def test_qp_objective_hessian_against_real_pattern_generator(self):
# instantiate pattern generator
gen = ClassicGenerator(fsm_state='L/R')
gen._preprocess_solution()
N = gen.N
nf = gen.nf
# data follows other convention, i.e.
# U_k = (dddC_x, dddC_y, F_x, F_y)
# assemble pos_H and pos_g for our convention
pos_H = numpy.loadtxt(os.path.join(BASEDIR, "data", "Q.dat"), skiprows=1)
H_mask = numpy.zeros(gen.pos_H.shape, dtype=bool)
# compare values for dddC_kp1_x
H_mask[...] = 0
H_mask[:N, :N] = 1
assert_allclose(
gen.pos_H[H_mask].reshape((N,N)),
pos_H [:N, :N],
rtol=RTOL, atol=ATOL
)
# compare values for dddC_kp1_y
H_mask[...] = 0
H_mask[N+nf:-nf, N+nf:-nf] = 1
assert_allclose(
gen.pos_H[H_mask].reshape((N,N)),
pos_H [N:2*N, N:2*N],
rtol=RTOL, atol=ATOL
)
# compare values for F_k_x
H_mask[...] = 0
H_mask[N:N+nf-1, N:N+nf-1] = 1
assert_allclose(
gen.pos_H[H_mask].reshape((1,1)),
pos_H [2*N:2*N+1, 2*N:2*N+1],
rtol=RTOL, atol=ATOL
)
# compare values for F_k_y
H_mask[...] = 0
H_mask[2*N+nf:-1, 2*N+nf:-1] = 1
assert_allclose(
gen.pos_H[H_mask].reshape((1,1)),
pos_H [2*N+1:, 2*N+1:],
rtol=RTOL, atol=ATOL
)
def test_qp_objective_gradient_against_real_pattern_generator(self):
# instantiate pattern generator
gen = ClassicGenerator(fsm_state='L/R')
secmargin = 0.04
gen.set_security_margin(secmargin, secmargin)
comx = [0.06591456, 0.07638739, -0.1467377]
comy = [2.49008564e-02, 6.61665254e-02, 6.72712187e-01]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
gen.set_initial_values(comx, comy, comz, footx, footy, footq)
# define reference velocity
gen.dC_kp1_x_ref[...] = 0.2
gen.dC_kp1_y_ref[...] = 0.0
gen._preprocess_solution()
N = gen.N
nf = gen.nf
# data follows other convention, i.e.
# U_k = (dddC_x, dddC_y, F_x, F_y)
pos_g = numpy.loadtxt(os.path.join(BASEDIR, "data", "P.dat"),
skiprows=1)
g_mask = numpy.zeros(gen.pos_g.shape, dtype=bool)
# compare values for dddC_kp1_x
g_mask[...] = 0
g_mask[:N] = 1
assert_allclose(gen.pos_g[g_mask], pos_g[:N], rtol=RTOL, atol=ATOL)
# compare values for dddC_kp1_y
g_mask[...] = 0
g_mask[N + nf:-nf] = 1
assert_allclose(gen.pos_g[g_mask],
pos_g[N:2 * N],
rtol=RTOL,
atol=ATOL)
# compare values for F_k_x
g_mask[...] = 0
g_mask[N:N + nf - 1] = 1
assert_allclose(gen.pos_g[g_mask],
pos_g[2 * N:2 * N + 1],
rtol=RTOL,
atol=ATOL)
# compare values for F_k_y
g_mask[...] = 0
g_mask[-nf:-1] = 1
assert_allclose(gen.pos_g[g_mask],
pos_g[2 * N + 1:],
rtol=RTOL,
atol=ATOL)
def test_matrices_of_position_qp_objective(self):
gen = ClassicGenerator()
gen._preprocess_solution()
# check symmetry of Hessian
pos_H = gen.pos_H
assert_allclose(pos_H - pos_H.transpose(), 0.0, rtol=RTOL, atol=ATOL)
# check positive definiteness
U, s, V = linalg.svd(pos_H)
assert_equal((s > 0).all(), True)
# test for equality of block ins pos_H
pos_H_A = pos_H[:gen.N + gen.nf, :gen.N + gen.nf]
pos_H_B = pos_H[-gen.N - gen.nf:, -gen.N - gen.nf:]
assert_allclose(pos_H_A, pos_H_B, rtol=RTOL, atol=ATOL)
def test_matrices_of_position_qp_objective(self):
gen = ClassicGenerator()
gen._preprocess_solution()
# check symmetry of Hessian
pos_H = gen.pos_H
assert_allclose(pos_H - pos_H.transpose(), 0.0, rtol=RTOL, atol=ATOL)
# check positive definiteness
U, s, V = linalg.svd(pos_H)
assert_equal((s > 0).all(), True)
# test for equality of block ins pos_H
pos_H_A = pos_H[ :gen.N+gen.nf, :gen.N+gen.nf]
pos_H_B = pos_H[-gen.N-gen.nf:,-gen.N-gen.nf:]
assert_allclose(pos_H_A, pos_H_B, rtol=RTOL, atol=ATOL)
def test_qp_objective_hessian_against_real_pattern_generator(self):
# instantiate pattern generator
gen = ClassicGenerator(fsm_state='L/R')
gen._preprocess_solution()
