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test_results.py
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test_results.py
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import sympy
import numpy
from math import cos, sin
import sympybotics
from sympybotics._compatibility_ import exec_
def test_scara_dh_sym_geo_kin():
pi = sympy.pi
q = sympybotics.robotdef.q
a1, a2, d3, d4 = sympy.symbols('a1, a2, d3, d4')
scara = sympybotics.robotdef.RobotDef(
'SCARA - Spong',
[( 0, a1, 0, q),
(pi, a2, 0, q),
( 0, 0, q, 0),
( 0, 0, d4, q)],
dh_convention='standard')
scara_geo = sympybotics.geometry.Geometry(scara)
scara_kin = sympybotics.kinematics.Kinematics(scara, scara_geo)
cos, sin = sympy.cos, sympy.sin
q1, q2, q3, q4 = sympy.flatten(scara.q)
T_spong = sympy.Matrix([
[(-sin(q1)*sin(q2) + cos(q1)*cos(q2))*cos(q4) + (sin(q1)*cos(q2) +
sin(q2)*cos(q1))*sin(q4), -(-sin(q1)*sin(q2) +
cos(q1)*cos(q2))*sin(q4) + (sin(q1)*cos(q2) +
sin(q2)*cos(q1))*cos(q4), 0, a1*cos(q1) - a2*sin(q1)*sin(q2) +
a2*cos(q1)*cos(q2)],
[(sin(q1)*sin(q2) - cos(q1)*cos(q2))*sin(q4) + (sin(q1)*cos(q2) +
sin(q2)*cos(q1))*cos(q4), (sin(q1)*sin(q2) -
cos(q1)*cos(q2))*cos(q4) - (sin(q1)*cos(q2) +
sin(q2)*cos(q1))*sin(q4), 0, a1*sin(q1) + a2*sin(q1)*cos(q2) +
a2*sin(q2)*cos(q1)],
[0, 0, -1, -d4 - q3],
[0, 0, 0, 1]])
J_spong = sympy.Matrix([[-a1*sin(q1) - a2*sin(q1)*cos(q2) -
a2*sin(q2)*cos(q1), -a2*sin(q1)*cos(q2) -
a2*sin(q2)*cos(q1), 0, 0],
[a1*cos(q1) - a2*sin(q1)*sin(q2) +
a2*cos(q1)*cos(q2), -a2*sin(q1)*sin(q2) +
a2*cos(q1)*cos(q2), 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 0, -1]])
assert (scara_geo.T[-1] - T_spong).expand() == sympy.zeros(4)
assert (scara_kin.J[-1] - J_spong).expand() == sympy.zeros(6, 4)
def test_puma_dh_num_geo_kin_dyn():
pi = sympy.pi
q = sympybotics.robotdef.q
puma560_def = sympybotics.RobotDef(
'Puma 560 Robot',
[( pi/2, 0, 0, q),
( 0, 0.4318, 0, q),
('-pi/2', '0.0203', '0.15005', 'q'), # test sympify
( pi/2, 0, 0.4318, q),
( -pi/2, 0, 0, q),
( 0, 0, 0, q)],
dh_convention='standard')
puma560_def.frictionmodel = None
puma560 = sympybotics.RobotDynCode(puma560_def)
q_test = [0.7504516826728697, 0.8395156106908136, 0.16851233582594916, 0.3849629637427072, 0.5252993946810777, 0.6701207256444748]
dq_test = [0.24721855939629367, 0.9805915670454258, 0.9895299755642817, 0.7861135739668947, 0.273842245476577, 0.17182358900767503]
ddq_test = [0.707405815485141, 0.25295715193420953, 0.9763909835998361, 0.8412822676113918, 0.4867768296473465, 0.11480270540937143]
dynparm_test = [0.0, -0, -0, 0.34999999999999998, -0, 0.0, 0.0, 0.0, 0.0, 0, 1.03118515, 0.037980719999999996, 1.4401022999999999,
3.7274564059999999, -0.023751000000000001, 2.8425240559999998, -6.33012, 0.10439999999999999, 3.9585, 17.4,
0.090474288000000014, -0.0013739039999999998, 0.0068208000000000001, 0.111498032, 0.0047375999999999998,
0.015432319999999999, -0.09743999999999998, -0.06767999999999999, 0.336, 4.8, 0.0020960200000000001, -0, -0,
