'''This class will log 1d array in Nd matrix from device and qualisys object'''
import numpy as np
from datetime import datetime as datetime
from time import time
import pinocchio as pin
class LoggerControl():
def __init__(self, dt, N0_gait, joystick=None, estimator=None, loop=None, gait=None, statePlanner=None,
footstepPlanner=None, footTrajectoryGenerator=None, logSize=60e3, ringBuffer=False):
self.ringBuffer = ringBuffer
logSize = np.int(logSize)
self.logSize = logSize
self.i = 0
self.dt = dt
# Allocate the data:
# Joystick
self.joy_v_ref = np.zeros([logSize, 6]) # reference velocity of the joystick
# Estimator
self.esti_feet_status = np.zeros([logSize, 4]) # input feet status (contact or not)
self.esti_feet_goals = np.zeros([logSize, 3, 4]) # input feet goals (desired on the ground)
self.esti_q_filt = np.zeros([logSize, 19]) # output position
self.esti_v_filt = np.zeros([logSize, 18]) # output velocity
self.esti_v_secu = np.zeros([logSize, 12]) # filtered output velocity for security check
self.esti_FK_lin_vel = np.zeros([logSize, 3]) # estimated velocity of the base with FK
self.esti_FK_xyz = np.zeros([logSize, 3]) # estimated position of the base with FK
self.esti_xyz_mean_feet = np.zeros([logSize, 3]) # average of feet goals
self.esti_filt_lin_vel = np.zeros([logSize, 3]) # estimated velocity of the base before low pass filter
self.esti_HP_x = np.zeros([logSize, 3]) # x input of the velocity complementary filter
self.esti_HP_dx = np.zeros([logSize, 3]) # dx input of the velocity complementary filter
self.esti_HP_alpha = np.zeros([logSize, 3]) # alpha parameter of the velocity complementary filter
self.esti_HP_filt_x = np.zeros([logSize, 3]) # filtered output of the velocity complementary filter
self.esti_LP_x = np.zeros([logSize, 3]) # x input of the position complementary filter
self.esti_LP_dx = np.zeros([logSize, 3]) # dx input of the position complementary filter
self.esti_LP_alpha = np.zeros([logSize, 3]) # alpha parameter of the position complementary filter
self.esti_LP_filt_x = np.zeros([logSize, 3]) # filtered output of the position complementary filter
self.esti_kf_X = np.zeros([logSize, 18]) # state of the Kalman filter
self.esti_kf_Z = np.zeros([logSize, 16]) # measurement for the Kalman filter
# Loop
self.loop_o_q_int = np.zeros([logSize, 18]) # position in world frame (esti_q_filt + dt * loop_o_v)
self.loop_o_v = np.zeros([logSize, 18]) # estimated velocity in world frame
self.loop_h_v = np.zeros([logSize, 18]) # estimated velocity in horizontal frame
self.loop_h_v_bis = np.zeros([logSize, 6]) # estimated velocity in horizontal frame
self.loop_pos_virtual_world = np.zeros([logSize, 3]) # x, y, yaw perfect position in world
self.loop_t_filter = np.zeros([logSize]) # time taken by the estimator
self.loop_t_planner = np.zeros([logSize]) # time taken by the planning
self.loop_t_mpc = np.zeros([logSize]) # time taken by the mcp
self.loop_t_wbc = np.zeros([logSize]) # time taken by the whole body control
self.loop_t_loop = np.zeros([logSize]) # time taken by the whole loop (without interface)
self.loop_t_loop_if = np.zeros([logSize]) # time taken by the whole loop (with interface)
self.loop_q_filt_mpc = np.zeros([logSize, 6]) # position in world frame filtered by 1st order low pass
self.loop_h_v_filt_mpc = np.zeros([logSize, 6]) # vel in base frame filtered by 1st order low pass
self.loop_h_v_bis_filt_mpc = np.zeros([logSize, 6]) # alt vel in base frame filtered by 1st order low pass
self.loop_vref_filt_mpc = np.zeros([logSize, 6]) # ref vel in base frame filtered by 1st order low pass
# Gait
self.planner_gait = np.zeros([logSize, N0_gait, 4]) # Gait sequence
self.planner_is_static = np.zeros([logSize]) # if the planner is in static mode or not
self.planner_q_static = np.zeros([logSize, 19]) # position in static mode (4 stance phase)
self.planner_RPY_static = np.zeros([logSize, 3]) # RPY orientation in static mode (4 stance phase)
# State planner
if statePlanner is not None:
self.planner_xref = np.zeros([logSize, 12, 1+statePlanner.getNSteps()]) # Reference trajectory
# Footstep planner
if gait is not None:
self.planner_fsteps = np.zeros([logSize, gait.getCurrentGait().shape[0], 12]) # Reference footsteps position
self.planner_h_ref = np.zeros([logSize]) # reference height of the planner
# Foot Trajectory Generator
self.planner_goals = np.zeros([logSize, 3, 4]) # 3D target feet positions
self.planner_vgoals = np.zeros([logSize, 3, 4]) # 3D target feet velocities
self.planner_agoals = np.zeros([logSize, 3, 4]) # 3D target feet accelerations
# Model Predictive Control
# output vector of the MPC (next state + reference contact force)
if statePlanner is not None:
if loop.type_MPC == 3:
self.mpc_x_f = np.zeros([logSize, 32, statePlanner.getNSteps()])
else:
self.mpc_x_f = np.zeros([logSize, 24, statePlanner.getNSteps()])
self.mpc_solving_duration = np.zeros([logSize])
# Whole body control
self.wbc_P = np.zeros([logSize, 12]) # proportionnal gains of the PD+