N = gen.N
nf = gen.nf
# data follows other convention, i.e.
# U_k = (dddC_x, dddC_y, F_x, F_y)
# assemble pos_H and pos_g for our convention
pos_H = numpy.loadtxt(os.path.join(BASEDIR, "data", "Q.dat"),
skiprows=1)
H_mask = numpy.zeros(gen.pos_H.shape, dtype=bool)
# compare values for dddC_kp1_x
H_mask[...] = 0
H_mask[:N, :N] = 1
assert_allclose(gen.pos_H[H_mask].reshape((N, N)),
pos_H[:N, :N],
rtol=RTOL,
atol=ATOL)
# compare values for dddC_kp1_y
H_mask[...] = 0
H_mask[N + nf:-nf, N + nf:-nf] = 1
assert_allclose(gen.pos_H[H_mask].reshape((N, N)),
pos_H[N:2 * N, N:2 * N],
rtol=RTOL,
atol=ATOL)
# compare values for F_k_x
H_mask[...] = 0
H_mask[N:N + nf - 1, N:N + nf - 1] = 1
assert_allclose(gen.pos_H[H_mask].reshape((1, 1)),
pos_H[2 * N:2 * N + 1, 2 * N:2 * N + 1],
rtol=RTOL,
atol=ATOL)
# compare values for F_k_y
H_mask[...] = 0
H_mask[2 * N + nf:-1, 2 * N + nf:-1] = 1
assert_allclose(gen.pos_H[H_mask].reshape((1, 1)),
pos_H[2 * N + 1:, 2 * N + 1:],
rtol=RTOL,
atol=ATOL)
def test_qp_constraint_setup_against_real_pattern_generator(self):
# instantiate pattern generator
gen = ClassicGenerator(fsm_state='L/R')
secmargin = 0.04
gen.set_security_margin(secmargin, secmargin)
# define initial state
comx = [0.06591456, 0.07638739, -0.1467377]
comy = [2.49008564e-02, 6.61665254e-02, 6.72712187e-01]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
gen.set_initial_values(comx, comy, comz, footx, footy, footq)
# data follows other convention, i.e.
# U_k = (dddC_x, dddC_y, F_x, F_y)
# get box constraints from data
pos_lb = numpy.loadtxt(os.path.join(BASEDIR, "data", "LB.dat"),
skiprows=1)
pos_ub = numpy.loadtxt(os.path.join(BASEDIR, "data", "UB.dat"),
skiprows=1)
# get linear constraints from data
pos_lbA = numpy.loadtxt(os.path.join(BASEDIR, "data", "lbA.dat"),
skiprows=1)[1:70]
# setup QP matrices
gen._preprocess_solution()
# test box constraints
assert_allclose(gen.pos_lb[:34], pos_lb, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_ub[:34], pos_ub, rtol=RTOL, atol=ATOL)
# test linear constraints
assert_allclose(gen.pos_ubA[:-gen.nc_fchange_eq -
gen.nFootPosHullEdges],
pos_lbA,
rtol=RTOL,
atol=ATOL)
gen.simulate()
def test_qp_setup_with_toy_example(self):
gen = ClassicGenerator()
# define input matrices
gen.pos_H[...] = numpy.eye(gen.pos_nv)
gen.pos_g[...] = numpy.ones((gen.pos_nv,))
gen.pos_lb[...] = -numpy.ones((gen.pos_nv,))*0.5
gen.pos_ub[...] = numpy.ones((gen.pos_nv,))*0.5
gen.pos_A[...] = numpy.eye(gen.pos_nc, gen.pos_nv) + numpy.eye(gen.pos_nc, gen.pos_nv, k=1)
gen.pos_lbA[...] = -numpy.ones((gen.pos_nc,))
gen.pos_ubA[...] = numpy.ones((gen.pos_nc,))
# define input matrices
gen.ori_H[...] = numpy.eye(gen.ori_nv)
gen.ori_g[...] = numpy.ones((gen.ori_nv,))
gen.ori_lb[...] = -numpy.ones((gen.ori_nv,))*0.5
gen.ori_ub[...] = numpy.ones((gen.ori_nv,))*0.5
gen.ori_A[...] = numpy.eye(gen.ori_nc, gen.ori_nv) + numpy.eye(gen.ori_nc, gen.ori_nv, k=1)
gen.ori_lbA[...] = -numpy.ones((gen.ori_nc,))
gen.ori_ubA[...] = numpy.ones((gen.ori_nc,))
# define solution
ori_x = -numpy.ones((gen.ori_nv,))*0.5
ori_f = -12.0
