-
Notifications
You must be signed in to change notification settings - Fork 1
/
GeneralFuncs.py
534 lines (472 loc) · 18.8 KB
/
GeneralFuncs.py
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
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
from matplotlib import pyplot as plt
from numpy import cos as cos
from numpy import sin as sin
import numpy as np
from numpy import diff
import matplotlib.pyplot as plt
from mujoco_py import (functions)
import ImpFuncs as IF
import globals
L1=globals.L1
L2=globals.L2
L3=globals.L3
SimStep=globals.SimStep
waitingTime=globals.waitingTime
sim=globals.sim
viewer=globals.viewer
#___# General navigation functions:
#full path navigation function
def Navigate(K,M,B,P,D,wantedPath, wantedTimes, waitingTime,method,methodCntrl,methodParams):
#initialazing:
dofs=np.shape(P[0])
elbow=0 #can be 0/1 - choose the path of the inverse kinematics of the manipulator.
q = invkin(wantedPath[0][0], wantedPath[0][1],wantedPath[0][2],elbow)
sim.data.qpos[0] = q[0]
sim.data.qpos[1] = q[1]
sim.data.qpos[2] = q[2]
#Impedance initital condition (for IMP only):
xm=np.array([wantedPath[0][0],wantedPath[0][1],wantedPath[0][2]]).reshape(-1,1)
xmDot=np.array([0,0,0]).reshape(-1,1) #specific for Robot at rest
#Generalazied data storage vectors:
accumulatedForce = []
accumulatedControl = []
accumulatedTravel = []
accumulatedPlannedPath = []
accumulatedJoints = []
accumulatedJointsVel = []
waitSim2(waitingTime)
forcebool=0 #boolean variable that can be used to check if the endeffector is being touched.
#General navigation loop:
for i in range(len(wantedPath) - 1):
nSteps = np.int32(wantedTimes[i] / SimStep)
path = linearPathPlan([wantedPath[i][0], wantedPath[i][1],wantedPath[i][2]], [wantedPath[i + 1][0], wantedPath[i + 1][1], wantedPath[i + 1][2]], wantedTimes[i])
[q_vec, q_dot_vec] = inversePathplan(path, wantedTimes[i], method,elbow,i)
x_vec=np.array([path[0,:],path[2,:],path[4,:]])
xDot_vec = np.array([path[1, :], path[3, :],path[5,:]])
if methodCntrl == "PD": #PD navigation between 2 pts
[travel, sensorData, controlData, jointData, jointVelData] = straightLineNav(q_vec, q_dot_vec, nSteps, P, D)
if methodCntrl =="imp": #Imp navigation between 2 pts
[travel, sensorData, controlData, jointData, jointVelData,xm] = IF.straightLineNavimp(x_vec,xDot_vec, nSteps,P,D,K,M,B,xm,xmDot,methodParams,dofs,elbow,q_vec, q_dot_vec,forcebool)
#append:
accumulatedJoints.append(jointData)
accumulatedJointsVel.append(jointVelData)
accumulatedForce.append(sensorData)
accumulatedControl.append(controlData)
accumulatedTravel.append(travel)
accumulatedPlannedPath.append(np.transpose(path)) # path needed to be transposed in order to work
# #finish path with step command to final target - can be applied ##
# tmplength=len(wantedPath)-1
# tmq=invkin(wantedPath[tmplength][0], wantedPath[tmplength][1],wantedPath[tmplength][2],elbow)
# tmpTime=4
# [q_vec,q_dot_vec]=stepPath(tmq[0],tmq[1],tmq[2],tmpTime)
# nSteps = np.int32(tmpTime / SimStep)
# [x_vec, xDot_vec] = stepPath(xm[0], xm[1],xm[2], tmpTime)
#
# x_vec=np.transpose(x_vec)
# xDot_vec=np.transpose(xDot_vec)
#
# if methodCntrl == "PD": # PD navigation between 2 pts
# [travel, sensorData, controlData, jointData, jointVelData] = straightLineNav(q_vec, q_dot_vec, nSteps, P, D)
# if methodCntrl == "imp": # Imp navigation between 2 pts
# [travel, sensorData, controlData, jointData, jointVelData,xm] = IF.straightLineNavimp(x_vec,xDot_vec, nSteps,P,D,K,M,B,xm,xmDot,methodParams,dofs,elbow,q_vec,q_dot_vec,forcebool)
#
# # append:
# accumulatedJoints.append(jointData)
# accumulatedJointsVel.append(jointVelData)
# accumulatedForce.append(sensorData)
# accumulatedControl.append(controlData)
# accumulatedTravel.append(travel)
# accumulatedPlannedPath.append(np.transpose(path)) # path needed to be transposed in order to work
# Total outputs arrange:
totalJoint = outputArrange(accumulatedJoints)
totalJointVel = outputArrange(accumulatedJointsVel)
totalForce = outputArrange(accumulatedForce)
totalControl = outputArrange(accumulatedControl)
totalTravel = outputArrange(accumulatedTravel)
totalPath = outputArrange(accumulatedPlannedPath)
#saving csv files with all the navigation data.
