_wrapper.newResult.value:
- time.sleep(0.002)
- x_f_mpc = self.mpc_wrapper.get_latest_result() # Retrieve last result of MPC
- self.roll(self.planner.gait, self.planner.fsteps)
- if(i > 0):
- self.planner.xref[:, 0] = x_f_mpc[:12, 0].copy() # Update current state of the robot
-
- print(x_f_mpc[:12, 0])
- self.assertTrue(np.allclose(x_f_mpc[:12, 0], self.planner.xref[:, 1], atol=1e-2), "Close to reference state")
-
- # Two feet in stance phase at a given time (FL-HR and FR-HL) with a 0.32s gait period. Check if robot stays centered.
- def test_twostance_not_centered(self):
- pair_1 = np.array([0.195, 0.147, 0., 0., 0., 0., 0., 0., 0., -0.195, -0.147, 0.])
- pair_2 = np.array([0., 0., 0., 0.195, -0.147, 0., -0.195, 0.147, 0., 0., 0., 0.])
-
- self.planner.xref[2, :] = 0.24474949993103629
- self.planner.gait[0, :] = np.array([7, 1, 0, 0, 1])
- self.planner.gait[1, :] = np.array([8, 0, 1, 1, 0])
- self.planner.gait[2, :] = np.array([1, 1, 0, 0, 1])
- self.planner.fsteps[0:3, 0] = np.array([7, 8, 1])
- self.planner.fsteps[0, 1:] = pair_1
- self.planner.fsteps[1, 1:] = pair_2
- self.planner.fsteps[2, 1:] = pair_1
-
- # Non centered state
- self.planner.xref[:, 0] = np.array([0.05, 0.05, 0.2, 0.1, 0.1, 0.1, 0.01, 0.01, 0.04, 0.4, 0.4, 0.4])
-
- # Run the mpc during 500 iterations
- for i in range(0, 2000):
- self.mpc_wrapper.solve(i, self.planner)
- while not self.mpc_wrapper.newResult.value:
- time.sleep(0.002)
- x_f_mpc = self.mpc_wrapper.get_latest_result() # Retrieve last result of MPC
- self.roll(self.planner.gait, self.planner.fsteps)
- if(i > 0):
- self.planner.xref[:, 0] = x_f_mpc[:12, 0].copy() # Update current state of the robot
-
- print(x_f_mpc[:12, 0])
- self.assertTrue(np.allclose(x_f_mpc[:12, 0], self.planner.xref[:, 1], atol=1e-2), "Close to reference state")
-
-
- def test_upper(self):
- self.assertEqual('foo'.upper(), 'FOO')
-
-
-
-if __name__ == '__main__':
- unittest.main()
diff --git a/scripts/test_new_InvKin.py b/scripts/test_new_InvKin.py
deleted file mode 100644
index 00c1bfe1..00000000
--- a/scripts/test_new_InvKin.py
+++ /dev/null
@@ -1,214 +0,0 @@
-import numpy as np
-import utils_mpc
-from example_robot_data.robots_loader import Solo12Loader
-import pinocchio as pin
-
-class Test:
-
- def __init__(self, params):
-
- self.k = 0
- self.k_mpc = 20
- self.dt_wbc = params.dt_wbc
- q_init = np.array(params.q_init.tolist())
-
- # Initialisation of the solo model/data and of the Gepetto viewer
- self.solo = utils_mpc.init_robot(q_init, params)
-
- self.h_ref = params.h_ref
-
- self.wbcWrapper = qrw.WbcWrapper()
- self.wbcWrapper.initialize(params)
-
- self.gait = qrw.Gait()
- self.gait.initialize(params)
-
- self.footstepPlanner = qrw.FootstepPlanner()
- self.footstepPlanner.initialize(params, self.gait)
-
- self.footTrajectoryGenerator = qrw.FootTrajectoryGenerator()
- self.footTrajectoryGenerator.initialize(params, self.gait)
-
- self.q = np.zeros((19, 1)) # Orientation part is in roll pitch yaw
- self.q[0:7, 0] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0])
- self.q[7:, 0] = q_init
- self.h_v = np.zeros((18, 1))
- self.h_v_ref = np.zeros((18, 1))
-
- self.q_wbc = np.zeros((18, 1))
- self.dq_wbc = np.zeros((18, 1))
- self.xgoals = np.zeros((12, 1))
- self.xgoals[2, 0] = self.h_ref
-
- self.ddq_cmd = np.zeros((18, 1))
-
- # Load robot model and data
- """Solo12Loader.free_flyer = True
- self.solo = Solo12Loader().robot"""
-
- def run(self):
-
- # Update gait
- self.gait.updateGait(self.k, self.k_mpc, 0)
- cgait = self.gait.getCurrentGait()
-
- # Compute target footstep based on current and reference velocities
- qftps = np.zeros((18, 1))
- qftps[:3, 0] = self.q[:3, 0]
- RPY = pin.rpy.matrixToRpy(pin.Quaternion(self.q[3:7, 0:1]).toRotationMatrix())
- qftps[3:6, 0] = RPY
- qftps[6:, 0] = self.q[7:, 0]
- o_targetFootstep = self.footstepPlanner.updateFootsteps(self.k % self.k_mpc == 0 and self.k != 0,
- int(self.k_mpc - self.k % self.k_mpc),
- qftps[:, 0],
- self.h_v[0:6, 0:1].copy(),
- self.h_v_ref[0:6, 0:1])
-
- # Update pos, vel and acc references for feet
- self.footTrajectoryGenerator.update(self.k, o_targetFootstep)
-
- # Update configuration vector for wbc
- self.q_wbc[3, 0] = RPY[0] # Roll
- self.q_wbc[4, 0] = RPY[1] # Pitch
- self.q_wbc[6:, 0] = self.q[7:, 0] # Joints
-
- # Update velocity vector for wbc
- self.dq_wbc[:, 0] = self.h_v[:, 0].copy()
-
- # Feet command position, velocity and acceleration in base frame
- oRh = pin.Quaternion(self.q[3:7, 0:1]).toRotationMatrix()
- oTh = self.q[:3, 0:1]
-
- self.feet_a_cmd = self.footTrajectoryGenerator.getFootAccelerationBaseFrame(
- oRh.transpose(), np.zeros((3, 1)), np.zeros((3, 1)))
- self.feet_v_cmd = self.footTrajectoryGenerator.getFootVelocityBaseFrame(
- oRh.transpose(), np.zeros((3, 1)), np.zeros((3, 1)))
- self.feet_p_cmd = self.footTrajectoryGenerator.getFootPositionBaseFrame(
- oRh.transpose(), oTh + np.array([[0.0], [0.0], [self.h_ref]]))
-
- # Desired position, orientation and velocities of the base
- self.xgoals[[0, 1, 5], 0] = np.zeros((3,))
- self.xgoals[2:5, 0] = [0.0, 0.0, 0.0]
-
- self.xgoals[6:, 0] = self.h_v_ref[:6, 0] # Velocities (in horizontal frame!)