self.wbc_D = np.zeros([logSize, 12]) # derivative gains of the PD+
self.wbc_q_des = np.zeros([logSize, 12]) # desired position of actuators
self.wbc_v_des = np.zeros([logSize, 12]) # desired velocity of actuators
self.wbc_tau_ff = np.zeros([logSize, 12]) # feedforward torques computed by the WBC
self.wbc_f_ctc = np.zeros([logSize, 12]) # contact forces computed by the WBC
self.wbc_feet_pos = np.zeros([logSize, 3, 4]) # current feet positions according to WBC
self.wbc_feet_pos_target = np.zeros([logSize, 3, 4]) # current feet positions targets for WBC
self.wbc_feet_err = np.zeros([logSize, 3, 4]) # error between feet positions and their reference
self.wbc_feet_vel = np.zeros([logSize, 3, 4]) # current feet velocities according to WBC
self.wbc_feet_vel_target = np.zeros([logSize, 3, 4]) # current feet velocities targets for WBC
self.wbc_feet_acc_target = np.zeros([logSize, 3, 4]) # current feet accelerations targets for WBC
# Timestamps
self.tstamps = np.zeros(logSize)
def sample(self, joystick, estimator, loop, gait, statePlanner, footstepPlanner, footTrajectoryGenerator, wbc, dT_whole):
if (self.i >= self.logSize):
if self.ringBuffer:
self.i = 0
else:
return
# Logging from joystick
self.joy_v_ref[self.i] = joystick.v_ref[:, 0]
# Logging from estimator
self.esti_feet_status[self.i] = estimator.getFeetStatus()
self.esti_feet_goals[self.i] = estimator.getFeetGoals()
self.esti_q_filt[self.i] = estimator.getQFilt()
self.esti_v_filt[self.i] = estimator.getVFilt()
self.esti_v_secu[self.i] = estimator.getVSecu()
self.esti_FK_lin_vel[self.i] = estimator.getFKLinVel()
self.esti_FK_xyz[self.i] = estimator.getFKXYZ()
self.esti_xyz_mean_feet[self.i] = estimator.getXYZMeanFeet()
self.esti_filt_lin_vel[self.i] = estimator.getFiltLinVel()
self.esti_HP_x[self.i] = estimator.getFilterVelX()
self.esti_HP_dx[self.i] = estimator.getFilterVelDX()
self.esti_HP_alpha[self.i] = estimator.getFilterVelAlpha()
self.esti_HP_filt_x[self.i] = estimator.getFilterVelFiltX()
self.esti_LP_x[self.i] = estimator.getFilterPosX()
self.esti_LP_dx[self.i] = estimator.getFilterPosDX()
self.esti_LP_alpha[self.i] = estimator.getFilterPosAlpha()
self.esti_LP_filt_x[self.i] = estimator.getFilterPosFiltX()
# Logging from the main loop
self.loop_o_q_int[self.i] = loop.q[:, 0]
self.loop_o_v[self.i] = loop.v[:, 0]
self.loop_h_v[self.i] = loop.h_v[:, 0]
self.loop_h_v_bis[self.i] = loop.h_v_bis[:, 0]
self.loop_pos_virtual_world[self.i] = np.array([loop.q[0, 0], loop.q[1, 0], loop.yaw_estim])
self.loop_t_filter[self.i] = loop.t_filter
self.loop_t_planner[self.i] = loop.t_planner
self.loop_t_mpc[self.i] = loop.t_mpc
self.loop_t_wbc[self.i] = loop.t_wbc
self.loop_t_loop[self.i] = loop.t_loop
self.loop_t_loop_if[self.i] = dT_whole
self.loop_q_filt_mpc[self.i] = loop.q_filt_mpc[:6, 0]
self.loop_h_v_filt_mpc[self.i] = loop.h_v_filt_mpc[:, 0]
self.loop_h_v_bis_filt_mpc[self.i] = loop.h_v_bis_filt_mpc[:, 0]
self.loop_vref_filt_mpc[self.i] = loop.vref_filt_mpc[:, 0]
# Logging from the planner
# self.planner_q_static[self.i] = planner.q_static[:]
# self.planner_RPY_static[self.i] = planner.RPY_static[:, 0]
self.planner_xref[self.i] = statePlanner.getReferenceStates()
self.planner_fsteps[self.i] = footstepPlanner.getFootsteps()
self.planner_gait[self.i] = gait.getCurrentGait()
self.planner_goals[self.i] = footTrajectoryGenerator.getFootPosition()
self.planner_vgoals[self.i] = footTrajectoryGenerator.getFootVelocity()
self.planner_agoals[self.i] = footTrajectoryGenerator.getFootAcceleration()
self.planner_is_static[self.i] = gait.getIsStatic()
self.planner_h_ref[self.i] = loop.h_ref
# Logging from model predictive control
self.mpc_x_f[self.i] = loop.x_f_mpc
self.mpc_solving_duration[self.i] = loop.mpc_wrapper.t_mpc_solving_duration
# Logging from whole body control
self.wbc_P[self.i] = loop.result.P
self.wbc_D[self.i] = loop.result.D
self.wbc_q_des[self.i] = loop.result.q_des
self.wbc_v_des[self.i] = loop.result.v_des
self.wbc_tau_ff[self.i] = loop.result.tau_ff
self.wbc_f_ctc[self.i] = wbc.f_with_delta
self.wbc_feet_pos[self.i] = wbc.feet_pos
self.wbc_feet_pos_target[self.i] = wbc.feet_pos_target
self.wbc_feet_err[self.i] = wbc.feet_err
self.wbc_feet_vel[self.i] = wbc.feet_vel
self.wbc_feet_vel_target[self.i] = wbc.feet_vel_target
self.wbc_feet_acc_target[self.i] = wbc.feet_acc_target
# Logging timestamp
self.tstamps[self.i] = time()
self.i += 1
def processMocap(self, N, loggerSensors):
self.mocap_pos = np.zeros([N, 3])
self.mocap_h_v = np.zeros([N, 3])
self.mocap_b_w = np.zeros([N, 3])
self.mocap_RPY = np.zeros([N, 3])
for i in range(N):
self.mocap_RPY[i] = pin.rpy.matrixToRpy(pin.Quaternion(loggerSensors.mocapOrientationQuat[i]).toRotationMatrix())
# Robot world to Mocap initial translationa and rotation
mTo = np.array([loggerSensors.mocapPosition[0, 0], loggerSensors.mocapPosition[0, 1], 0.02])
mRo = pin.rpy.rpyToMatrix(0.0, 0.0, self.mocap_RPY[0, 2])
for i in range(N):
oRb = loggerSensors.mocapOrientationMat9[i]
