Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
'''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
class LoggerControl():
def __init__(self, dt, joystick=None, estimator=None, loop=None, planner=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_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
# 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
# Planner
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)
self.planner_xref = np.zeros([logSize, 12, 1+planner.n_steps]) # Reference trajectory
self.planner_fsteps = np.zeros([logSize, planner.gait.shape[0], 13]) # Reference footsteps position
self.planner_gait = np.zeros([logSize, 20, 5]) # Gait sequence
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
self.planner_is_static = np.zeros([logSize]) # if the planner is in static mode or not
self.planner_h_ref = np.zeros([logSize]) # reference height of the planner
# Model Predictive Control
self.mpc_x_f = np.zeros([logSize, 24]) # output vector of the MPC (next state + reference contact force)
# 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_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_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)
def sample(self, joystick, estimator, loop, planner, 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
self.esti_feet_status[self.i] = estimator.feet_status[:]
self.esti_feet_goals[self.i] = estimator.feet_goals
self.esti_q_filt[self.i] = estimator.q_filt[:, 0]
self.esti_v_filt[self.i] = estimator.v_filt[:, 0]
self.esti_v_secu[self.i] = estimator.v_secu[:]
self.esti_FK_lin_vel[self.i] = estimator.FK_lin_vel[:]
self.esti_FK_xyz[self.i] = estimator.FK_xyz[:]
self.esti_xyz_mean_feet[self.i] = estimator.xyz_mean_feet[:]
self.esti_HP_x[self.i] = estimator.filter_xyz_vel.x
self.esti_HP_dx[self.i] = estimator.filter_xyz_vel.dx
self.esti_HP_alpha[self.i] = estimator.filter_xyz_vel.alpha
self.esti_HP_filt_x[self.i] = estimator.filter_xyz_vel.filt_x
self.esti_LP_x[self.i] = estimator.filter_xyz_pos.x
self.esti_LP_dx[self.i] = estimator.filter_xyz_pos.dx
self.esti_LP_alpha[self.i] = estimator.filter_xyz_pos.alpha
self.esti_LP_filt_x[self.i] = estimator.filter_xyz_pos.filt_x
# Logging from the main loop
self.loop_o_q_int[self.i] = loop.q_estim[:, 0]
self.loop_o_v[self.i] = loop.v_estim[:, 0]
# Logging from the planner
self.planner_q_static[self.i] = planner.q_static[:, 0]
self.planner_RPY_static[self.i] = planner.RPY_static[:, 0]
self.planner_xref[self.i] = planner.xref
self.planner_fsteps[self.i] = planner.fsteps
self.planner_gait[self.i] = planner.gait
self.planner_goals[self.i] = planner.goals
self.planner_vgoals[self.i] = planner.vgoals
self.planner_agoals[self.i] = planner.agoals
self.planner_is_static[self.i] = planner.is_static
self.planner_h_ref[self.i] = planner.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
self.wbc_feet_err[self.i] = wbc.feet_err
self.wbc_feet_vel[self.i] = wbc.feet_vel
self.wbc_feet_pos_invkin[self.i] = wbc.invKin.cpp_posf.transpose()
self.wbc_feet_vel_invkin[self.i] = wbc.invKin.cpp_vf.transpose()
# Logging timestamp
self.tstamps[self.i] = time()
self.i += 1
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)])
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)
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)], color='g', linewidth=3, marker='')
plt.plot(t_range, self.planner_goals[:, i % 3, np.int(i/3)], color='r', linewidth=3, marker='')
plt.plot(t_range, self.wbc_feet_pos_invkin[:, i % 3, np.int(i/3)], color='darkviolet', linewidth=3, linestyle="--", marker='')
if (i % 3) == 2:
plt.plot(t_range, self.planner_gait[:, 0, 1+np.int(
i/3)] * np.max(self.wbc_feet_pos[:, i % 3, np.int(i/3)]), 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 (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)
plt.plot(t_range, self.wbc_feet_vel[:, i % 3, np.int(i/3)], color='b', linewidth=3, marker='')
plt.plot(t_range, self.planner_vgoals[:, 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 (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)
plt.plot(t_range, self.planner_agoals[:, 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 (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)
plt.plot(t_range, self.planner_xref[:, i, 0], "b", linewidth=2)
plt.plot(t_range, self.planner_xref[:, i, 1], "r", 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"], 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.esti_v_filt[:, i], "b", linewidth=2)
plt.plot(t_range, self.joy_v_ref[:, i], "r", linewidth=3)
# 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(["WBC integrated output state", "Robot reference state"], 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"])"""
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], "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")
plt.show(block=True)
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_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,
loop_o_q_int=self.loop_o_q_int,
loop_o_v=self.loop_o_v,
loop_q_static=self.loop_q_static,
loop_RPY_static=self.loop_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_err=self.wbc_feet_err,
wbc_feet_vel=self.wbc_feet_vel,
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,
)