0.0012999999999999999, -0, 0.0020960200000000001, 0.0, 0.015579999999999998, 0.0, 0.82, 0.00029999999999999997, -0, -0,
0.00040000000000000002, -0, 0.00029999999999999997, 0.0, 0.0, 0.0, 0.34, 0.00024216, -0, -0, 0.00024216, -0,
4.0000000000000003e-05, 0.0, 0.0, 0.0028799999999999997, 0.09]
dynparm_test2 = [0.47804562306292275, 0.5871876506259908, 0.9487349009746813, 0.35387185413632094, 0.28071604959871554, 0.368556182617345,
0.24355010647230801, 0.6753463418802456, 0.9728151452953864, 0.6620741264406734, 0.34669638996014096, 0.01593886435340608,
0.3521748260592804, 0.5384045845183812, 0.021600503502885116, 0.4654003203805651, 0.5202014161122065, 0.33744920539722967,
0.052363297799702835, 0.07051826001770234, 0.7389222546505236, 0.771434543548951, 0.3652539269897015, 0.2603059367721896,
0.4310648491411889, 0.7071252186366607, 0.16320122542325732, 0.44948421506462655, 0.48085540250421277, 0.08408482356412372,
0.923593157615906, 0.46852453511703684, 0.6670004526434297, 0.573634975268657, 0.12665814747855264, 0.3152822779549781,
0.21725524421221942, 0.5727451645276381, 0.7984025459787872, 0.7630923700903489, 0.27670044727992893, 0.919667661316697,
0.8828363383223908, 0.3618378476984413, 0.20690997158181246, 0.8321536435628591, 0.556153477173109, 0.3985225289975399,
0.7128303168401632, 0.433898343199971, 0.35116090526732047, 0.551636779637059, 0.03397585970570116, 0.28333059714010744,
0.4785548774382852, 0.16258401709779136, 0.8246056496848533, 0.23027686557606952, 0.26655111443159285, 0.6087446254045791]
q_num_subs = dict(zip(puma560_def.q, q_test))
T = puma560.geo.T[-1]
T = T.subs(q_num_subs)
T = numpy.matrix(T).astype(numpy.float64)
J = puma560.kin.J[-1]
J = J.subs(q_num_subs)
J = numpy.matrix(J).astype(numpy.float64)
M_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.M_code, 'M', 'M_puma560', puma560_def)
c_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.c_code, 'c', 'c_puma560', puma560_def)
C_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.C_code, 'C', 'C_puma560', puma560_def)
g_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.g_code, 'g', 'g_puma560', puma560_def)
tau_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.invdyn_code, 'tau', 'tau_puma560', puma560_def)
H_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.H_code, 'H', 'H_puma560', puma560_def)
l = locals()
exec_(M_func_def, globals(), l)
exec_(c_func_def, globals(), l)
exec_(C_func_def, globals(), l)
exec_(g_func_def, globals(), l)
exec_(tau_func_def, globals(), l)
exec_(H_func_def, globals(), l)
tau_puma560 = l['tau_puma560']
g_puma560 = l['g_puma560']
c_puma560 = l['c_puma560']
C_puma560 = l['C_puma560']
M_puma560 = l['M_puma560']
H_puma560 = l['H_puma560']
tau = tau_puma560(dynparm_test, q_test, dq_test, ddq_test)
tau = numpy.matrix(tau).T.astype(numpy.float64)
g = g_puma560(dynparm_test, q_test)
g = numpy.matrix(g).T.astype(numpy.float64)
c = c_puma560(dynparm_test, q_test, dq_test)
c = numpy.matrix(c).T.astype(numpy.float64)
C = C_puma560(dynparm_test, q_test, dq_test)