pos_x = -numpy.ones((gen.pos_nv,))*0.5
pos_f = -13.5
# test first qp solution
gen._solve_qp()
gen._postprocess_solution()
# get solution
assert_allclose(gen.ori_dofs, ori_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_qp.getObjVal(), ori_f, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_dofs, pos_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_qp.getObjVal(), pos_f, rtol=RTOL, atol=ATOL)
def test_classic_generator_weights(self):
# weights defined for the Heirdt algorithm
a = 1.0 # weight for CoM velocity tracking
b = 0.0 # weight for CoM average velocity tracking
c = 1e-06 # weight for ZMP reference tracking
d = 1e-05 # weight for jerk minimization
gen = ClassicGenerator()
assert_equal(gen.a, a)
assert_equal(gen.b, b)
assert_equal(gen.c, c)
assert_equal(gen.d, d)
def test_qp_constraint_setup_against_real_pattern_generator(self):
# instantiate pattern generator
gen = ClassicGenerator(fsm_state='L/R')
secmargin = 0.04
gen.set_security_margin(secmargin, secmargin)
# define initial state
comx = [0.06591456,0.07638739,-0.1467377]
comy = [2.49008564e-02,6.61665254e-02,6.72712187e-01]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
gen.set_initial_values(comx, comy, comz, footx, footy, footq)
# data follows other convention, i.e.
# U_k = (dddC_x, dddC_y, F_x, F_y)
# get box constraints from data
pos_lb = numpy.loadtxt(os.path.join(BASEDIR, "data", "LB.dat"), skiprows=1)
pos_ub = numpy.loadtxt(os.path.join(BASEDIR, "data", "UB.dat"), skiprows=1)
# get linear constraints from data
pos_lbA = numpy.loadtxt(os.path.join(BASEDIR, "data", "lbA.dat"), skiprows=1)[1:70]
# setup QP matrices
gen._preprocess_solution()
# test box constraints
assert_allclose(gen.pos_lb[:34], pos_lb, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_ub[:34], pos_ub, rtol=RTOL, atol=ATOL)
# test linear constraints
assert_allclose(gen.pos_ubA[:-gen.nc_fchange_eq-gen.nFootPosHullEdges], pos_lbA, rtol=RTOL, atol=ATOL)
gen.simulate()
def test_qp_setup_with_toy_example(self):
gen = ClassicGenerator()
# define input matrices
gen.pos_H[...] = numpy.eye(gen.pos_nv)
gen.pos_g[...] = numpy.ones((gen.pos_nv, ))
gen.pos_lb[...] = -numpy.ones((gen.pos_nv, )) * 0.5
gen.pos_ub[...] = numpy.ones((gen.pos_nv, )) * 0.5
gen.pos_A[...] = numpy.eye(gen.pos_nc, gen.pos_nv) + numpy.eye(
gen.pos_nc, gen.pos_nv, k=1)
gen.pos_lbA[...] = -numpy.ones((gen.pos_nc, ))
gen.pos_ubA[...] = numpy.ones((gen.pos_nc, ))
# define input matrices
gen.ori_H[...] = numpy.eye(gen.ori_nv)
gen.ori_g[...] = numpy.ones((gen.ori_nv, ))
gen.ori_lb[...] = -numpy.ones((gen.ori_nv, )) * 0.5
gen.ori_ub[...] = numpy.ones((gen.ori_nv, )) * 0.5
gen.ori_A[...] = numpy.eye(gen.ori_nc, gen.ori_nv) + numpy.eye(
gen.ori_nc, gen.ori_nv, k=1)
gen.ori_lbA[...] = -numpy.ones((gen.ori_nc, ))
gen.ori_ubA[...] = numpy.ones((gen.ori_nc, ))
# define solution
ori_x = -numpy.ones((gen.ori_nv, )) * 0.5
ori_f = -12.0
pos_x = -numpy.ones((gen.pos_nv, )) * 0.5
pos_f = -13.5
# test first qp solution
gen._solve_qp()
gen._postprocess_solution()