-
- cgait = self.gait.getCurrentGait()
-
- """if self.k == 50:
- from IPython import embed
- embed()"""
-
- # Run InvKin + WBC QP
- self.wbcWrapper.compute(self.q_wbc, self.dq_wbc,
- np.zeros((12, 1)), np.array([cgait[0, :]]),
- self.feet_p_cmd,
- self.feet_v_cmd,
- self.feet_a_cmd,
- self.xgoals)
-
- # Quantities sent to the control board
- self.h_v[:, 0] += self.dt_wbc * self.wbcWrapper.ddq_cmd[:]
- self.q[:, 0] = pin.integrate(self.solo.model, self.q, self.dt_wbc * self.h_v)
-
- self.k += 1
-
-
-if __name__ == "__main__":
-
- import quadruped_reactive_walking as qrw
- import pinocchio as pin
-
- params = qrw.Params() # Object that holds all controller parameters
- params.enable_corba_viewer = True
- test = Test(params)
- q_display = np.zeros((19, 1))
-
- test.h_v[0, 0] = 0.6
- test.h_v_ref[0, 0] = 0.7
-
- N = 10000
- log_q = np.zeros((N, 19))
- log_q_wbc = np.zeros((N, 18))
- log_dq = np.zeros((N, 18))
- log_ddq_cmd = np.zeros((N, 18))
- log_ddq_with_delta = np.zeros((N, 18))
- for i in range(N):
- test.run()
-
- log_ddq_cmd[i] = test.wbcWrapper.ddq_cmd
- log_dq[i] = test.h_v[:, 0]
- log_q[i] = test.q[:, 0]
- log_q_wbc[i] = test.q_wbc[:, 0]
-
- print("#####-> ", test.h_v[0, 0])
-
- """test.q[2, 0] = test.b_des[2]
- test.q[3:6, 0] = pin.rpy.matrixToRpy(pin.Quaternion(test.b_des[3:7].reshape((-1, 1))).toRotationMatrix())
- test.q[6:, 0] = test.q_des[:]"""
-
- # Display robot in Gepetto corba viewer
- if (i % 10 == 0 and i > 0):
- q_display[:3, 0] = test.q[0:3, 0]
- q_display[3:7, 0] = test.q[3:7, 0]
- q_display[7:, 0] = test.q[7:, 0]
- test.solo.display(q_display)
-
- """from IPython import embed
- embed()"""
- """test.q[0, 0] += params.dt_wbc * test.h_v[0, 0]
- test.q[1, 0] = 0.0
- test.q[5, 0] = 0.0"""
-
- index6 = [1, 3, 5, 2, 4, 6]
-
- from matplotlib import pyplot as plt
- t_range = np.array([k*0.001 for k in range(N)])
-
- lgd = ["Acc X", "Acc Y", "Acc Z", "Acc Roll", "Acc Pitch", "Acc Yaw"]
- plt.figure()
- for i in range(6):
- if i == 0:
- ax0 = plt.subplot(3, 2, index6[i])
- else:
- plt.subplot(3, 2, index6[i], sharex=ax0)
-
- plt.plot(t_range, log_ddq_cmd[:, i], "b", linewidth=3)
-
- plt.legend(["ddq_cmd"], prop={'size': 8})
- plt.ylabel(lgd[i])
-
- plt.show(block=True)
-
- lgd = ["Pos X", "Pos Y", "Pos Z", "Roll", "Pitch", "Yaw"]
- plt.figure()
- for i in range(6):
- if i == 0:
- ax0 = plt.subplot(3, 2, index6[i])
- else:
- plt.subplot(3, 2, index6[i], sharex=ax0)
-
- if i < 3:
- plt.plot(t_range, log_q[:, i], "b", linewidth=3)
- else:
- plt.plot(t_range, log_q_wbc[:, i], "b", linewidth=3)
-
- plt.legend(["Robot state"], prop={'size': 8})
- plt.ylabel(lgd[i])
-
- lgd = ["Vel X", "Vel Y", "Vel Z", "Vel Roll", "Vel Pitch", "Vel Yaw"]
- plt.figure()
- for i in range(6):
- if i == 0:
- ax0 = plt.subplot(3, 2, index6[i])
- else:
- plt.subplot(3, 2, index6[i], sharex=ax0)
-
- plt.plot(t_range, log_dq[:, i], "b", linewidth=3)
-
- plt.legend(["Robot state"], prop={'size': 8})
- plt.ylabel(lgd[i])
-
- plt.show(block=True)
- from IPython import embed
- embed()
-
diff --git a/scripts/test_tasks_InvKin.py b/scripts/test_tasks_InvKin.py
deleted file mode 100644
index 0f2136f7..00000000
--- a/scripts/test_tasks_InvKin.py
+++ /dev/null
@@ -1,277 +0,0 @@
-import numpy as np
-import utils_mpc
-from example_robot_data.robots_loader import Solo12Loader
-import pinocchio as pin
-import quadruped_reactive_walking as qrw
-from IPython import embed
-
-def check_task_tracking():
-
- params = qrw.Params() # Object that holds all controller parameters
- params.w_tasks = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] # Keep only contact task
- dt = params.dt_wbc
- h_ref = 0.244749
-
- # Load robot model and data
- # Initialisation of the Gepetto viewer
- Solo12Loader.free_flyer = True
- solo = Solo12Loader().robot
- q = solo.q0.reshape((-1, 1))
- q[2, 0] = h_ref
- q[7:, 0] = params.q_init
- dq = np.zeros((18, 1))
-
- solo.initViewer(loadModel=True)
- if ('viewer' in solo.viz.__dict__):
- solo.viewer.gui.addFloor('world/floor')
- solo.viewer.gui.setRefreshIsSynchronous(False)
- solo.display(q)
-
- wbcWrapper = qrw.WbcWrapper()
- wbcWrapper.initialize(params)
-
- feet_p_cmd0 = np.array([[0.1946, 0.1946, -0.1946, -0.1946],