oRh = pin.rpy.rpyToMatrix(0.0, 0.0, self.mocap_RPY[i, 2] - self.mocap_RPY[0, 2])
self.mocap_h_v[i] = (oRh.transpose() @ mRo.transpose() @ loggerSensors.mocapVelocity[i].reshape((3, 1))).ravel()
self.mocap_b_w[i] = (oRb.transpose() @ loggerSensors.mocapAngularVelocity[i].reshape((3, 1))).ravel()
self.mocap_pos[i] = (mRo.transpose() @ (loggerSensors.mocapPosition[i, :] - mTo).reshape((3, 1))).ravel()
def plotAll(self, loggerSensors):
from matplotlib import pyplot as plt
N = self.tstamps.shape[0]
t_range = np.array([k*self.dt for k in range(N)])
self.processMocap(N, loggerSensors)
index6 = [1, 3, 5, 2, 4, 6]
index12 = [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]
"""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)
switch = np.diff(self.esti_feet_status[:, i])
tmp = self.wbc_feet_pos[:-1, 2, i]
tmp_y = tmp[switch > 0]
tmp_x = t_range[:-1]
tmp_x = tmp_x[switch > 0]
plt.plot(tmp_x, tmp_y, linewidth=3)"""
from example_robot_data.robots_loader import Solo12Loader
Solo12Loader.free_flyer = False
solo12 = Solo12Loader().robot
FL_FOOT_ID = solo12.model.getFrameId('FL_FOOT')
FR_FOOT_ID = solo12.model.getFrameId('FR_FOOT')
HL_FOOT_ID = solo12.model.getFrameId('HL_FOOT')
HR_FOOT_ID = solo12.model.getFrameId('HR_FOOT')
foot_ids = np.array([FL_FOOT_ID, FR_FOOT_ID, HL_FOOT_ID, HR_FOOT_ID])
q = np.zeros((12, 1))
pin.computeAllTerms(solo12.model, solo12.data, q, np.zeros((12, 1)))
feet_pos = np.zeros([self.esti_q_filt.shape[0], 3, 4])
for i in range(self.esti_q_filt.shape[0]):
q[:, 0] = self.loop_o_q_int[i, 6:]
pin.forwardKinematics(solo12.model, solo12.data, q)
pin.updateFramePlacements(solo12.model, solo12.data)
for j, idx in enumerate(foot_ids):
feet_pos[i, :, j] = solo12.data.oMf[int(idx)].translation
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)
plt.plot(t_range, self.wbc_feet_pos[:, i % 3, np.int(i/3)], color='b', linewidth=3, marker='')
plt.plot(t_range, self.wbc_feet_err[:, i % 3, np.int(i/3)] + self.wbc_feet_pos[0, i % 3, np.int(i/3)], color='g', linewidth=3, marker='')
plt.plot(t_range, self.wbc_feet_pos_target[:, i % 3, np.int(i/3)], color='r', linewidth=3, marker='')
plt.plot(t_range, feet_pos[:, i % 3, np.int(i/3)], color='rebeccapurple', linewidth=3, marker='')
if (i % 3) == 2:
mini = np.min(self.wbc_feet_pos[:, i % 3, np.int(i/3)])
maxi = np.max(self.wbc_feet_pos[:, i % 3, np.int(i/3)])
plt.plot(t_range, self.planner_gait[:, 0, np.int(
i/3)] * (maxi - mini) + mini, color='k', linewidth=3, marker='')
plt.legend([lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+"", "error",
lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+" Ref",
lgd_Y[i % 3] + " " + lgd_X[np.int(i/3)]+" Robot", "Contact state"], prop={'size': 8})
plt.suptitle("Measured & Reference feet positions (base 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)
plt.plot(t_range, self.wbc_feet_vel[:, i % 3, np.int(i/3)], color='b', linewidth=3, marker='')
plt.plot(t_range, self.wbc_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"], prop={'size': 8})
plt.suptitle("Measured and Reference feet velocities (base 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)
plt.plot(t_range, self.wbc_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)]+" Ref"], prop={'size': 8})
plt.suptitle("Reference feet accelerations (base 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)
if i in [0, 1]:
plt.plot(t_range, self.loop_pos_virtual_world[:, i], "b", linewidth=3)
plt.plot(t_range, self.loop_pos_virtual_world[:, i], "r", linewidth=3)
elif i == 5:
plt.plot(t_range, self.loop_pos_virtual_world[:, 2], "b", linewidth=3)
plt.plot(t_range, self.loop_pos_virtual_world[:, 2], "r", linewidth=3)
else:
plt.plot(t_range, self.planner_xref[:, i, 0], "b", linewidth=2)
plt.plot(t_range, self.planner_xref[:, i, 1], "r", linewidth=3)
if i < 3:
plt.plot(t_range, self.mocap_pos[:, i], "k", linewidth=3)
else:
plt.plot(t_range, self.mocap_RPY[:, i-3], "k", linewidth=3)
# plt.plot(t_range, self.log_q[i, :], "grey", linewidth=4)
# plt.plot(t_range[:-2], self.log_x_invkin[i, :-2], "g", linewidth=2)
# plt.plot(t_range[:-2], self.log_x_ref_invkin[i, :-2], "violet", linewidth=2, linestyle="--")
plt.legend(["Robot state", "Robot reference state", "Ground truth"], prop={'size': 8})
plt.ylabel(lgd[i])
plt.suptitle("Measured & Reference position and orientation")
# 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)
plt.plot(t_range, self.loop_h_v[:, i], "b", linewidth=2)
plt.plot(t_range, self.joy_v_ref[:, i], "r", linewidth=3)
if i < 3:
plt.plot(t_range, self.mocap_h_v[:, i], "k", linewidth=3)
plt.plot(t_range, self.loop_h_v_bis_filt_mpc[:, i], "forestgreen", linewidth=2)
else:
plt.plot(t_range, self.mocap_b_w[:, i-3], "k", linewidth=3)
"""N = 2000
y = np.convolve(self.mocap_b_w[:, i-3], np.ones(N)/N, mode='valid')
plt.plot(t_range[int(N/2)-1:-int(N/2)], y, linewidth=3, linestyle="--")"""