# get solution
assert_allclose(gen.ori_dofs, ori_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_qp.getObjVal(), ori_f, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_dofs, pos_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_qp.getObjVal(), pos_f, rtol=RTOL, atol=ATOL)
def test_generator_with_zero_reference_velocity(self):
gen = ClassicGenerator()
# set reference velocities to zero
gen.dC_kp1_x_ref[...] = 0.0
gen.dC_kp1_y_ref[...] = 0.0
gen.dC_kp1_q_ref[...] = 0.0
for i in range(100):
print 'iteration: i = ', i
gen.solve()
gen.update()
gen.simulate()
gen.c_k_x[0] = gen. C_kp1_x[0]
gen.c_k_x[1] = gen. dC_kp1_x[0]
gen.c_k_x[2] = gen.ddC_kp1_x[0]
gen.c_k_y[0] = gen. C_kp1_y[0]
gen.c_k_y[1] = gen. dC_kp1_y[0]
gen.c_k_y[2] = gen.ddC_kp1_y[0]
gen.c_k_q[0] = gen. C_kp1_q[0]
gen.c_k_q[1] = gen. dC_kp1_q[0]
gen.c_k_q[2] = gen.ddC_kp1_q[0]
RTOL = 1e-05
ATOL = 1e-05
from walking_generator.classic import ClassicGenerator
from walking_generator.visualization import Plotter
gen = ClassicGenerator()
gen = None
show_canvas = True
save_to_file = False
plotter = Plotter(
generator=gen, show_canvas=show_canvas,
save_to_file=save_to_file, filename='./poster_pics', fmt='pdf'
)
plotter.load_from_file('./data.json')
plotter.update()
#plotter.create_reference_plot()
plotter.create_data_plot()
if __name__=='__main__':
raw_input('press key: ')
import os, sys import time import numpy numpy.set_printoptions(threshold=numpy.nan, linewidth =numpy.nan) from walking_generator.visualization import Plotter from walking_generator.classic import ClassicGenerator from walking_generator.interpolation import Interpolation # instantiate pattern generator gen = ClassicGenerator(fsm_state='L/R') # instantiate plotter show_canvas = True save_to_file = False plot = Plotter(gen, show_canvas, save_to_file) # Pattern Generator Preparation # set reference velocities to zero velocity_reference = [0.2,0.0,0.2] gen.set_velocity_reference(velocity_reference) gen.set_security_margin(0.09, 0.05) # set initial values comx = [0.00949035, 0.0, 0.0] comy = [0.095, 0.0, 0.0] comz = 0.814 footx = 0.00949035 footy = 0.095 footq = 0.0 gen.set_initial_values(comx, comy, comz, footx, footy, footq, foot='left')
def test_qp_setup_with_toy_example_hack_from_qpoases_manual(self):
gen = ClassicGenerator()
options = Options()
options.printLevel = PrintLevel.LOW
# define matrices from qpoases manual
H = numpy.array([1.0, 0.0, 0.0, 0.5]).reshape((2, 2))
A = numpy.array([1.0, 1.0]).reshape((1, 2))
g = numpy.array([1.5, 1.0])
lb = numpy.array([0.5, -2.0])
ub = numpy.array([5.0, 2.0])
lbA = numpy.array([-1.0])
ubA = numpy.array([2.0])
x = numpy.array([0.5, -1.5])
f = -6.25e-02
# hack pattern generator
gen.ori_nv = 2
gen.ori_nc = 1
gen.ori_dofs = numpy.zeros((2, ))
gen.ori_qp = SQProblem(gen.ori_nv, gen.ori_nc)
gen.ori_qp.setOptions(options)
gen.ori_H = H
gen.ori_A = A
gen.ori_g = g
gen.ori_lb = lb
gen.ori_ub = ub
gen.ori_lbA = lbA
gen.ori_ubA = ubA
gen.pos_nv = 2
gen.pos_nc = 1
gen.pos_dofs = numpy.zeros((2, ))
gen.pos_qp = SQProblem(gen.pos_nv, gen.pos_nc)
gen.pos_qp.setOptions(options)
gen.pos_H = H
gen.pos_A = A
gen.pos_g = g
gen.pos_lb = lb
gen.pos_ub = ub