C = numpy.matrix(C).reshape(puma560.dof, puma560.dof).astype(numpy.float64)
C_dq = C * numpy.matrix(dq_test).T
M = M_puma560(dynparm_test, q_test)
M = numpy.matrix(M).reshape(puma560.dof, puma560.dof).astype(numpy.float64)
H = H_puma560(q_test, dq_test, ddq_test)
H = numpy.matrix(H).reshape(puma560.dof, puma560.dyn.n_dynparms
).astype(numpy.float64)
tau_t2 = tau_puma560(dynparm_test2, q_test, dq_test, ddq_test)
tau_t2 = numpy.matrix(tau_t2).T.astype(numpy.float64)
g_t2 = g_puma560(dynparm_test2, q_test)
g_t2 = numpy.matrix(g_t2).T.astype(numpy.float64)
c_t2 = c_puma560(dynparm_test2, q_test, dq_test)
c_t2 = numpy.matrix(c_t2).T.astype(numpy.float64)
C_t2 = C_puma560(dynparm_test2, q_test, dq_test)
C_t2 = numpy.matrix(C_t2).reshape(puma560.dof, puma560.dof
).astype(numpy.float64)
C_dq_t2 = C_t2 * numpy.matrix(dq_test).T
M_t2 = M_puma560(dynparm_test2, q_test)
M_t2 = numpy.matrix(M_t2).reshape(puma560.dof, puma560.dof
).astype(numpy.float64)
T_pcorke = numpy.matrix([[-0.655113870655343, -0.474277925274361, -0.588120962109346, 0.0540498757911011],
[ 0.524340309498464, 0.275038163391149, -0.805866768463298, -0.154761559833806],
[ 0.543960528264717, -0.836310025215453, 0.0685017183295358, 0.568944734102513],
[ 0.0, 0.0, 0.0, 1.0]])
J_pcorke = numpy.matrix([[ 0.154761559833806, -0.416115317270121, -0.181051500179202, 0.0, 0.0, 0.0],
[ 0.0540498757911011, -0.388002774727404, -0.16881975145483, 0.0, 0.0, 0.0],
[-1.56125112837913e-17, -0.0660115669805396, -0.354377730397368, 0.0, 0.0, 0.0],
[ 2.77555756156289e-17, 0.681969181663071, 0.681969181663071, -0.618588326585383, 0.778592276991744, -0.588120962109346],
[ 1.04083408558608e-16, -0.731380909828662, -0.731380909828662, -0.576796808883883, -0.541217849732837, -0.805866768463298],
[ 1.0, 5.72458747072346e-17, 5.72458747072346e-17, 0.533529683779323, 0.317611878460765, 0.0685017183295358]])
tau_pcorke = numpy.matrix([[ 0.986185688341252],
[ 16.1055550633721],
[ -6.54839494827661],
[0.00626452648190803],
[-0.0208972707132494],
[5.59805923645945e-5]])
g_pcorke = numpy.matrix([[1.0295354054077e-15],
[ 16.8135891283022],
[ -7.29033633567802],
[0.00449992182015992],
[ -0.026719893613902],
[ 0.0]])
c_pcorke = numpy.matrix([[ -0.240237883763956],
[ -1.0843848843681],
[ 0.365583586254475],
[-0.000123404998742911],
[ 0.00359951257553373],
[ 1.1075729558421e-5]])
M_pcorke = numpy.matrix([[ 2.05591694561963, -0.607149114401804, -0.0772414386669633, 0.00110264316988551, 0.000264135330891356, 2.74006873318143e-6],
[ -0.607149114401804, 2.00048563124708, 0.306870038373922, -0.000227846077158843, 0.000780985328855223, 7.53260796283046e-6],
[ -0.0772414386669632, 0.306870038373922, 0.361368447500762, -0.000267122764221431, 0.00156301630001611, 7.53260796283046e-6],
[ 0.00110264316988551, -0.000227846077158844, -0.000267122764221432, 0.00169083802882858, 1.45380752747483e-20, 3.4606953693396e-5],
[0.000264135330891356, 0.000780985328855223, 0.00156301630001611, 2.33135442795155e-20, 0.00064216, 2.44929359829471e-21],