from numpy import savetxt
savetxt('totalTravel.csv',totalTravel,delimiter=',')
savetxt('totalPath.csv',totalPath,delimiter=',')
savetxt('totalControl.csv', totalControl, delimiter=',')
savetxt('totalForce.csv', totalForce, delimiter=',')
savetxt('totalJointVel.csv', totalJointVel, delimiter=',')
savetxt('totalJoint.csv', totalJoint, delimiter=',')
#plotting: #plot all graphs.
#jointPlotter(totalJoint)
#jointVelPlotter(totalJointVel)
#forcePlotter(totalForce)
#controlPlotter(totalControl)
#pathPlotter(totalTravel, totalPath)
#plt.show()
sim.reset()
#____#kinematics:
# inverse kinematics function. input px,py,phi,elbow - output: q1,q2,q3
def invkin(px, py,phi,elbow):
delta=0
change_elbow=0
x3 = px - L3 * cos(phi)
y3 = py - L3 * sin(phi)
c2 = (x3 ** 2 + y3 ** 2 - L1 ** 2 - L2 ** 2) / (2 * L1 * L2+delta)
if c2>1:
c2=1
if elbow ==1:
s2 = (1 - c2 ** 2) ** 0.5
if elbow == 0:
s2 = -(1 - c2 ** 2) ** 0.5
theta2 = np.arctan2(s2, c2)
k1 = L1 + L2 * c2
k2 = L2 * s2
c1 = (k1 * x3 + k2 * y3) / (k1 ** 2 + k2 ** 2 + delta)
s1 = (-k2 * x3 + k1 * y3) / (k1 ** 2 + k2 ** 2 + delta)
theta1 = np.arctan2(s1, c1)
if np.abs(theta2)<0:
change_elbow==1
theta3 = phi - theta1 - theta2
if change_elbow==1:
if elbow ==1:
elbow=0
if elbow ==0:
elbow=1
q = [theta1, theta2, theta3, elbow]
return q
# forward kinematics function. input q1,q2,q3 output: x1,x2,phi
def forwardkin(q1,q2,q3):
global L1
global L2
x=L3*(cos(q1+q2+q3))+ L2*(cos(q1)*cos(q2)-sin(q1)*sin(q2))+L1*cos(q1)
y=L3*(sin(q1+q2+q3))+L2*(cos(q1)*sin(q2)+sin(q1)*cos(q2))+L1*sin(q1)
phi=q1+q2+q3
return x,y,phi
# planar path planning function. Input: current position of the end effector,
# target position of the robot, time of navigation, number of points in the path
# output: navigation vector in cartesian domain.
def linearPathPlan(curr_pos, target_pos, T):
global SimStep
xPath = []
yPath = []
phiPath = []
x_dot_Path = []
y_dot_Path = []
phi_dot_Path = []
t_list = np.linspace(0, T, num=np.int32(T / SimStep))
# Const definitions:
kx = (target_pos[0] - curr_pos[0]) / T ** 3
kphi = (target_pos[2] - curr_pos[2]) / T ** 3
a5 = 6 / T ** 2
a4 = -(15 / T)
a3 = 10
delta = 0.0001
# Specific case when path is vertical:
if target_pos[0] == curr_pos[0]:
ky = (target_pos[1] - curr_pos[1]) / T ** 3
for t in t_list:
x = target_pos[0]
y = ky * (a5 * (t ** 5) + a4 * (t ** 4) + a3 * (t ** 3)) + curr_pos[1]
phi = kphi * (a5 * (t ** 5) + a4 * (t ** 4) + a3 * (t ** 3)) + curr_pos[2]
x_dot = 0
y_dot = ky * (a5 * 5 * (t ** 4) + a4 * 4 * (t ** 3) + 3 * a3 * (t ** 2))
phi_dot = kphi * (a5 * 5 * (t ** 4) + a4 * 4 * (t ** 3) + 3 * a3 * (t ** 2))
xPath.append(x)
yPath.append(y)
phiPath.append(phi)
x_dot_Path.append(x_dot)
y_dot_Path.append(y_dot)
phi_dot_Path.append(phi_dot)
path = np.array([xPath, x_dot_Path, yPath, y_dot_Path, phiPath, phi_dot_Path])