- [0.16891, -0.16891, 0.16891, -0.16891],
- [0.0191028, 0.0191028, 0.0191028, 0.0191028]])
- feet_p_cmd0[2, :] = - h_ref
- feet_p_cmd = np.zeros((3, 4))
- feet_v_cmd = np.zeros((3, 4))
- feet_a_cmd = np.zeros((3, 4))
- xgoals = np.zeros((12, 1))
- q_wbc = np.zeros((18, 1))
-
- k = 0
- N = 50000
- while k < N:
-
- # Rotation from world to base frame
- oRb = pin.Quaternion(q[3:7, 0:1]).toRotationMatrix()
-
- # Roll pitch yaw vector
- RPY = pin.rpy.matrixToRpy(oRb)
-
- # Rotation from horizontal to base frame
- hRb = pin.rpy.rpyToMatrix(RPY[0], RPY[1], 0.0)
-
- # Target positions of feet
- feet_p_cmd = feet_p_cmd0.copy()
- feet_v_cmd = np.zeros((3, 4))
- feet_a_cmd = np.zeros((3, 4))
- feet_p_cmd[0, 0] = 0.1946 + 0.04 * np.sin(2 * np.pi * 0.25 * k * dt)
- feet_p_cmd[1, 1] = -0.16891 + 0.04 * np.sin(2 * np.pi * 0.25 * k * dt)
- feet_p_cmd[2, 2] = - h_ref + 0.04 * np.sin(2 * np.pi * 0.25 * k * dt)
- feet_v_cmd[0, 0] = 0.04 * 2 * np.pi * 0.25 * np.cos(2 * np.pi * 0.25 * k * dt)
- feet_v_cmd[1, 1] = 0.04 * 2 * np.pi * 0.25 * np.cos(2 * np.pi * 0.25 * k * dt)
- feet_v_cmd[2, 2] = 0.04 * 2 * np.pi * 0.25 * np.cos(2 * np.pi * 0.25 * k * dt)
- feet_a_cmd[0, 0] = -0.04 * (2 * np.pi * 0.25)**2 * np.sin(2 * np.pi * 0.25 * k * dt)
- feet_a_cmd[1, 1] = -0.04 * (2 * np.pi * 0.25)**2 * np.sin(2 * np.pi * 0.25 * k * dt)
- feet_a_cmd[2, 2] = -0.04 * (2 * np.pi * 0.25)**2 * np.sin(2 * np.pi * 0.25 * k * dt)
-
- # Express feet so that they follow base orientation
- feet_p_cmd = hRb @ feet_p_cmd
- feet_v_cmd = hRb @ feet_v_cmd
- feet_a_cmd = hRb @ feet_a_cmd
-
- # Goal is 20 degrees in pitch
- xgoals[4, 0] = - 20.0 / 57
-
- # Make q_wbc vector
- q_wbc[:3, 0] = np.zeros(3) # Position
- q_wbc[3, 0] = RPY[0] # Roll
- q_wbc[4, 0] = RPY[1] # Pitch
- q_wbc[5, 0] = 0.0 # Yaw
- q_wbc[6:, 0] = q[7:, 0] # Actuators
-
- # Run InvKin + WBC QP
- wbcWrapper.compute(q_wbc, dq, np.zeros(12), np.array([[0.0, 0.0, 0.0, 1.0]]),
- feet_p_cmd, feet_v_cmd, feet_a_cmd, xgoals)
-
- # Velocity integration
- dq[0:3, 0:1] += dt * oRb.transpose() @ wbcWrapper.ddq_cmd[0:3].reshape((-1, 1))
- dq[3:6, 0:1] += dt * oRb.transpose() @ wbcWrapper.ddq_cmd[3:6].reshape((-1, 1))
- dq[6:, 0] += dt * wbcWrapper.ddq_cmd[6:]
-
- # Position integration
- q[:, 0] = pin.integrate(solo.model, q, dt * dq)
-
- k += 1
-
- if (k % 10 == 0):
- solo.display(q)
-
-
-def check_task_contact():
-
- params = qrw.Params() # Object that holds all controller parameters
- params.w_tasks = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] # Keep only contact task
- dt = params.dt_wbc
- h_ref = 0.244749
-
- # Load robot model and data
- # Initialisation of the Gepetto viewer
- Solo12Loader.free_flyer = True
- solo = Solo12Loader().robot
- q = solo.q0.reshape((-1, 1))
- q[2, 0] = h_ref
- q[7:, 0] = params.q_init
- dq = np.zeros((18, 1))
-
- solo.initViewer(loadModel=True)
- if ('viewer' in solo.viz.__dict__):
- solo.viewer.gui.addFloor('world/floor')
- solo.viewer.gui.setRefreshIsSynchronous(False)
- solo.display(q)
-
- wbcWrapper = qrw.WbcWrapper()
- wbcWrapper.initialize(params)
-
- feet_p_cmd0 = np.array([[0.1946, 0.1946, -0.1946, -0.1946],
- [0.16891, -0.16891, 0.16891, -0.16891],
- [0.0191028, 0.0191028, 0.0191028, 0.0191028]])
- feet_p_cmd0[2, :] = - h_ref
- feet_p_cmd = np.zeros((3, 4))
- feet_v_cmd = np.zeros((3, 4))
- feet_a_cmd = np.zeros((3, 4))
- xgoals = np.zeros((12, 1))
- q_wbc = np.zeros((18, 1))
-
- k = 0
- N = 10000
- freq = 0.2
- log_f = np.zeros((N, 3, 4))
- log_fcmd = np.zeros((N, 3, 4))
- dx = 0.0
- dy = 0.0
- dw = 0.0
- while k < N:
-
- # Displacement
- sig = np.sign(np.sin(2 * np.pi * freq * k * dt))
- V = 0.02
- W = 10.0 / 57
- dx += dt * V * sig
- dy += dt * V * sig
- dw += dt * W * sig
- q[0, 0] = dx # 0.04 * np.cos(2 * np.pi * freq * k * dt)
- q[1, 0] = dy # 0.04 * np.sin(2 * np.pi * freq * k * dt)
- RPY = pin.rpy.matrixToRpy(pin.Quaternion(q[3:7, 0:1]).toRotationMatrix())
- RPY[2] = dw
- q[3:7, 0] = pin.Quaternion(pin.rpy.rpyToMatrix(RPY[0], RPY[1], RPY[2])).coeffs()
-
- # Rotation from world to base frame
- oRb = pin.Quaternion(q[3:7, 0:1]).toRotationMatrix()
-
- # Roll pitch yaw vector
- RPY = pin.rpy.matrixToRpy(oRb)