[ 2.74006873318143e-6, 7.53260796283046e-6, 7.53260796283046e-6, 3.4606953693396e-5, 2.44929359829471e-21, 4.0e-5]])
tau_assrt2 = numpy.matrix([[ 4.55384377546122],
[-18.3482765770679],
[-18.5150406032816],
[-3.48354572715293],
[-4.22434630683546],
[-8.50580534103007]])
g_assrt2 = numpy.matrix([[-4.44089209850063e-16],
[ -16.5628257742538],
[ -23.0524142097215],
[ -4.2536826692411],
[ -8.37451830056579],
[ -7.9940463468124]])
c_assrt2 = numpy.matrix([[ 3.79031173486171],
[ -6.37866680030809],
[ 0.668430906458299],
[ 0.0446113764650349],
[ 2.68800462309735],
[-0.0871119983741941]])
M_assrt2 = numpy.matrix([[ 2.80338554010218, -0.884144436072495, -1.36196693847736, 0.914512998462046, -0.799840112619873, -0.402048288616148],
[-0.884144436072495, 5.09045072475534, 3.75033319331688, -0.822003775709123, 2.00576876282064, -0.136034063369629],
[ -1.36196693847736, 3.75033319331688, 3.69036945486662, -0.784876338891255, 1.91719457979014, 0.0657267986145439],
[ 0.914512998462046, -0.822003775709123, -0.784876338891255, 1.86573777350968, -1.06272697183618, 0.00496876753726561],
[-0.799840112619873, 2.00576876282064, 1.91719457979014, -1.06272697183618, 1.2083737144556, -0.396167549434339],
[-0.402048288616148, -0.136034063369629, 0.0657267986145439, 0.00496876753726561, -0.396167549434339, 0.162584017097791]])
assert_precision = 10
assert not numpy.any(numpy.round(T_pcorke - T, assert_precision))
assert not numpy.any(numpy.round(J_pcorke - J, assert_precision))
assert not numpy.any(numpy.round(tau_pcorke - tau, assert_precision))
assert not numpy.any(numpy.round(g_pcorke - g, assert_precision))
assert not numpy.any(numpy.round(c_pcorke - c, assert_precision))
assert not numpy.any(numpy.round(C_dq - c, assert_precision))
assert not numpy.any(numpy.round(M_pcorke - M, assert_precision))
assert not numpy.any(numpy.round(
tau_pcorke - H * numpy.matrix(dynparm_test).T, assert_precision))
assert not numpy.any(numpy.round(tau_assrt2 - tau_t2, assert_precision))
assert not numpy.any(numpy.round(g_assrt2 - g_t2, assert_precision))
assert not numpy.any(numpy.round(c_assrt2 - c_t2, assert_precision))
assert not numpy.any(numpy.round(C_dq_t2 - c_t2, assert_precision))
assert not numpy.any(numpy.round(M_assrt2 - M_t2, assert_precision))
assert not numpy.any(numpy.round(
tau_assrt2 - H * numpy.matrix(dynparm_test2).T, assert_precision))
def test_puma_mdh_num_geo_kin_dyn():
pi = sympy.pi
q = sympybotics.robotdef.q
puma560_def = sympybotics.RobotDef(
'Puma Robot 560 mdh',
[( 0., 0., 0., q),
(-pi/2., 0., 0.2435, q),
( 0., 0.4318, -0.0934, q),
( pi/2., -0.0203, 0.4331, q),
(-pi/2., 0., 0., q),
( pi/2., 0., 0., q)],
dh_convention='modified')
puma560_def.frictionmodel = None
puma560 = sympybotics.RobotDynCode(puma560_def)
q_test = [0.7504516826728697, 0.8395156106908136, 0.16851233582594916, 0.3849629637427072, 0.5252993946810777, 0.6701207256444748]
dq_test = [0.24721855939629367, 0.9805915670454258, 0.9895299755642817, 0.7861135739668947, 0.273842245476577, 0.17182358900767503]