return path
# General path:
ky = ((target_pos[1] - curr_pos[1]) / (delta + target_pos[0] - curr_pos[0]))
ky0 = (target_pos[0] * curr_pos[1] - target_pos[1] * curr_pos[0]) / (delta + target_pos[0] - curr_pos[0])
# General case:
for t in t_list:
x = kx * (a5 * (t ** 5) + a4 * (t ** 4) + a3 * (t ** 3)) + curr_pos[0]
y = ky * x + ky0
phi = kphi * (a5 * (t ** 5) + a4 * (t ** 4) + a3 * (t ** 3)) + curr_pos[2]
x_dot = kx * (a5 * 5 * (t ** 4) + a4 * 4 * (t ** 3) + 3 * a3 * (t ** 2))
y_dot = ky * x_dot
phi_dot = kphi * (a5 * 5 * (t ** 4) + a4 * 4 * (t ** 3) + 3 * a3 * (t ** 2))
xPath.append(x)
yPath.append(y)
phiPath.append(phi)
x_dot_Path.append(x_dot)
y_dot_Path.append(y_dot)
phi_dot_Path.append(phi_dot)
path = np.array([xPath, x_dot_Path, yPath, y_dot_Path, phiPath, phi_dot_Path])
return path
# function that convert path vector to path vector in the joints domain
def inversePathplan(path, Time,method,elbow,ii):
xPath = path[0, :]
x_dot_Path = path[1, :]
yPath = path[2, :]
y_dot_Path = path[3, :]
phiPath = path[4, :]
phi_dot_Path = path[5, :]
# plt.scatter(xPath, yPath) # used for debug.
q_vec = []
t_list = np.linspace(0, Time, np.int32(Time / SimStep))
for i in range(len(t_list)):
q_vec.append(invkin(xPath[i], yPath[i],phiPath[i],elbow))
q_vec = np.array(q_vec)
if method=='numerical': #numeric
q_dot_vec = np.array([diff(q_vec[:, 0]) / SimStep, diff(q_vec[:, 1]) / SimStep,diff(q_vec[:, 2]) / SimStep])
b = [0, 0, 0]
q_dot_vec = q_dot_vec.transpose()
q_dot_vec = np.append(q_dot_vec, b).reshape(q_dot_vec.shape[0] + 1, q_dot_vec.shape[1])
return [q_vec, q_dot_vec]
# one point navigating function with PD control.
def pdNav2Target(q_target, q_dot_target,P,D):
#output parameters:
effector_pos = []
sensorData = []
control_effort = []
angle=[]
angleVel=[]
# Control:
error = q_target - sim.data.actuator_length
q_dot = sim.data.actuator_velocity - q_dot_target
#torques output:
action = np.matmul(P, error) - np.matmul(D, q_dot) +[sim.data.qfrc_bias[0], sim.data.qfrc_bias[1], sim.data.qfrc_bias[2]] # sim.data.qfrc_bias is Gravity bias/robot dependant
#Force collection:
forces,torques =IF.getForces(3,'gripper')
# collecting sensors data: particular to 3Dof Robot
sensorData.append([forces[0],forces[1],forces[2],torques[0],torques[1],torques[2]])
effector_pos.append([np.array(sim.data.site_xpos[0])[0], np.array(sim.data.site_xpos[0])[1], np.array(sim.data.site_xpos[0])[2]])
control_effort.append([action[0],action[1],action[2]])
angle.append([sim.data.actuator_length[0],sim.data.actuator_length[1],sim.data.actuator_length[2]])
angleVel.append([sim.data.actuator_velocity[0],sim.data.actuator_velocity[1],sim.data.actuator_velocity[2]])
return effector_pos, sensorData, control_effort, action,angle,angleVel # collecting position of endeffector