# plt.plot(t_range, self.log_dq[i, :], "g", linewidth=2)
# plt.plot(t_range[:-2], self.log_dx_invkin[i, :-2], "g", linewidth=2)
# plt.plot(t_range[:-2], self.log_dx_ref_invkin[i, :-2], "violet", linewidth=2, linestyle="--")
plt.legend(["Robot state", "Robot reference state", "Ground truth", "Alternative"], prop={'size': 8})
plt.ylabel(lgd[i])
plt.suptitle("Measured & Reference linear and angular velocities")
"""plt.figure()
plt.plot(t_range[:-2], self.log_x[6, :-2], "b", linewidth=2)
plt.plot(t_range[:-2], self.log_x_cmd[6, :-2], "r", linewidth=2)
plt.plot(t_range[:-2], self.log_dx_invkin[0, :-2], "g", linewidth=2)
plt.plot(t_range[:-2], self.log_dx_ref_invkin[0, :-2], "violet", linewidth=2)
plt.legend(["WBC integrated output state", "Robot reference state",
"Task current state", "Task reference state"])"""
# Analysis of the footstep locations (current and future) with a slider to move along time
# self.slider_predicted_footholds()
# Analysis of the footholds locations during the whole experiment
"""import utils_mpc
import pinocchio as pin
f_c = ["r", "b", "forestgreen", "rebeccapurple"]
quat = np.zeros((4, 1))
steps = np.zeros((12, 1))
o_step = np.zeros((3, 1))
plt.figure()
plt.plot(self.loop_o_q_int[:, 0], self.loop_o_q_int[:, 1], linewidth=2, color="k")
for i in range(self.planner_fsteps.shape[0]):
fsteps = self.planner_fsteps[i]
RPY = utils_mpc.quaternionToRPY(self.loop_o_q_int[i, 3:7])
quat[:, 0] = utils_mpc.EulerToQuaternion([0.0, 0.0, RPY[2]])
oRh = pin.Quaternion(quat).toRotationMatrix()
for j in range(4):
#if np.any(fsteps[k, (j*3):((j+1)*3)]) and not np.array_equal(steps[(j*3):((j+1)*3), 0],
# fsteps[k, (j*3):((j+1)*3)]):
# steps[(j*3):((j+1)*3), 0] = fsteps[k, (j*3):((j+1)*3)]
# o_step[:, 0:1] = oRh @ steps[(j*3):((j+1)*3), 0:1] + self.loop_o_q_int[i:(i+1), 0:3].transpose()
o_step[:, 0:1] = oRh @ fsteps[0:1, (j*3):((j+1)*3)].transpose() + self.loop_o_q_int[i:(i+1), 0:3].transpose()
plt.plot(o_step[0, 0], o_step[1, 0], linestyle=None, linewidth=1, marker="o", color=f_c[j])
"""
lgd1 = ["HAA", "HFE", "Knee"]
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)
tau_fb = self.wbc_P[:, i] * (self.wbc_q_des[:, i] - self.esti_q_filt[:, 7+i]) + \
self.wbc_D[:, i] * (self.wbc_v_des[:, i] - self.esti_v_filt[:, 6+i])
h1, = plt.plot(t_range, self.wbc_tau_ff[:, i], "r", linewidth=3)
h2, = plt.plot(t_range, tau_fb, "b", linewidth=3)
h3, = plt.plot(t_range, self.wbc_tau_ff[:, i] + tau_fb, "g", linewidth=3)
h4, = plt.plot(t_range[:-1], loggerSensors.torquesFromCurrentMeasurment[1:, i],
"violet", linewidth=3, linestyle="--")
plt.xlabel("Time [s]")
plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [Nm]")
tmp = lgd1[i % 3]+" "+lgd2[int(i/3)]
plt.legend([h1, h2, h3, h4], ["FF "+tmp, "FB "+tmp, "PD+ "+tmp, "Meas "+tmp], prop={'size': 8})
plt.ylim([-8.0, 8.0])
plt.suptitle("FF torques & FB torques & Sent torques & Meas torques")
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(t_range, self.mpc_x_f[:, 12+i, 0], "r", linewidth=3)
h2, = plt.plot(t_range, self.wbc_f_ctc[:, i], "b", linewidth=3, linestyle="--")
plt.xlabel("Time [s]")
plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [N]")
plt.legend([h1, h2], ["MPC " + lgd1[i % 3]+" "+lgd2[int(i/3)],
"WBC " + lgd1[i % 3]+" "+lgd2[int(i/3)]], prop={'size': 8})
if (i % 3) == 2:
plt.ylim([-0.0, 26.0])
else:
plt.ylim([-26.0, 26.0])
plt.suptitle("Contact forces (MPC command) & WBC QP output")
lgd1 = ["HAA", "HFE", "Knee"]
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(t_range, self.wbc_q_des[:, i], color='r', linewidth=3)
h2, = plt.plot(t_range, self.esti_q_filt[:, 7+i], color='b', linewidth=3)
plt.xlabel("Time [s]")
plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [rad]")
plt.legend([h1, h2], ["Ref "+lgd1[i % 3]+" "+lgd2[int(i/3)],
lgd1[i % 3]+" "+lgd2[int(i/3)]], prop={'size': 8})
plt.suptitle("Desired actuator positions & Measured actuator positions")
# Evolution of predicted trajectory along time
log_t_pred = np.array([k*self.dt*10 for k in range(self.mpc_x_f.shape[2])])
log_t_ref = np.array([k*self.dt*10 for k in range(self.planner_xref.shape[2])])
"""from IPython import embed
embed()"""
titles = ["X", "Y", "Z", "Roll", "Pitch", "Yaw"]
step = 1000
plt.figure()
for j in range(6):
plt.subplot(3, 2, index6[j])
c = [[i/(self.mpc_x_f.shape[0]+5), 0.0, i/(self.mpc_x_f.shape[0]+5)]
for i in range(0, self.mpc_x_f.shape[0], step)]
for i in range(0, self.mpc_x_f.shape[0], step):
h1, = plt.plot(log_t_pred+(i+10)*self.dt,
self.mpc_x_f[i, j, :], "b", linewidth=2, color=c[int(i/step)])
h2, = plt.plot(log_t_ref+i*self.dt,
self.planner_xref[i, j, :], linestyle="--", marker='x', color="g", linewidth=2)
#h3, = plt.plot(np.array([k*self.dt for k in range(self.mpc_x_f.shape[0])]),
# self.planner_xref[:, j, 0], linestyle=None, marker='x', color="r", linewidth=1)
plt.xlabel("Time [s]")
plt.legend([h1, h2, h3], ["Output trajectory of MPC",
"Input trajectory of planner"]) #, "Actual robot trajectory"])
plt.title("Predicted trajectory for " + titles[j])
plt.suptitle("Analysis of trajectories in position and orientation computed by the MPC")
plt.figure()