ddq_test = [0.707405815485141, 0.25295715193420953, 0.9763909835998361, 0.8412822676113918, 0.4867768296473465, 0.11480270540937143]
dynparm_test = [0.0, 0.0, 0.0, 0.0, 0.0, 0.34999999999999998, 0, 0, 0, 0, 0.1350808, -0.0070992, 0.018931199999999999, 0.60891200000000001,
0.0016703999999999998, 0.62008400000000008, 1.1832, 0.10439999999999999, -0.2784, 17.4, 0.090460800000000008, 0.0, 0.0,
0.013440800000000001, 0.0047039999999999998, 0.089520000000000002, 0.0, -0.336, 0.0672, 4.8, 0.0020960200000000001, 0.0,
0.0, 0.0020960200000000001, 0.0, 0.0012999999999999999, 0.0, 0.0, -0.015579999999999998, 0.82, 0.00029999999999999997, 0.0,
0.0, 0.00029999999999999997, 0.0, 0.00040000000000000002, 0.0, 0.0, 0.0, 0.34, 0.00024216, 0.0, 0.0, 0.00024216, 0.0,
4.0000000000000003e-05, 0.0, 0, 0.0028799999999999997, 0.09]
dynparm_test2 = [0.47804562306292275, 0.5871876506259908, 0.9487349009746813, 0.35387185413632094, 0.28071604959871554, 0.368556182617345,
0.24355010647230801, 0.6753463418802456, 0.9728151452953864, 0.6620741264406734, 0.34669638996014096, 0.01593886435340608,
0.3521748260592804, 0.5384045845183812, 0.021600503502885116, 0.4654003203805651, 0.5202014161122065, 0.33744920539722967,
0.052363297799702835, 0.07051826001770234, 0.7389222546505236, 0.771434543548951, 0.3652539269897015, 0.2603059367721896,
0.4310648491411889, 0.7071252186366607, 0.16320122542325732, 0.44948421506462655, 0.48085540250421277, 0.08408482356412372,
0.923593157615906, 0.46852453511703684, 0.6670004526434297, 0.573634975268657, 0.12665814747855264, 0.3152822779549781,
0.21725524421221942, 0.5727451645276381, 0.7984025459787872, 0.7630923700903489, 0.27670044727992893, 0.919667661316697,
0.8828363383223908, 0.3618378476984413, 0.20690997158181246, 0.8321536435628591, 0.556153477173109, 0.3985225289975399,
0.7128303168401632, 0.433898343199971, 0.35116090526732047, 0.551636779637059, 0.03397585970570116, 0.28333059714010744,
0.4785548774382852, 0.16258401709779136, 0.8246056496848533, 0.23027686557606952, 0.26655111443159285, 0.6087446254045791]
q_num_subs = dict(zip(puma560_def.q, q_test))
T = puma560.geo.T[-1]
T = T.subs(q_num_subs)
T = numpy.matrix(T).astype(numpy.float64)
J = puma560.kin.J[-1]
J = J.subs(q_num_subs)
J = numpy.matrix(J).astype(numpy.float64)
M_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.M_code, 'M', 'M_puma560', puma560_def)
c_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.c_code, 'c', 'c_puma560', puma560_def)
C_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.C_code, 'C', 'C_puma560', puma560_def)
g_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.g_code, 'g', 'g_puma560', puma560_def)
tau_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.invdyn_code, 'tau', 'tau_puma560', puma560_def)
H_func_def = sympybotics.robotcodegen.robot_code_to_func(
'python', puma560.H_code, 'H', 'H_puma560', puma560_def)
l = locals()
exec_(M_func_def, globals(), l)