# total navigation function - > gets all navigation points and looping for one
# point navigation function.
def straightLineNav(qvec, q_dot_vec, nPts,P,D):
jointDataTotal = []
jointVelDataTotal = []
effector_pos_total = []
sensorDataTotal = []
control_effort_total = []
for i in range(nPts):
effector_pos, sensorData, control_effort, action, angle, angleVel = pdNav2Target([qvec[i, 0], qvec[i, 1], qvec[i, 2]], [q_dot_vec[i, 0], q_dot_vec[i, 1],q_dot_vec[i, 2]],P,D)
#Output appending:
jointDataTotal.append(angle)
jointVelDataTotal.append(angleVel)
effector_pos_total.append(effector_pos)
sensorDataTotal.append(sensorData)
control_effort_total.append(control_effort)
# Action and rendering:
sim.data.ctrl[:] = action
sim.step()
sim.forward()
viewer.render()
#Arrange vectors:
full_joints_data = outputArrange(jointDataTotal)
full_joints_vel_data = outputArrange(jointVelDataTotal)
full_sensor_data = outputArrange(sensorDataTotal)
full_effector_data = outputArrange(effector_pos_total)
full_control_data = outputArrange(control_effort_total)
return full_effector_data, full_sensor_data, full_control_data ,full_joints_data , full_joints_vel_data
#__#General purpose functions:
#function that arranges the output vector and a form that is easy to use.
#input un arranged vector -> output arranged vector
def outputArrange(vec):
sizeF = 0
for i in range(len(vec)):
for j in range(len(vec[i])):
sizeF = sizeF + 1
vec_width=len(vec[0][0])
full_vec = np.zeros([sizeF, vec_width])
sizeF = 0
for i in range(len(vec)):
for j in range(len(vec[i])):
full_vec[sizeF, :] = np.array(vec[i][j])
sizeF = sizeF + 1
return full_vec
#step input signal to the robot joints space
def stepPath(q1,q2,q3,T):
global SimStep
nSteps = np.int32(T / SimStep)
path=np.zeros([nSteps,np.size(sim.data.actuator_length)])
path_qdot=np.zeros([nSteps,np.size(sim.data.actuator_length)])
for i in range(nSteps):
path[i,:]=[q1,q2,q3]
path_qdot[i,:]=[0,0,0]
return path,path_qdot
#step input signal to the robot cartesian space
def stepPathCartesian(x,y,T):
global SimStep
nSteps = np.int32(T / SimStep)
path=np.zeros([nSteps,np.size(sim.data.actuator_length)])
path_qdot=np.zeros([nSteps,np.size(sim.data.actuator_length)])
for i in range(nSteps):
path[i,:]=[x,y]
path_qdot[i,:]=[0,0]
return path,path_qdot
#get forces and torques that acting about simulation site with name sitename
#___# all the rest:
#function that output q dot by knowitng xdot vec and q vec.
#specific to RR planar robot
def get_q_dot_from_jack(q,x_dot,y_dot):
global waitingTime
global L1
global L2
J=np.zeros([2,2])
delta=0.1
J[0,0]=-L2*(cos(q[0])*sin(q[1]) + cos(q[1])*sin(q[0])) -L1*sin(q[0])
J[1,0]= L2*(cos(q[0])*cos(q[1]) - sin(q[0])*sin(q[1])) +L1*cos(q[0])
J[0,1]= -L2*(cos(q[0])*sin(q[1]) + cos(q[1])*sin(q[0]))
J[1,1] = L2 * (cos(q[0]) * cos(q[1]) - sin(q[0]) * sin(q[1]))
x_vec=np.array([x_dot,y_dot]).reshape(-1,1)
try: #try singular point
J_inv=np.linalg.inv(J)
qdot_vec = np.matmul(np.linalg.inv(J), x_vec)
except Exception:
print('robot went through a singular point')
try: #custom solution for first step at qvec=0,0
if sim.data.time<waitingTime*1.1:
qdot_vec=np.transpose([0,0])
finally:
return (np.transpose(qdot_vec))
return (np.transpose(qdot_vec))
#function that output qdotaim by knowing xdot vec and q vec
#specific for RRR planar robot
def get_q_dotaim(q,qd,x_dotaim):
global waitingTime
global L1
global L2
q=np.array(q)
qd=np.array(qd)
Jd=np.zeros([2,2])
Jd[0,0] = -L2*(cos(q[0])*cos(q[1])*qd[0] + cos(q[0])*cos(q[1])*qd[1] - sin(q[0])*sin(q[1])*qd[0] - sin(q[0])*sin(q[1])*qd[1]) - L1*cos(q[0])*qd[0]
Jd[1,0] = -L2*(cos(q[0])*sin(q[1])*qd[1] + cos(q[1])*sin(q[0])*qd[0] + cos(q[0])*sin(q[1])*qd[1] + cos(q[1])*sin(q[0])*qd[1]) - L1*sin(q[0])*qd[0]