for j in range(6):
plt.subplot(3, 2, index6[j])
c = [[i/(self.mpc_x_f.shape[0]+5), 0.0, i/(self.mpc_x_f.shape[0]+5)]
for i in range(0, self.mpc_x_f.shape[0], step)]
for i in range(0, self.mpc_x_f.shape[0], step):
h1, = plt.plot(log_t_pred+(i+10)*self.dt,
self.mpc_x_f[i, j+6, :], "b", linewidth=2, color=c[int(i/step)])
h2, = plt.plot(log_t_ref+i*self.dt,
self.planner_xref[i, j+6, :], linestyle="--", marker='x', color="g", linewidth=2)
h3, = plt.plot(np.array([k*self.dt for k in range(self.mpc_x_f.shape[0])]),
self.planner_xref[:, j+6, 0], linestyle=None, marker='x', color="r", linewidth=1)
plt.xlabel("Time [s]")
plt.legend([h1, h2, h3], ["Output trajectory of MPC",
"Input trajectory of planner", "Actual robot trajectory"])
plt.title("Predicted trajectory for velocity in " + titles[j])
plt.suptitle("Analysis of trajectories of linear and angular velocities computed by the MPC")
step = 1000
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(t_range, self.mpc_x_f[:, 12+i, 0], "r", linewidth=3)
h2, = plt.plot(t_range, self.wbc_f_ctc[:, i], "b", linewidth=3, linestyle="--")
plt.xlabel("Time [s]")
plt.ylabel(lgd1[i % 3]+" "+lgd2[int(i/3)]+" [N]")
plt.legend([h1, h2], ["MPC " + lgd1[i % 3]+" "+lgd2[int(i/3)],
"WBC " + lgd1[i % 3]+" "+lgd2[int(i/3)]], prop={'size': 8})
if (i % 3) == 2:
plt.ylim([-0.0, 26.0])
else:
plt.ylim([-26.0, 26.0])
plt.suptitle("Contact forces (MPC command) & WBC QP output")
lgd1 = ["Ctct force X", "Ctct force Y", "Ctct force Z"]
lgd2 = ["FL", "FR", "HL", "HR"]
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)
for k in range(0, self.mpc_x_f.shape[0], step):
h2, = plt.plot(log_t_pred+k*self.dt, self.mpc_x_f[k, 12+(3*i+2), :], linestyle="--", marker='x', linewidth=2)
h1, = plt.plot(t_range, self.mpc_x_f[:, 12+(3*i+2), 0], "r", linewidth=3)
# h3, = plt.plot(t_range, self.wbc_f_ctc[:, i], "b", linewidth=3, linestyle="--")
plt.plot(t_range, self.esti_feet_status[:, i], "k", linestyle="--")
plt.xlabel("Time [s]")
plt.ylabel(lgd2[i]+" [N]")
plt.legend([h1, h2], ["MPC "+lgd2[i],
"MPC "+lgd2[i]+" trajectory"])
plt.ylim([-1.0, 26.0])
plt.suptitle("Contact forces trajectories & Actual forces trajectories")
# Analysis of the complementary filter behaviour
clr = ["b", "darkred", "forestgreen"]
# Velocity complementary filter
lgd_Y = ["dx", "ddx", "alpha dx", "dx_out", "dy", "ddy", "alpha dy", "dy_out", "dz", "ddz", "alpha dz", "dz_out"]
plt.figure()
for i in range(12):
if i == 0:
ax0 = plt.subplot(3, 4, i+1)
else:
plt.subplot(3, 4, i+1, sharex=ax0)
if i % 4 == 0:
plt.plot(t_range, self.esti_HP_x[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # x input of the velocity complementary filter
elif i % 4 == 1:
plt.plot(t_range, self.esti_HP_dx[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # dx input of the velocity complementary filter
elif i % 4 == 2:
plt.plot(t_range, self.esti_HP_alpha[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # alpha parameter of the velocity complementary filter
else:
plt.plot(t_range, self.esti_HP_filt_x[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # filtered output of the velocity complementary filter
plt.legend([lgd_Y[i]], prop={'size': 8})
plt.suptitle("Evolution of the quantities of the velocity complementary filter")
# Position complementary filter
lgd_Y = ["x", "dx", "alpha x", "x_out", "y", "dy", "alpha y", "y_out", "z", "dz", "alpha z", "z_out"]
plt.figure()
for i in range(12):
if i == 0:
ax0 = plt.subplot(3, 4, i+1)
else:
plt.subplot(3, 4, i+1, sharex=ax0)
if i % 4 == 0:
plt.plot(t_range, self.esti_LP_x[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # x input of the position complementary filter
elif i % 4 == 1:
plt.plot(t_range, self.esti_LP_dx[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # dx input of the position complementary filter
elif i % 4 == 2:
plt.plot(t_range, self.esti_LP_alpha[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # alpha parameter of the position complementary filter
else:
plt.plot(t_range, self.esti_LP_filt_x[:, int(i/4)], color=clr[int(i/4)], linewidth=3, marker='') # filtered output of the position complementary filter
plt.legend([lgd_Y[i]], prop={'size': 8})
plt.suptitle("Evolution of the quantities of the position complementary filter")
# Power supply profile
plt.figure()
for i in range(3):
if i == 0:
ax0 = plt.subplot(3, 1, i+1)
else:
plt.subplot(3, 1, i+1, sharex=ax0)
if i == 0:
plt.plot(t_range, loggerSensors.current[:], linewidth=2)
plt.ylabel("Bus current [A]")
elif i == 1:
plt.plot(t_range, loggerSensors.voltage[:], linewidth=2)
plt.ylabel("Bus voltage [V]")
else:
plt.plot(t_range, loggerSensors.energy[:], linewidth=2)
plt.ylabel("Bus energy [J]")
plt.xlabel("Time [s]")
# Plot estimated computation time for each step for the control architecture
plt.figure()
plt.plot(t_range, self.loop_t_filter, 'r+')
plt.plot(t_range, self.loop_t_planner, 'g+')
plt.plot(t_range, self.loop_t_mpc, 'b+')
plt.plot(t_range, self.loop_t_wbc, '+', color="violet")