exec_(c_func_def, globals(), l)
exec_(C_func_def, globals(), l)
exec_(g_func_def, globals(), l)
exec_(tau_func_def, globals(), l)
exec_(H_func_def, globals(), l)
tau_puma560 = l['tau_puma560']
g_puma560 = l['g_puma560']
c_puma560 = l['c_puma560']
C_puma560 = l['C_puma560']
M_puma560 = l['M_puma560']
H_puma560 = l['H_puma560']
tau = tau_puma560(dynparm_test, q_test, dq_test, ddq_test)
tau = numpy.matrix(tau).T.astype(numpy.float64)
g = g_puma560(dynparm_test, q_test)
g = numpy.matrix(g).T.astype(numpy.float64)
c = c_puma560(dynparm_test, q_test, dq_test)
c = numpy.matrix(c).T.astype(numpy.float64)
C = C_puma560(dynparm_test, q_test, dq_test)
C = numpy.matrix(C).reshape(puma560.dof, puma560.dof).astype(numpy.float64)
C_dq = C * numpy.matrix(dq_test).T
M = M_puma560(dynparm_test, q_test)
M = numpy.matrix(M).reshape(puma560.dof, puma560.dof).astype(numpy.float64)
H = H_puma560(q_test, dq_test, ddq_test)
H = numpy.matrix(H).reshape(puma560.dof, puma560.dyn.n_dynparms
).astype(numpy.float64)
tau_t2 = tau_puma560(dynparm_test2, q_test, dq_test, ddq_test)
tau_t2 = numpy.matrix(tau_t2).T.astype(numpy.float64)
g_t2 = g_puma560(dynparm_test2, q_test)
g_t2 = numpy.matrix(g_t2).T.astype(numpy.float64)
c_t2 = c_puma560(dynparm_test2, q_test, dq_test)
c_t2 = numpy.matrix(c_t2).T.astype(numpy.float64)
C_t2 = C_puma560(dynparm_test2, q_test, dq_test)
C_t2 = numpy.matrix(C_t2).reshape(puma560.dof, puma560.dof
).astype(numpy.float64)
C_dq_t2 = C_t2 * numpy.matrix(dq_test).T
M_t2 = M_puma560(dynparm_test2, q_test)
M_t2 = numpy.matrix(M_t2).reshape(puma560.dof, puma560.dof
).astype(numpy.float64)
T_pcorke = numpy.matrix([[-0.655113870655343, -0.474277925274361, 0.588120962109346, 0.368531204501578],
[ 0.524340309498464, 0.275038163391149, 0.805866768463298, 0.548861637700235],
[-0.543960528264717, 0.836310025215453, 0.0685017183295358, -0.0731561881633544],
[ 0.0, 0.0, 0.0, 1.0]])
J_pcorke = numpy.matrix([[ -0.548861637700235, -0.053505039458511, 0.181558777632408, 0.0, 0.0, 0.0],
[ 0.368531204501578, -0.0498902657753524, 0.169292757497222, 0.0, 0.0, 0.0],
[ 2.66713734431434e-17, -0.643843409557299, -0.35547724614047, 0.0, 0.0, 0.0],
[-2.77555756156289e-17, -0.681969181663072, -0.681969181663072, 0.618588326585383, -0.778592276991744, 0.588120962109346],
[-1.04083408558608e-16, 0.731380909828662, 0.731380909828662, 0.576796808883883, 0.541217849732837, 0.805866768463298],
[ 1.0, 5.72458747072346e-17, 5.72458747072346e-17, 0.533529683779323, 0.317611878460765, 0.0685017183295358]])
tau_pcorke = numpy.matrix([[ 2.71610302653938],
[ -28.7944111980059],
[ -7.07013200484716],
[0.00682888994276653],
[-0.0220889100229662],
[5.59805923645946e-5]])
g_pcorke = numpy.matrix([[2.88527195030991e-15],
[ -31.1492065095002],
[ -7.04528104551999],
[ 0.00449992182016002],
[ -0.026719893613902],
[ 0.0]])
c_pcorke = numpy.matrix([[ 0.634177626441184],
[ 1.12398760139525],
[ -0.373925639216401],
[-8.54577359118394e-5],
[ 0.00162606594660281],
[ 1.1075729558421e-5]])