-
- # Rotation from horizontal to base frame
- oRh = pin.rpy.rpyToMatrix(0.0, 0.0, RPY[2])
- hRb = pin.rpy.rpyToMatrix(RPY[0], RPY[1], 0.0)
-
- # Target positions of feet
- feet_p_cmd = feet_p_cmd0.copy()
- feet_v_cmd = np.zeros((3, 4))
- feet_a_cmd = np.zeros((3, 4))
-
- # Feet in the air move with the base
- """feet_p_cmd[0, 1] += 0.04 * np.cos(2 * np.pi * freq * k * dt)
- feet_p_cmd[1, 1] += 0.04 * np.sin(2 * np.pi * freq * k * dt)
- feet_p_cmd[0, 2] += 0.04 * np.cos(2 * np.pi * freq * k * dt)
- feet_p_cmd[1, 2] += 0.04 * np.sin(2 * np.pi * freq * k * dt)"""
-
- """feet_v_cmd[0, 1] = -0.04 * 2 * np.pi * freq * np.sin(2 * np.pi * freq * k * dt)
- feet_v_cmd[1, 1] = 0.04 * 2 * np.pi * freq * np.cos(2 * np.pi * freq * k * dt)
- feet_v_cmd[0, 2] = -0.04 * 2 * np.pi * freq * np.sin(2 * np.pi * freq * k * dt)
- feet_v_cmd[1, 2] = 0.04 * 2 * np.pi * freq * np.cos(2 * np.pi * freq * k * dt)
-
- feet_a_cmd[0, 1] = -0.04 * (2 * np.pi * freq)**2 * np.cos(2 * np.pi * freq * k * dt)
- feet_a_cmd[1, 1] = -0.04 * (2 * np.pi * freq)**2 * np.sin(2 * np.pi * freq * k * dt)
- feet_a_cmd[0, 2] = -0.04 * (2 * np.pi * freq)**2 * np.cos(2 * np.pi * freq * k * dt)
- feet_a_cmd[1, 2] = -0.04 * (2 * np.pi * freq)**2 * np.sin(2 * np.pi * freq * k * dt)
-
- # Feet in contact do not move with the base
- feet_p_cmd[0, 0] -= 0.04 * np.cos(2 * np.pi * freq * k * dt)
- feet_p_cmd[1, 0] -= 0.04 * np.sin(2 * np.pi * freq * k * dt)
- feet_p_cmd[0, 3] -= 0.04 * np.cos(2 * np.pi * freq * k * dt)
- feet_p_cmd[1, 3] -= 0.04 * np.sin(2 * np.pi * freq * k * dt)"""
-
- feet_v_cmd[0, 1] = V * sig
- feet_v_cmd[1, 1] = V * sig
- feet_v_cmd[0, 2] = V * sig
- feet_v_cmd[1, 2] = V * sig
-
- for i in [0, 3]:
- feet_p_cmd[0, i] -= dx
- feet_p_cmd[1, i] -= dy
- feet_p_cmd[:, i:(i+1)] = oRh.transpose() @ feet_p_cmd[:, i:(i+1)]
- feet_v_cmd[:, i:(i+1)] = oRh.transpose() @ feet_v_cmd[:, i:(i+1)]
- feet_a_cmd[:, i:(i+1)] = oRh.transpose() @ feet_a_cmd[:, i:(i+1)]
-
- # Express feet so that they follow base orientation
- feet_p_cmd = hRb @ feet_p_cmd
- feet_v_cmd = hRb @ feet_v_cmd
- feet_a_cmd = hRb @ feet_a_cmd
-
- # Make q_wbc vector
- q_wbc[:3, 0] = np.zeros(3) # Position
- q_wbc[3, 0] = RPY[0] # Roll
- q_wbc[4, 0] = RPY[1] # Pitch
- q_wbc[5, 0] = 0.0 # Yaw
- q_wbc[6:, 0] = q[7:, 0] # Actuators
-
- xgoals[6, 0] = V * sig
- xgoals[7, 0] = V * sig
- xgoals[11, 0] = W * sig
-
- # Run InvKin + WBC QP
- wbcWrapper.compute(q_wbc, dq, np.zeros(12), np.array([[1.0, 0.0, 0.0, 1.0]]),
- feet_p_cmd, feet_v_cmd, feet_a_cmd, xgoals)
-
- # Logging
- """log_f_cmd = feet_p_cmd0.copy()
- log_f_cmd[0, 1] += 0.04 * np.cos(2 * np.pi * freq * k * dt)
- log_f_cmd[1, 1] += 0.04 * np.sin(2 * np.pi * freq * k * dt)
- log_f_cmd[0, 2] += 0.04 * np.cos(2 * np.pi * freq * k * dt)
- log_f_cmd[1, 2] += 0.04 * np.sin(2 * np.pi * freq * k * dt)"""
- log_fcmd[k] = wbcWrapper.feet_pos_target
- log_fcmd[k, 0, :] += q[0, 0]
- log_fcmd[k, 1, :] += q[1, 0]
- log_fcmd[k, :, :] = oRh @ log_fcmd[k, :, :]
- log_f[k] = wbcWrapper.feet_pos
- log_f[k, 0, :] += q[0, 0]
- log_f[k, 1, :] += q[1, 0]
- log_f[k, :, :] = oRh @ log_f[k, :, :]
-
- # Velocity integration
- dq[0:3, 0:1] += dt * oRb.transpose() @ wbcWrapper.ddq_cmd[0:3].reshape((-1, 1))
- dq[3:6, 0:1] += dt * oRb.transpose() @ wbcWrapper.ddq_cmd[3:6].reshape((-1, 1))
- dq[6:, 0] += dt * wbcWrapper.ddq_cmd[6:]
-
- # Position integration
- q[:, 0] = pin.integrate(solo.model, q, dt * dq)
-
- k += 1
-
- if (k % 10 == 0):
- solo.display(q)
-
- from matplotlib import pyplot as plt
- for j in range(3):
- plt.figure()
- for i in range(4):
- if i == 0:
- ax0 = plt.subplot(2, 2, i+1)
- else:
- plt.subplot(2, 2, i+1, sharex=ax0)
-
- plt.plot(log_f[:, j, i], "b", linewidth=3)
- plt.plot(log_fcmd[:, j, i], "r", linewidth=3, linestyle="--")
-
- plt.legend(["Mes", "Ref"], prop={'size': 8})
-
- plt.show(block=True)
-
-if __name__ == "__main__":
-
- # check_task_tracking()
- check_task_contact()
diff --git a/scripts/ForceMonitor.py b/scripts/tools/ForceMonitor.py
similarity index 99%
rename from scripts/ForceMonitor.py
rename to scripts/tools/ForceMonitor.py
index bb573625..36a4eb8d 100644
--- a/scripts/ForceMonitor.py
+++ b/scripts/tools/ForceMonitor.py