plt.plot(t_range, self.loop_t_loop, 'k+')
plt.plot(t_range, self.loop_t_loop_if, '+', color="rebeccapurple")
plt.legend(["Estimator", "Planner", "MPC", "WBC", "Control loop", "Whole loop"])
plt.xlabel("Time [s]")
plt.ylabel("Time [s]")
# Plot estimated solving time of the model prediction control
plt.figure()
plt.plot(t_range[35:], self.mpc_solving_duration[35:], 'k+')
plt.legend(["Solving duration"])
plt.xlabel("Time [s]")
plt.ylabel("Time [s]")
# Comparison between position quantities before and after 15Hz low-pass filtering
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)
plt.plot(t_range, self.loop_o_q_int[:, i], linewidth=3)
plt.plot(t_range, self.loop_q_filt_mpc[:, i], linewidth=3)
plt.legend(["Estimated", "Estimated 15Hz filt"], prop={'size': 8})
plt.ylabel(lgd[i])
plt.suptitle("Comparison between position quantities before and after 15Hz low-pass filtering")
# Comparison between velocity quantities before and after 15Hz low-pass filtering
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)
plt.plot(t_range, self.loop_h_v[:, i], linewidth=3)
plt.plot(t_range, self.loop_h_v_bis[:, i], linewidth=3)
plt.plot(t_range, self.loop_h_v_filt_mpc[:, i], linewidth=3)
plt.plot(t_range, self.loop_h_v_bis_filt_mpc[:, i], linewidth=3)
plt.plot(t_range, self.loop_vref_filt_mpc[:, i], linewidth=3)
plt.legend(["Estimated", "Estimated 3Hz windowed", "Estimated 15Hz filt",
"Estimated 15Hz filt 3Hz windowed", "Reference 15Hz filt"], prop={'size': 8})
plt.ylabel(lgd[i])
plt.suptitle("Comparison between velocity quantities before and after 15Hz low-pass filtering")
# Display all graphs and wait
plt.show(block=True)
from IPython import embed
embed()
def saveAll(self, loggerSensors, fileName="data"):
date_str = datetime.now().strftime('_%Y_%m_%d_%H_%M')
np.savez(fileName + date_str + ".npz",
joy_v_ref=self.joy_v_ref,
esti_feet_status=self.esti_feet_status,
esti_feet_goals=self.esti_feet_goals,
esti_q_filt=self.esti_q_filt,
esti_v_filt=self.esti_v_filt,
esti_v_secu=self.esti_v_secu,
esti_FK_lin_vel=self.esti_FK_lin_vel,
esti_FK_xyz=self.esti_FK_xyz,
esti_xyz_mean_feet=self.esti_xyz_mean_feet,
esti_filt_lin_vel=self.esti_filt_lin_vel,
esti_HP_x=self.esti_HP_x,
esti_HP_dx=self.esti_HP_dx,
esti_HP_alpha=self.esti_HP_alpha,
esti_HP_filt_x=self.esti_HP_filt_x,
esti_LP_x=self.esti_LP_x,
esti_LP_dx=self.esti_LP_dx,
esti_LP_alpha=self.esti_LP_alpha,
esti_LP_filt_x=self.esti_LP_filt_x,
esti_kf_X=self.esti_kf_X,
esti_kf_Z=self.esti_kf_Z,
loop_o_q_int=self.loop_o_q_int,
loop_o_v=self.loop_o_v,
loop_h_v=self.loop_h_v,
loop_pos_virtual_world=self.loop_pos_virtual_world,
loop_t_filter=self.loop_t_filter,
loop_t_planner=self.loop_t_planner,
loop_t_mpc=self.loop_t_mpc,
loop_t_wbc=self.loop_t_wbc,
loop_t_loop=self.loop_t_loop,
loop_t_loop_if=self.loop_t_loop_if,
loop_q_filt_mpc=self.loop_q_filt_mpc,
loop_h_v_filt_mpc=self.loop_h_v_filt_mpc,
loop_h_v_bis_filt_mpc=self.loop_h_v_bis_filt_mpc,
loop_vref_filt_mpc=self.loop_vref_filt_mpc,
planner_q_static=self.planner_q_static,
planner_RPY_static=self.planner_RPY_static,
planner_xref=self.planner_xref,
planner_fsteps=self.planner_fsteps,
planner_gait=self.planner_gait,
planner_goals=self.planner_goals,
planner_vgoals=self.planner_vgoals,
planner_agoals=self.planner_agoals,
planner_is_static=self.planner_is_static,
planner_h_ref=self.planner_h_ref,
mpc_x_f=self.mpc_x_f,
mpc_solving_duration=self.mpc_solving_duration,
wbc_P=self.wbc_P,
wbc_D=self.wbc_D,
wbc_q_des=self.wbc_q_des,
wbc_v_des=self.wbc_v_des,
wbc_tau_ff=self.wbc_tau_ff,
wbc_f_ctc=self.wbc_f_ctc,
wbc_feet_pos=self.wbc_feet_pos,
wbc_feet_pos_target=self.wbc_feet_pos_target,
wbc_feet_err=self.wbc_feet_err,
wbc_feet_vel=self.wbc_feet_vel,
wbc_feet_vel_target=self.wbc_feet_vel_target,
wbc_feet_acc_target=self.wbc_feet_acc_target,
tstamps=self.tstamps,
q_mes=loggerSensors.q_mes,
v_mes=loggerSensors.v_mes,
baseOrientation=loggerSensors.baseOrientation,
baseAngularVelocity=loggerSensors.baseAngularVelocity,
baseLinearAcceleration=loggerSensors.baseLinearAcceleration,
baseAccelerometer=loggerSensors.baseAccelerometer,
torquesFromCurrentMeasurment=loggerSensors.torquesFromCurrentMeasurment,
mocapPosition=loggerSensors.mocapPosition,
mocapVelocity=loggerSensors.mocapVelocity,
mocapAngularVelocity=loggerSensors.mocapAngularVelocity,
mocapOrientationMat9=loggerSensors.mocapOrientationMat9,
mocapOrientationQuat=loggerSensors.mocapOrientationQuat,
)
def loadAll(self, loggerSensors, fileName=None):
if fileName is None:
import glob
fileName = np.sort(glob.glob('data_2021_*.npz'))[-1] # Most recent file
data = np.load(fileName)
# Load LoggerControl arrays
self.joy_v_ref = data["joy_v_ref"]
self.logSize = self.joy_v_ref.shape[0]
self.esti_feet_status = data["esti_feet_status"]
self.esti_feet_goals = data["esti_feet_goals"]
self.esti_q_filt = data["esti_q_filt"]
self.esti_v_filt = data["esti_v_filt"]
self.esti_v_secu = data["esti_v_secu"]
self.esti_FK_lin_vel = data["esti_FK_lin_vel"]
self.esti_FK_xyz = data["esti_FK_xyz"]