M_pcorke = numpy.matrix([[ 2.88092539107775, 0.454967124647697, -0.0748835030743064, 0.00187610810095465, 0.00080480941146298, 2.74006873318143e-6],
[ 0.454967124647696, 2.16866309038072, 0.368189596401607, -0.000307104503389342, 0.00234804941178354, 7.53260796283046e-6],
[-0.0748835030743065, 0.368189596401607, 0.315830104422493, -0.000267827816326753, 0.00156601844062265, 7.53260796283046e-6],
[0.00187610810095465, -0.000307104503389342, -0.000267827816326753, 0.00169083802882858, 1.98357371583556e-20, 3.4606953693396e-5],
[0.00080480941146298, 0.00234804941178354, 0.00156601844062265, 2.86112061631228e-20, 0.00064216, 2.44929359829471e-21],
[2.74006873318143e-6, 7.53260796283046e-6, 7.53260796283046e-6, 3.4606953693396e-5, 2.44929359829471e-21, 4.0e-5]])
tau_assrt2 = numpy.matrix([[ 5.46017281703168],
[ -8.53813836226581],
[-0.490552199963579],
[ 16.4258371315422],
[ 6.18803453956645],
[ 7.99017179545593]])
g_assrt2 = numpy.matrix([[-1.77635683940025e-15],
[ -12.2719368424473],
[ -5.98183554268347],
[ 18.5386690234033],
[ 0.512467781908107],
[ 7.9940463468124]])
c_assrt2 = numpy.matrix([[ 3.69185531689127],
[ -1.34087787458136],
[ 1.44951875057327],
[ -2.77730540324791],
[ 3.31210163468715],
[-0.788977149763209]])
M_assrt2 = numpy.matrix([[ 3.65840168922149, -0.555774017452474, -1.39220275090878, 0.985927680308992, -0.439435427090482, 0.563816026114764],
[-0.555774017452474, 5.33926241554461, 4.32747701210166, -1.24139932228482, 1.97231403813762, -0.207221260322988],
[ -1.39220275090878, 4.32747701210166, 4.13345124166948, -1.07958817736071, 1.65363538346271, -0.00546039833881495],
[ 0.985927680308992, -1.24139932228482, -1.07958817736071, 1.75689881264024, -0.358749490679038, 0.276358110012213],
[-0.439435427090482, 1.97231403813762, 1.65363538346271, -0.358749490679038, 1.67868951032001, 0.396167549434339],
[ 0.563816026114764, -0.207221260322988, -0.00546039833881495, 0.276358110012213, 0.396167549434339, 0.162584017097791]])
assert_precision = 10
assert not numpy.any(numpy.round(T_pcorke - T, assert_precision))
assert not numpy.any(numpy.round(J_pcorke - J, assert_precision))
assert not numpy.any(numpy.round(tau_pcorke - tau, assert_precision))
assert not numpy.any(numpy.round(g_pcorke - g, assert_precision))
assert not numpy.any(numpy.round(c_pcorke - c, assert_precision))
assert not numpy.any(numpy.round(C_dq - c, assert_precision))
assert not numpy.any(numpy.round(M_pcorke - M, assert_precision))
assert not numpy.any(numpy.round(
tau_pcorke - H * numpy.matrix(dynparm_test).T, assert_precision))
assert not numpy.any(numpy.round(tau_assrt2 - tau_t2, assert_precision))
assert not numpy.any(numpy.round(g_assrt2 - g_t2, assert_precision))
assert not numpy.any(numpy.round(c_assrt2 - c_t2, assert_precision))
assert not numpy.any(numpy.round(C_dq_t2 - c_t2, assert_precision))
assert not numpy.any(numpy.round(M_assrt2 - M_t2, assert_precision))
assert not numpy.any(numpy.round(
tau_assrt2 - H * numpy.matrix(dynparm_test2).T, assert_precision))