@@ -1,9 +1,5 @@
-# coding: utf8
-
-import numpy as np
import pybullet as pyb
-
class ForceMonitor:
def __init__(self, robotId, planeId):
diff --git a/scripts/LoggerControl.py b/scripts/tools/LoggerControl.py
similarity index 100%
rename from scripts/LoggerControl.py
rename to scripts/tools/LoggerControl.py
diff --git a/scripts/LoggerSensors.py b/scripts/tools/LoggerSensors.py
similarity index 100%
rename from scripts/LoggerSensors.py
rename to scripts/tools/LoggerSensors.py
diff --git a/scripts/PyBulletSimulator.py b/scripts/tools/PyBulletSimulator.py
similarity index 100%
rename from scripts/PyBulletSimulator.py
rename to scripts/tools/PyBulletSimulator.py
diff --git a/scripts/gamepadClient.py b/scripts/tools/gamepadClient.py
similarity index 100%
rename from scripts/gamepadClient.py
rename to scripts/tools/gamepadClient.py
diff --git a/scripts/place_com_with_invkin.py b/scripts/tools/place_com_with_invkin.py
similarity index 99%
rename from scripts/place_com_with_invkin.py
rename to scripts/tools/place_com_with_invkin.py
index ec936ced..a34d50e6 100644
--- a/scripts/place_com_with_invkin.py
+++ b/scripts/tools/place_com_with_invkin.py
@@ -1,7 +1,6 @@
-from IPython import embed
import numpy as np
-from example_robot_data.robots_loader import Solo12Loader
import pinocchio as pin
+from example_robot_data.robots_loader import Solo12Loader
# Parameters of the InvkinÂ
l = 0.1946 * 2
diff --git a/scripts/tools/qualisysClient.py b/scripts/tools/qualisysClient.py
new file mode 100644
index 00000000..614da788
--- /dev/null
+++ b/scripts/tools/qualisysClient.py
@@ -0,0 +1,161 @@
+"""
+This client connects to a Qualisys (motion capture) server with an asyncronous subprocess and expose 6d position and velocity of a given body
+Thomas FLAYOLS - LAAS CNRS
+"""
+
+import asyncio
+from multiprocessing import Process, Lock
+from multiprocessing.sharedctypes import Value, Array
+from ctypes import c_double
+import qtm
+import numpy as np
+
+import pinocchio
+from pinocchio.utils import se3ToXYZQUAT
+from pinocchio.explog import log
+
+
+class QualisysClient():
+ def __init__(self, ip="127.0.0.1", body_id=0):
+ # shared c_double array
+ self.shared_bodyPosition = Array(c_double, 3, lock=False)
+ self.shared_bodyVelocity = Array(c_double, 3, lock=False)
+ self.shared_bodyOrientationQuat = Array(c_double, 4, lock=False)
+ self.shared_bodyOrientationMat9 = Array(c_double, 9, lock=False)
+ self.shared_bodyAngularVelocity = Array(c_double, 3, lock=False)
+ self.shared_timestamp = Value(c_double, lock=False)
+ #self.shared_timestamp = -1
+ args = (ip, body_id, self.shared_bodyPosition, self.shared_bodyVelocity,
+ self.shared_bodyOrientationQuat, self.shared_bodyOrientationMat9,
+ self.shared_bodyAngularVelocity, self.shared_timestamp)
+ self.p = Process(target=self.qualisys_process, args=args)
+ self.p.start()
+
+ def stop(self):
+ self.p.terminate()
+ self.p.join()
+
+ def getPosition(self):
+ return np.array([self.shared_bodyPosition[0],
+ self.shared_bodyPosition[1],
+ self.shared_bodyPosition[2]])
+
+ def getVelocity(self):
+ return np.array([self.shared_bodyVelocity[0],
+ self.shared_bodyVelocity[1],
+ self.shared_bodyVelocity[2]])
+
+ def getAngularVelocity(self):
+ return np.array([self.shared_bodyAngularVelocity[0],
+ self.shared_bodyAngularVelocity[1],
+ self.shared_bodyAngularVelocity[2]])
+
+ def getOrientationMat9(self):
+ return np.array([[self.shared_bodyOrientationMat9[0], self.shared_bodyOrientationMat9[1], self.shared_bodyOrientationMat9[2]],
+ [self.shared_bodyOrientationMat9[3], self.shared_bodyOrientationMat9[4],
+ self.shared_bodyOrientationMat9[5]],
+ [self.shared_bodyOrientationMat9[6], self.shared_bodyOrientationMat9[7], self.shared_bodyOrientationMat9[8]]])
+
+ def getOrientationQuat(self):
+ return np.array([self.shared_bodyOrientationQuat[0],
+ self.shared_bodyOrientationQuat[1],
+ self.shared_bodyOrientationQuat[2],
+ self.shared_bodyOrientationQuat[3]])
+
+ def qualisys_process(self, ip, body_id, shared_bodyPosition, shared_bodyVelocity,
+ shared_bodyOrientationQuat, shared_bodyOrientationMat9,
+ shared_bodyAngularVelocity, shared_timestamp):
+ print("Qualisys process!")