self.esti_xyz_mean_feet = data["esti_xyz_mean_feet"]
self.esti_filt_lin_vel = data["esti_filt_lin_vel"]
self.esti_HP_x = data["esti_HP_x"]
self.esti_HP_dx = data["esti_HP_dx"]
self.esti_HP_alpha = data["esti_HP_alpha"]
self.esti_HP_filt_x = data["esti_HP_filt_x"]
self.esti_LP_x = data["esti_LP_x"]
self.esti_LP_dx = data["esti_LP_dx"]
self.esti_LP_alpha = data["esti_LP_alpha"]
self.esti_LP_filt_x = data["esti_LP_filt_x"]
self.esti_kf_X = data["esti_kf_X"]
self.esti_kf_Z = data["esti_kf_Z"]
self.loop_o_q_int = data["loop_o_q_int"]
self.loop_o_v = data["loop_o_v"]
self.loop_h_v = data["loop_h_v"]
self.loop_pos_virtual_world = data["loop_pos_virtual_world"]
self.loop_t_filter = data["loop_t_filter"]
self.loop_t_planner = data["loop_t_planner"]
self.loop_t_mpc = data["loop_t_mpc"]
self.loop_t_wbc = data["loop_t_wbc"]
self.loop_t_loop = data["loop_t_loop"]
self.loop_t_loop_if = data["loop_t_loop_if"]
self.loop_q_filt_mpc = data["loop_q_filt_mpc"]
self.loop_h_v_filt_mpc = data["loop_h_v_filt_mpc"]
self.loop_h_v_bis_filt_mpc = data["loop_h_v_bis_filt_mpc"]
self.loop_vref_filt_mpc = data["loop_vref_filt_mpc"]
self.planner_q_static = data["planner_q_static"]
self.planner_RPY_static = data["planner_RPY_static"]
self.planner_xref = data["planner_xref"]
self.planner_fsteps = data["planner_fsteps"]
self.planner_gait = data["planner_gait"]
self.planner_goals = data["planner_goals"]
self.planner_vgoals = data["planner_vgoals"]
self.planner_agoals = data["planner_agoals"]
self.planner_is_static = data["planner_is_static"]
self.planner_h_ref = data["planner_h_ref"]
self.mpc_x_f = data["mpc_x_f"]
self.mpc_solving_duration = data["mpc_solving_duration"]
self.wbc_P = data["wbc_P"]
self.wbc_D = data["wbc_D"]
self.wbc_q_des = data["wbc_q_des"]
self.wbc_v_des = data["wbc_v_des"]
self.wbc_tau_ff = data["wbc_tau_ff"]
self.wbc_f_ctc = data["wbc_f_ctc"]
self.wbc_feet_pos = data["wbc_feet_pos"]
self.wbc_feet_pos_target = data["wbc_feet_pos_target"]
self.wbc_feet_err = data["wbc_feet_err"]
self.wbc_feet_vel = data["wbc_feet_vel"]
self.wbc_feet_vel_target = data["wbc_feet_vel_target"]
self.wbc_feet_acc_target = data["wbc_feet_acc_target"]
self.tstamps = data["tstamps"]
# Load LoggerSensors arrays
loggerSensors.q_mes = data["q_mes"]
loggerSensors.v_mes = data["v_mes"]
loggerSensors.baseOrientation = data["baseOrientation"]
loggerSensors.baseAngularVelocity = data["baseAngularVelocity"]
loggerSensors.baseLinearAcceleration = data["baseLinearAcceleration"]
loggerSensors.baseAccelerometer = data["baseAccelerometer"]
loggerSensors.torquesFromCurrentMeasurment = data["torquesFromCurrentMeasurment"]
loggerSensors.mocapPosition = data["mocapPosition"]
loggerSensors.mocapVelocity = data["mocapVelocity"]
loggerSensors.mocapAngularVelocity = data["mocapAngularVelocity"]
loggerSensors.mocapOrientationMat9 = data["mocapOrientationMat9"]
loggerSensors.mocapOrientationQuat = data["mocapOrientationQuat"]
loggerSensors.logSize = loggerSensors.q_mes.shape[0]
def slider_predicted_trajectory(self):
from matplotlib import pyplot as plt
from matplotlib.widgets import Slider, Button
# The parametrized function to be plotted
def f(t, time):
return np.sin(2 * np.pi * t) + time
index6 = [1, 3, 5, 2, 4, 6]
log_t_pred = np.array([(k+1)*self.dt*10 for k in range(self.mpc_x_f.shape[2])])
log_t_ref = np.array([k*self.dt*10 for k in range(self.planner_xref.shape[2])])
trange = np.max([np.max(log_t_pred), np.max(log_t_ref)])
h1s = []
h2s = []
axs = []
h1s_vel = []
h2s_vel = []
axs_vel = []
# Define initial parameters
init_time = 0.0
# Create the figure and the line that we will manipulate
fig = plt.figure()
ax = plt.gca()
for j in range(6):
ax = plt.subplot(3, 2, index6[j])
h1, = plt.plot(log_t_pred, self.mpc_x_f[0, j, :], "b", linewidth=2)
h2, = plt.plot(log_t_ref, self.planner_xref[0, j, :], linestyle="--", marker='x', color="g", linewidth=2)
h3, = plt.plot(np.array([k*self.dt for k in range(self.mpc_x_f.shape[0])]),
self.planner_xref[:, j, 0], linestyle=None, marker='x', color="r", linewidth=1)
axs.append(ax)
h1s.append(h1)
h2s.append(h2)
#ax.set_xlabel('Time [s]')
axcolor = 'lightgoldenrodyellow'
#ax.margins(x=0)
# Make a horizontal slider to control the time.
axtime = plt.axes([0.25, 0.03, 0.65, 0.03], facecolor=axcolor)
time_slider = Slider(
ax=axtime,
label='Time [s]',
valmin=0.0,
valmax=self.logSize*self.dt,
valinit=init_time,
)
# Create the figure and the line that we will manipulate (for velocities)
fig_vel = plt.figure()
ax = plt.gca()
for j in range(6):
ax = plt.subplot(3, 2, index6[j])
h1, = plt.plot(log_t_pred, self.mpc_x_f[0, j, :], "b", linewidth=2)
h2, = plt.plot(log_t_ref, self.planner_xref[0, j, :], linestyle="--", marker='x', color="g", linewidth=2)
h3, = plt.plot(np.array([k*self.dt for k in range(self.mpc_x_f.shape[0])]),
self.planner_xref[:, j+6, 0], linestyle=None, marker='x', color="r", linewidth=1)
axs_vel.append(ax)
h1s_vel.append(h1)
h2s_vel.append(h2)
#axcolor = 'lightgoldenrodyellow'
#ax.margins(x=0)
