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'''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
from utils_mpc import quaternionToRPY
class LoggerControl():
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def __init__(self, dt, N0_gait, joystick=None, estimator=None, loop=None, gait=None, statePlanner=None,
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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
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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, 19]) # 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_pos_virtual_world = np.zeros([logSize, 3]) # x, y, yaw perfect position in world
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# Gait
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self.planner_gait = np.zeros([logSize, N0_gait, 4]) # Gait sequence
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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)
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# State planner
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if statePlanner is not None:
self.planner_xref = np.zeros([logSize, 12, 1+statePlanner.getNSteps()]) # Reference trajectory
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# Footstep planner
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if gait is not None:
self.planner_fsteps = np.zeros([logSize, gait.getCurrentGait().shape[0], 12]) # Reference footsteps position
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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)
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if statePlanner is not None:
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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()])
# Whole body control
self.wbc_x_f = np.zeros([logSize, 24]) # input vector of the WBC (next state + reference contact force)
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
self.wbc_feet_pos_invkin = np.zeros([logSize, 3, 4]) # current feet positions according to InvKin
self.wbc_feet_vel_invkin = np.zeros([logSize, 3, 4]) # current feet velocities according to InvKin
# Timestamps
self.tstamps = np.zeros(logSize)
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def sample(self, joystick, estimator, loop, gait, statePlanner, footstepPlanner, footTrajectoryGenerator, wbc):
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
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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
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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_pos_virtual_world[self.i] = np.array([loop.q[0, 0], loop.q[1, 0], loop.yaw_estim])
# Logging from the planner
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# 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()
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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
# Logging from whole body control
self.wbc_x_f[self.i] = loop.x_f_wbc
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[:, 0]
self.wbc_feet_pos[self.i] = wbc.feet_pos
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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
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self.wbc_feet_vel_target[self.i] = wbc.feet_vel_target
self.wbc_feet_acc_target[self.i] = wbc.feet_acc_target
#self.wbc_feet_pos_invkin[self.i] = wbc.invKin.cpp_posf.transpose() # TODO: Adapt logging
#self.wbc_feet_vel_invkin[self.i] = wbc.invKin.cpp_vf.transpose()
# Logging timestamp
self.tstamps[self.i] = time()
self.i += 1
def processMocap(self, N, loggerSensors):
self.mocap_b_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):
oRb = loggerSensors.mocapOrientationMat9[i]
"""from IPython import embed
embed()"""
self.mocap_b_v[i] = (oRb.transpose() @ loggerSensors.mocapVelocity[i].reshape((3, 1))).ravel()
self.mocap_b_w[i] = (oRb.transpose() @ loggerSensors.mocapAngularVelocity[i].reshape((3, 1))).ravel()
self.mocap_RPY[i] = quaternionToRPY(loggerSensors.mocapOrientationQuat[i])[:, 0]
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)"""
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='')
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plt.plot(t_range, self.wbc_feet_pos_invkin[:, i % 3, np.int(i/3)],
color='darkviolet', linewidth=3, linestyle="--", marker='')
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)])
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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", "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.plot(t_range, self.wbc_feet_vel_invkin[:, i % 3, np.int(i/3)],
color='darkviolet', linewidth=3, linestyle="--", 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, loggerSensors.mocapPosition[:, 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="--")
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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_b_v[:, i], "k", linewidth=3)
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# plt.plot(t_range, self.esti_FK_lin_vel[:, i], "violet", linewidth=3, linestyle="--")
plt.plot(t_range, self.esti_filt_lin_vel[:, i], "violet", linewidth=3, linestyle="--")
else:
plt.plot(t_range, self.mocap_b_w[:, i-3], "k", linewidth=3)
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"""N = 2000
y = np.convolve(self.mocap_b_w[:, i-3], np.ones(N)/N, mode='valid')
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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="--")
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plt.legend(["Robot state", "Robot reference state", "Ground truth"], 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"]
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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)
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#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",
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"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")
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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()
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for i in range(4):
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ax0 = plt.subplot(1, 4, i+1)
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plt.subplot(1, 4, i+1, sharex=ax0)
for k in range(0, self.mpc_x_f.shape[0], step):
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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="--")
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plt.plot(t_range, self.esti_feet_status[:, i], "k", linestyle="--")
plt.xlabel("Time [s]")
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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")
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# 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")
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,
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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,
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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,
wbc_x_f=self.wbc_x_f,
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,
)
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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"]
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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"]
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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.wbc_x_f = data["wbc_x_f"]
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"]
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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"]
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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)
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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)
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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()
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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 = utils_mpc.quaternionToRPY(self.loop_o_q_int[0, 3:7])
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 = utils_mpc.quaternionToRPY(self.loop_o_q_int[rounded, 3:7])
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()
Pierre-Alexandre Leziart
committed
if __name__ == "__main__":
import LoggerSensors
# Create loggers
loggerSensors = LoggerSensors.LoggerSensors(logSize=5997)
logger = LoggerControl(0.002, 100, logSize=5997)
# Load data from .npz file
logger.loadAll(loggerSensors)
# Call all ploting functions
#logger.plotAll(loggerSensors)
logger.slider_predicted_trajectory()