+ ''' This will run on a different process'''
+ shared_timestamp.value = -1
+
+ def on_packet(packet):
+ """ Callback function that is called everytime a data packet arrives from QTM """
+ position = packet.get_6d()[1][body_id][0]
+ orientation = packet.get_6d()[1][body_id][1]
+ timestamp = packet.timestamp * 1e-6
+
+ # Get the last position and Rotation matrix from the shared memory.
+ last_position = np.array(
+ [shared_bodyPosition[0], shared_bodyPosition[1], shared_bodyPosition[2]])
+ last_rotation = np.array([[shared_bodyOrientationMat9[0], shared_bodyOrientationMat9[1], shared_bodyOrientationMat9[2]],
+ [shared_bodyOrientationMat9[3], shared_bodyOrientationMat9[4], shared_bodyOrientationMat9[5]],
+ [shared_bodyOrientationMat9[6], shared_bodyOrientationMat9[7], shared_bodyOrientationMat9[8]]])
+ last_se3 = pinocchio.SE3(last_rotation, last_position)
+
+ # Get the new position and Rotation matrix from the motion capture.
+ position = np.array([position.x, position.y, position.z]) * 1e-3
+ rotation = np.array(orientation.matrix).reshape(3, 3).transpose()
+ se3 = pinocchio.SE3(rotation, position)
+ xyzquat = se3ToXYZQUAT(se3)
+
+ # Get the position, Rotation matrix and Quaternion
+ shared_bodyPosition[0] = xyzquat[0]
+ shared_bodyPosition[1] = xyzquat[1]
+ shared_bodyPosition[2] = xyzquat[2]
+ shared_bodyOrientationQuat[0] = xyzquat[3]
+ shared_bodyOrientationQuat[1] = xyzquat[4]
+ shared_bodyOrientationQuat[2] = xyzquat[5]
+ shared_bodyOrientationQuat[3] = xyzquat[6]
+
+ shared_bodyOrientationMat9[0] = orientation.matrix[0]
+ shared_bodyOrientationMat9[1] = orientation.matrix[3]
+ shared_bodyOrientationMat9[2] = orientation.matrix[6]
+ shared_bodyOrientationMat9[3] = orientation.matrix[1]
+ shared_bodyOrientationMat9[4] = orientation.matrix[4]
+ shared_bodyOrientationMat9[5] = orientation.matrix[7]
+ shared_bodyOrientationMat9[6] = orientation.matrix[2]
+ shared_bodyOrientationMat9[7] = orientation.matrix[5]
+ shared_bodyOrientationMat9[8] = orientation.matrix[8]
+
+ # Compute world velocity.
+ if (shared_timestamp.value == -1):
+ shared_bodyVelocity[0] = 0
+ shared_bodyVelocity[1] = 0
+ shared_bodyVelocity[2] = 0
+ shared_bodyAngularVelocity[0] = 0.0
+ shared_bodyAngularVelocity[1] = 0.0
+ shared_bodyAngularVelocity[2] = 0.0
+ else:
+ dt = timestamp - shared_timestamp.value
+ shared_bodyVelocity[0] = (position[0] - last_position[0])/dt
+ shared_bodyVelocity[1] = (position[1] - last_position[1])/dt
+ shared_bodyVelocity[2] = (position[2] - last_position[2])/dt
+ bodyAngularVelocity = log(last_se3.rotation.T.dot(se3.rotation))/dt
+ shared_bodyAngularVelocity[0] = bodyAngularVelocity[0]
+ shared_bodyAngularVelocity[1] = bodyAngularVelocity[1]
+ shared_bodyAngularVelocity[2] = bodyAngularVelocity[2]
+
+ shared_timestamp.value = timestamp
+
+ async def setup():
+ """ Main function """
+ connection = await qtm.connect(ip)
+ if connection is None:
+ print("no connection with qualisys!")
+ return
+ print("Connected")
+ try:
+ await connection.stream_frames(components=["6d"], on_packet=on_packet)
+ except:
+ print("connection with qualisys lost")
+
+ asyncio.ensure_future(setup())
+ asyncio.get_event_loop().run_forever()
+
+
+def exampleOfUse():
+ import time
+ qc = QualisysClient(ip="140.93.16.160", body_id=0)
+ for i in range(300):
+ print(chr(27) + "[2J")
+ print("position: ", qc.getPosition())
+ print("quaternion: ", qc.getOrientationQuat())
+ print("linear velocity: ", qc.getVelocity())
+ print("angular velocity: ", qc.getAngularVelocity())
+ time.sleep(0.3)
+ print("killme!")