# Make a horizontal slider to control the time.
axtime_vel = plt.axes([0.25, 0.03, 0.65, 0.03], facecolor=axcolor)
time_slider_vel = Slider(
ax=axtime_vel,
label='Time [s]',
valmin=0.0,
valmax=self.logSize*self.dt,
valinit=init_time,
)
# The function to be called anytime a slider's value changes
def update(val, recursive=False):
time_slider.val = np.round(val / (self.dt*10), decimals=0) * (self.dt*10)
rounded = int(np.round(time_slider.val / self.dt, decimals=0))
for j in range(6):
h1s[j].set_xdata(log_t_pred + time_slider.val)
h2s[j].set_xdata(log_t_ref + time_slider.val)
y1 = self.mpc_x_f[rounded, j, :] - self.planner_xref[rounded, j, 1:]
y2 = self.planner_xref[rounded, j, :] - self.planner_xref[rounded, j, :]
h1s[j].set_ydata(y1)
h2s[j].set_ydata(y2)
axs[j].set_xlim([time_slider.val - self.dt * 3, time_slider.val+trange+self.dt * 3])
ymin = np.min([np.min(y1), np.min(y2)])
ymax = np.max([np.max(y1), np.max(y2)])
axs[j].set_ylim([ymin - 0.05 * (ymax - ymin), ymax + 0.05 * (ymax - ymin)])
fig.canvas.draw_idle()
if not recursive:
update_vel(time_slider.val, True)
def update_vel(val, recursive=False):
time_slider_vel.val = np.round(val / (self.dt*10), decimals=0) * (self.dt*10)
rounded = int(np.round(time_slider_vel.val / self.dt, decimals=0))
for j in range(6):
h1s_vel[j].set_xdata(log_t_pred + time_slider.val)
h2s_vel[j].set_xdata(log_t_ref + time_slider.val)
y1 = self.mpc_x_f[rounded, j+6, :]
y2 = self.planner_xref[rounded, j+6, :]
h1s_vel[j].set_ydata(y1)
h2s_vel[j].set_ydata(y2)
axs_vel[j].set_xlim([time_slider.val - self.dt * 3, time_slider.val+trange+self.dt * 3])
ymin = np.min([np.min(y1), np.min(y2)])
ymax = np.max([np.max(y1), np.max(y2)])
axs_vel[j].set_ylim([ymin - 0.05 * (ymax - ymin), ymax + 0.05 * (ymax - ymin)])
fig_vel.canvas.draw_idle()
if not recursive:
update(time_slider_vel.val, True)
# register the update function with each slider
time_slider.on_changed(update)
time_slider_vel.on_changed(update)
plt.show()
def slider_predicted_footholds(self):
from matplotlib import pyplot as plt
from matplotlib.widgets import Slider, Button
import utils_mpc
import pinocchio as pin
self.planner_fsteps
# Define initial parameters
init_time = 0.0
# Create the figure and the line that we will manipulate
fig = plt.figure()
ax = plt.gca()
h1s = []
f_c = ["r", "b", "forestgreen", "rebeccapurple"]
quat = np.zeros((4, 1))
fsteps = self.planner_fsteps[0]
o_step = np.zeros((3*int(fsteps.shape[0]), 1))
RPY = pin.rpy.matrixToRpy(pin.Quaternion(self.loop_o_q_int[0, 3:7]).toRotationMatrix())
quat[:, 0] = utils_mpc.EulerToQuaternion([0.0, 0.0, RPY[2]])
oRh = pin.Quaternion(quat).toRotationMatrix()
for j in range(4):
o_step[0:3, 0:1] = oRh @ fsteps[0:1, (j*3):((j+1)*3)].transpose() + self.loop_o_q_int[0:1, 0:3].transpose()
h1, = plt.plot(o_step[0::3, 0], o_step[1::3, 0], linestyle=None, linewidth=0, marker="o", color=f_c[j])
h1s.append(h1)
axcolor = 'lightgoldenrodyellow'
# Make a horizontal slider to control the time.
axtime = plt.axes([0.25, 0.03, 0.65, 0.03], facecolor=axcolor)
time_slider = Slider(
ax=axtime,
label='Time [s]',
valmin=0.0,
valmax=self.logSize*self.dt,
valinit=init_time,
)
ax.set_xlim([-0.3, 0.5])
ax.set_ylim([-0.3, 0.5])
# The function to be called anytime a slider's value changes
def update(val):
time_slider.val = np.round(val / (self.dt*10), decimals=0) * (self.dt*10)
rounded = int(np.round(time_slider.val / self.dt, decimals=0))
fsteps = self.planner_fsteps[rounded]
o_step = np.zeros((3*int(fsteps.shape[0]), 1))
RPY = pin.rpy.matrixToRpy(pin.Quaternion(self.loop_o_q_int[rounded, 3:7]).toRotationMatrix())
quat[:, 0] = utils_mpc.EulerToQuaternion([0.0, 0.0, RPY[2]])
oRh = pin.Quaternion(quat).toRotationMatrix()
for j in range(4):
for k in range(int(fsteps.shape[0])):
o_step[(3*k):(3*(k+1)), 0:1] = oRh @ fsteps[(k):(k+1), (j*3):((j+1)*3)].transpose() + self.loop_o_q_int[rounded:(rounded+1), 0:3].transpose()
h1s[j].set_xdata(o_step[0::3, 0].copy())
h1s[j].set_ydata(o_step[1::3, 0].copy())
fig.canvas.draw_idle()
# register the update function with each slider
time_slider.on_changed(update)
plt.show()
if __name__ == "__main__":
import LoggerSensors
# Create loggers
loggerSensors = LoggerSensors.LoggerSensors(logSize=15000-3)
logger = LoggerControl(0.001, 30, logSize=15000-3)
# Load data from .npz file
logger.loadAll(loggerSensors)
# Call all ploting functions
logger.plotAll(loggerSensors)
# logger.slider_predicted_trajectory()