Jd[0,1] = -L2*(cos(q[0])*cos(q[1])*qd[0] + cos(q[0])*cos(q[1])*qd[1] - sin(q[0])*sin(q[1])*qd[0] - sin(q[0])*sin(q[1])*qd[1])
Jd[1,1] = -L2*(cos(q[0])*sin(q[1])*qd[0] + cos(q[1])*sin(q[0])*qd[0] + cos(q[0])*sin(q[1])*qd[1] + cos(q[1])*sin(q[0])*qd[1])
J=np.zeros([2,2])
J[0,0]=-L2*(cos(q[0])*sin(q[1]) + cos(q[1])*sin(q[0])) -L1*sin(q[0])
J[1,0]= L2*(cos(q[0])*cos(q[1]) - sin(q[0])*sin(q[1])) +L1*cos(q[0])
J[0,1]= -L2*(cos(q[0])*sin(q[1]) + cos(q[1])*sin(q[0]))
J[1,1] = L2 * (cos(q[0]) * cos(q[1]) - sin(q[0]) * sin(q[1]))
try: #try singular point
J_inv=np.linalg.inv(J)
qdotaim_vec = np.matmul(np.linalg.inv(J),x_dotaim-np.matmul(Jd,qd))
except Exception:
print('robot went through a singular point')
try: #custom solution for first step at qvec=0,0
if sim.data.time<waitingTime*1.1:
qdotaim_vec=([0,0])
finally:
return (np.array(qdotaim_vec))
return (np.array(qdotaim_vec))
#___#Plotting functions
def pathPlotter(travel,path):
fig = plt.figure(0)
plt.plot(travel[:, 0], travel[:, 1], c='r')
plt.plot(travel[0, 0], travel[0, 1], c='r', marker='X')
plt.axis('equal')
plt.grid()
plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.scatter(path[1:, 0], path[1:, 2], c='b')
plt.scatter(path[0, 0], path[0, 2], c='b', marker='X')
def forcePlotter(force):
fig = plt.figure(1)
plt.plot(force[:, 0], 'green')
plt.xlabel("Sim step")
plt.ylabel("x force")
plt.grid()
plt.draw()
fig = plt.figure(2)
plt.plot(force[:, 1], 'blue')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("y force")
plt.draw()
'''''''''
fig = plt.figure(3)
plt.plot(force[:, 2], 'red')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("z force")
plt.draw()
fig = plt.figure(4)
plt.plot(force[:, 3], 'green')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("torque 1")
plt.draw()
fig = plt.figure(5)
plt.plot(force[:, 4], 'blue')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("torque 2")
plt.draw()
'''''''''
fig = plt.figure(3)
plt.plot(force[:, 5], 'red')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("torque 3")
plt.draw()
def controlPlotter(control):
fig = plt.figure(4)
plt.plot(control[:, 0], 'red')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("q1 control effort")
plt.draw()
fig = plt.figure(5)
plt.plot(control[:, 1], 'blue')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("q2 control effort")
plt.draw()
def jointPlotter(joint):
fig = plt.figure(6)
plt.plot(joint[:, 0], 'red')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("q1 [rad]]")
plt.draw()
fig = plt.figure(7)
plt.plot(joint[:, 1], 'blue')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("q2 [rad]")
plt.draw()
def jointVelPlotter(jointVel):
fig = plt.figure(8)
plt.plot(jointVel[:, 0], 'red')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("q1 dot [rad/sec]]")
plt.draw()
fig = plt.figure(9)
plt.plot(jointVel[:, 1], 'blue')
plt.grid()
plt.xlabel("Sim step")
plt.ylabel("q2 dot [rad/sec]")
plt.draw()
#__#Waiting functions:
#waits with documentation and action
def waitSim(T):
global SimStep
nSteps = np.int32(T / SimStep)
q_vec=np.zeros([nSteps,np.size(sim.data.actuator_length)])
q_dot_vec = np.zeros([nSteps, np.size(sim.data.actuator_length)])
for i in range(nSteps):
q_vec[i,:]=[sim.data.qpos[0],sim.data.qpos[1]]
q_dot_vec[i,:]=sim.data.actuator_velocity
[travel, sensorData, controlData] = straightLineNav(q_vec,q_dot_vec, nSteps)
return travel, sensorData, controlData
#waits without documentationand without any action
def waitSim2(T):
nSteps = np.int32(T / SimStep)
for i in range(nSteps):
sim.step()
sim.forward()
#viewer.render()
#get desired impedance params:
def getDesiredParamsAnalytic(xm,xmDot,xmDotaim):
qd = invkin(xm[0],xm[1],xm[2])
qdDot=get_q_dot_from_jack(qd, xmDot[0], xmDot[1])
qdDot=np.array(qdDot).reshape(-1,1)
qdDotaim=get_q_dotaim(qd,qdDot,xmDotaim)
return qd, qdDot, qdDotaim