+
+
+if __name__ == "__main__":
+ exampleOfUse()
diff --git a/scripts/utils_mpc.py b/scripts/tools/utils_mpc.py
similarity index 69%
rename from scripts/utils_mpc.py
rename to scripts/tools/utils_mpc.py
index 7651307f..b0b82fd9 100644
--- a/scripts/utils_mpc.py
+++ b/scripts/tools/utils_mpc.py
@@ -3,11 +3,6 @@ from example_robot_data.robots_loader import Solo12Loader
import pinocchio as pin
-##################
-# Initialisation #
-##################
-
-
def init_robot(q_init, params):
"""Load the solo model and initialize the Gepetto viewer if it is enabled
@@ -15,7 +10,6 @@ def init_robot(q_init, params):
q_init (array): the default position of the robot actuators
params (object): store parameters
"""
-
# Load robot model and data
# Initialisation of the Gepetto viewer
Solo12Loader.free_flyer = True
@@ -81,35 +75,6 @@ def init_robot(q_init, params):
for j in range(3):
params.shoulders[3*i+j] = shoulders_init[j, i]
params.footsteps_init[3*i+j] = fsteps_init[j, i]
- params.footsteps_under_shoulders[3*i+j] = fsteps_init[j, i] #Â Use initial feet pos as reference
+ params.footsteps_under_shoulders[3*i+j] = fsteps_init[j, i] # Â Use initial feet pos as reference
return solo
-
-
-def display_all(solo, k, sequencer, fstep_planner, ftraj_gen, mpc):
- """Update various objects in the Gepetto viewer: the Solo robot as well as debug spheres
-
- Args:
- k (int): current iteration of the simulation
- sequencer (object): ContactSequencer object
- fstep_planner (object): FootstepPlanner object
- ftraj_gen (object): FootTrajectoryGenerator object
- mpc (object): MpcSolver object
- """
-
- # Display non-locked target footholds with green spheres (gepetto gui)
- fstep_planner.update_viewer(solo.viewer, (k == 0))
-
- # Display locked target footholds with red spheres (gepetto gui)
- # Display desired 3D position of feet with magenta spheres (gepetto gui)
- ftraj_gen.update_viewer(solo.viewer, (k == 0))
-
- # Display reference trajectory, predicted trajectory, desired contact forces, current velocity
- # mpc.update_viewer(solo.viewer, (k == 0), sequencer)
- # mpc.plot_graphs(sequencer)
-
- qu_pinocchio = np.array(solo.q0).flatten()
- qu_pinocchio[0:3] = mpc.q_w[0:3, 0]
- qu_pinocchio[3:7] = pin.rpy.matrixToRpy(pin.Quaternion(mpc.q_w[3:6, 0]).toRotationMatrix()).ravel()
- # Refresh the gepetto viewer display
- solo.display(qu_pinocchio)
diff --git a/src/Controller.cpp b/src/Controller.cpp
index e9b787c4..6404dd5f 100644
--- a/src/Controller.cpp
+++ b/src/Controller.cpp
@@ -2,6 +2,10 @@
#include "pinocchio/algorithm/crba.hpp"
#include "pinocchio/math/rpy.hpp"
+#include "pinocchio/math/rpy.hpp"
+#include "pinocchio/parsers/urdf.hpp"
+#include "pinocchio/algorithm/compute-all-terms.hpp"
+#include "pinocchio/algorithm/frames.hpp"
Controller::Controller()
: P(Vector12::Zero()),
diff --git a/src/FootstepPlanner.cpp b/src/FootstepPlanner.cpp
index e0fb9d45..fda686c3 100644
--- a/src/FootstepPlanner.cpp
+++ b/src/FootstepPlanner.cpp
@@ -1,5 +1,10 @@
#include <example-robot-data/path.hpp>
+#include "pinocchio/math/rpy.hpp"
+#include "pinocchio/parsers/urdf.hpp"
+#include "pinocchio/algorithm/compute-all-terms.hpp"
+#include "pinocchio/algorithm/frames.hpp"
+
#include "qrw/FootstepPlanner.hpp"
FootstepPlanner::FootstepPlanner()
diff --git a/src/Joystick.cpp b/src/Joystick.cpp
index d29885eb..c4c02f4a 100644
--- a/src/Joystick.cpp
+++ b/src/Joystick.cpp
@@ -1,5 +1,9 @@
#include "qrw/Joystick.hpp"
+#include <fcntl.h>
+#include <stdio.h>
+#include <unistd.h>
+
Joystick::Joystick()
: A3_(Vector6::Zero()),
A2_(Vector6::Zero()),
@@ -9,6 +13,12 @@ Joystick::Joystick()
v_gp_(Vector6::Zero()),
v_ref_heavy_filter_(Vector6::Zero()) {}
+Joystick::~Joystick() {
+ if (js != -1) {
+ close(js);
+ }
+}
+
void Joystick::initialize(Params& params) {
params_ = ¶ms;
dt_wbc = params.dt_wbc;
diff --git a/src/MpcWrapper.cpp b/src/MpcWrapper.cpp
index b781d5a8..1aa24e55 100644
--- a/src/MpcWrapper.cpp
+++ b/src/MpcWrapper.cpp
@@ -1,4 +1,7 @@
#include "qrw/MpcWrapper.hpp"
+#include <chrono>
+#include <thread>
+#include <mutex>
// Shared global variables
Params* shared_params = nullptr; // Shared pointer to object that stores parameters
diff --git a/src/FootTrajectoryGeneratorBezier.cpp b/src/Solo3D/FootTrajectoryGeneratorBezier.cpp
similarity index 99%
rename from src/FootTrajectoryGeneratorBezier.cpp
rename to src/Solo3D/FootTrajectoryGeneratorBezier.cpp
index 47f7aa87..381b39bf 100644
--- a/src/FootTrajectoryGeneratorBezier.cpp
+++ b/src/Solo3D/FootTrajectoryGeneratorBezier.cpp
@@ -1,6 +1,10 @@
#include <example-robot-data/path.hpp>
-#include "qrw/FootTrajectoryGeneratorBezier.hpp"
+#include "pinocchio/math/rpy.hpp"
+#include "pinocchio/parsers/urdf.hpp"
+#include "pinocchio/algorithm/compute-all-terms.hpp"
+#include "pinocchio/algorithm/frames.hpp"
+#include "qrw/Solo3D/FootTrajectoryGeneratorBezier.hpp"
#include <chrono>
diff --git a/src/FootstepPlannerQP.cpp b/src/Solo3D/FootstepPlannerQP.cpp
similarity index 98%
rename from src/FootstepPlannerQP.cpp
rename to src/Solo3D/FootstepPlannerQP.cpp
index c290c31a..c4195bf0 100644
--- a/src/FootstepPlannerQP.cpp
+++ b/src/Solo3D/FootstepPlannerQP.cpp
@@ -1,6 +1,11 @@
#include <example-robot-data/path.hpp>
-#include "qrw/FootstepPlannerQP.hpp"
+#include "pinocchio/math/rpy.hpp"
+#include "pinocchio/parsers/urdf.hpp"
+#include "pinocchio/algorithm/compute-all-terms.hpp"
+#include "pinocchio/algorithm/frames.hpp"
+
+#include "qrw/Solo3D/FootstepPlannerQP.hpp"
FootstepPlannerQP::FootstepPlannerQP()
: gait_(NULL),
diff --git a/src/Heightmap.cpp b/src/Solo3D/Heightmap.cpp
similarity index 98%
rename from src/Heightmap.cpp
rename to src/Solo3D/Heightmap.cpp
index 2ebb340a..b460258b 100644
--- a/src/Heightmap.cpp
+++ b/src/Solo3D/Heightmap.cpp
@@ -1,4 +1,4 @@
-#include "qrw/Heightmap.hpp"
+#include "qrw/Solo3D/Heightmap.hpp"
Heightmap::Heightmap()
: z_(),
diff --git a/src/StatePlanner3D.cpp b/src/Solo3D/StatePlanner3D.cpp
similarity index 96%
rename from src/StatePlanner3D.cpp
rename to src/Solo3D/StatePlanner3D.cpp
index ef1cc62f..1eb8977c 100644
--- a/src/StatePlanner3D.cpp
+++ b/src/Solo3D/StatePlanner3D.cpp
@@ -1,4 +1,9 @@
-#include "qrw/StatePlanner3D.hpp"
+#include "qrw/Solo3D/StatePlanner3D.hpp"
+
+#include "pinocchio/math/rpy.hpp"
+#include "pinocchio/parsers/urdf.hpp"
+#include "pinocchio/algorithm/compute-all-terms.hpp"
+#include "pinocchio/algorithm/frames.hpp"
StatePlanner3D::StatePlanner3D()
: nStates_(0),
diff --git a/src/Surface.cpp b/src/Solo3D/Surface.cpp
similarity index 96%
rename from src/Surface.cpp
rename to src/Solo3D/Surface.cpp
index 24ab1746..13e05999 100644
--- a/src/Surface.cpp
+++ b/src/Solo3D/Surface.cpp
@@ -1,4 +1,4 @@
-#include "qrw/Surface.hpp"
+#include "qrw/Solo3D/Surface.hpp"
Surface::Surface() {
// Empty
diff --git a/src/st_to_cc.cpp b/src/st_to_cc.cpp
index 7e3869b5..67254e00 100644
--- a/src/st_to_cc.cpp
+++ b/src/st_to_cc.cpp
@@ -1,8 +1,7 @@
#include <stdlib.h>
#include <stdio.h>
-#include <math.h>
#include <time.h>
-#include <string.h>
+#include <math.h>
#include "other/st_to_cc.hpp"
diff --git a/scripts/crocoddyl_eval/README.md b/tests/python/crocoddyl/README.md
similarity index 100%
rename from scripts/crocoddyl_eval/README.md
rename to tests/python/crocoddyl/README.md
diff --git a/scripts/crocoddyl_eval/test_1/analyse_control_cycle.py b/tests/python/crocoddyl/analyse_control_cycle.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_1/analyse_control_cycle.py
rename to tests/python/crocoddyl/analyse_control_cycle.py
diff --git a/scripts/crocoddyl_eval/test_3/analyse_control_cycle.py b/tests/python/crocoddyl/analyse_control_cycle_2.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_3/analyse_control_cycle.py
rename to tests/python/crocoddyl/analyse_control_cycle_2.py
diff --git a/scripts/crocoddyl_eval/test_4/analyse_simu.py b/tests/python/crocoddyl/analyse_simu.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_4/analyse_simu.py
rename to tests/python/crocoddyl/analyse_simu.py
diff --git a/scripts/crocoddyl_eval/test_5/analyse_simu.py b/tests/python/crocoddyl/analyse_simu_2.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_5/analyse_simu.py
rename to tests/python/crocoddyl/analyse_simu_2.py
diff --git a/scripts/crocoddyl_eval/test_6/compare_mpcs.py b/tests/python/crocoddyl/compare_mpcs.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_6/compare_mpcs.py
rename to tests/python/crocoddyl/compare_mpcs.py
diff --git a/scripts/crocoddyl_eval/test_4/main.py b/tests/python/crocoddyl/main.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_4/main.py
rename to tests/python/crocoddyl/main.py
diff --git a/scripts/crocoddyl_eval/test_5/main.py b/tests/python/crocoddyl/main_2.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_5/main.py
rename to tests/python/crocoddyl/main_2.py
diff --git a/scripts/crocoddyl_eval/test_4/run_scenarios.py b/tests/python/crocoddyl/run_scenarios.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_4/run_scenarios.py
rename to tests/python/crocoddyl/run_scenarios.py
diff --git a/scripts/crocoddyl_eval/test_5/run_scenarios.py b/tests/python/crocoddyl/run_scenarios_2.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_5/run_scenarios.py
rename to tests/python/crocoddyl/run_scenarios_2.py
diff --git a/scripts/crocoddyl_eval/test_2/unittest_augmented.py b/tests/python/crocoddyl/unittest_augmented.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_2/unittest_augmented.py
rename to tests/python/crocoddyl/unittest_augmented.py
diff --git a/scripts/crocoddyl_eval/test_2/unittest_linear.py b/tests/python/crocoddyl/unittest_linear.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_2/unittest_linear.py
rename to tests/python/crocoddyl/unittest_linear.py
diff --git a/scripts/crocoddyl_eval/test_2/unittest_nonlinear.py b/tests/python/crocoddyl/unittest_nonlinear.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_2/unittest_nonlinear.py
rename to tests/python/crocoddyl/unittest_nonlinear.py
diff --git a/scripts/crocoddyl_eval/test_2/unittest_step.py b/tests/python/crocoddyl/unittest_step.py
similarity index 100%
rename from scripts/crocoddyl_eval/test_2/unittest_step.py
rename to tests/python/crocoddyl/unittest_step.py
--
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