gen.pos_lbA = lbA
gen.pos_ubA = ubA
# test first qp solution
gen._solve_qp()
# get solution
# NOTE post_process put entries into array that dont match anymore
gen.pos_qp.getPrimalSolution(gen.pos_dofs)
gen.ori_qp.getPrimalSolution(gen.ori_dofs)
assert_allclose(gen.pos_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
# define matrices for warmstart
H_new = numpy.array([1.0, 0.5, 0.5, 0.5]).reshape((2, 2))
A_new = numpy.array([1.0, 5.0]).reshape((1, 2))
g_new = numpy.array([1.0, 1.5])
lb_new = numpy.array([0.0, -1.0])
ub_new = numpy.array([5.0, -0.5])
lbA_new = numpy.array([-2.0])
ubA_new = numpy.array([1.0])
x = numpy.array([0.5, -0.5])
f = -1.875e-01
# hack pattern generator
gen.ori_H = H_new
gen.ori_A = A_new
gen.ori_g = g_new
gen.ori_lb = lb_new
gen.ori_ub = ub_new
gen.ori_lbA = lbA_new
gen.pos_H = H_new
gen.pos_A = A_new
gen.pos_g = g_new
gen.pos_lb = lb_new
gen.pos_ub = ub_new
gen.pos_lbA = lbA_new
# test qp warmstart
gen._solve_qp()
# get solution
# NOTE post_process put entries into array that dont match anymore
gen.pos_qp.getPrimalSolution(gen.pos_dofs)
gen.ori_qp.getPrimalSolution(gen.ori_dofs)
assert_allclose(gen.pos_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
def test_qp_objective_gradient_against_real_pattern_generator(self):
# instantiate pattern generator
gen = ClassicGenerator(fsm_state='L/R')
secmargin = 0.04
gen.set_security_margin(secmargin, secmargin)
comx = [0.06591456,0.07638739,-0.1467377]
comy = [2.49008564e-02,6.61665254e-02,6.72712187e-01]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
gen.set_initial_values(comx, comy, comz, footx, footy, footq)
# define reference velocity
gen.dC_kp1_x_ref[...] = 0.2
gen.dC_kp1_y_ref[...] = 0.0
gen._preprocess_solution()
N = gen.N
nf = gen.nf
# data follows other convention, i.e.
# U_k = (dddC_x, dddC_y, F_x, F_y)
pos_g = numpy.loadtxt(os.path.join(BASEDIR, "data", "P.dat"), skiprows=1)
g_mask = numpy.zeros(gen.pos_g.shape, dtype=bool)
# compare values for dddC_kp1_x
g_mask[...] = 0
g_mask[:N] = 1
assert_allclose(
gen.pos_g[g_mask],
pos_g[:N],
rtol=RTOL, atol=ATOL
)
# compare values for dddC_kp1_y
g_mask[...] = 0
g_mask[N+nf:-nf] = 1
assert_allclose(
gen.pos_g[g_mask],
pos_g[N:2*N],
rtol=RTOL, atol=ATOL
)
# compare values for F_k_x
g_mask[...] = 0
g_mask[N:N+nf-1] = 1
assert_allclose(
gen.pos_g[g_mask],
pos_g[2*N:2*N+1],
rtol=RTOL, atol=ATOL
)
# compare values for F_k_y
g_mask[...] = 0
g_mask[-nf:-1] = 1
assert_allclose(
gen.pos_g[g_mask],
pos_g[2*N+1:],
rtol=RTOL, atol=ATOL
)
def test_real_pattern_genererator_data(self):
""" verify if data is reproducible """
interp_data = numpy.loadtxt(
os.path.join(BASEDIR, "data", "walkForward2m_sInterpolation.dat"))
# instantiate pattern generator
gen = ClassicGenerator()
# index of 3 step after idle motion
# 5.0s : idle time
# 5.8s : first step
# 6.6s : second step
# 7.4s : third step ('left')
index = 1479
for i in range(1):
qp_idx = index + i
ini_idx = index + i
qp_dofs = interp_data[qp_idx, -(2 * gen.N + 2 * gen.nf):]
# extract states
c_k_x = interp_data[ini_idx, 1]
c_k_y = interp_data[ini_idx, 2]
c_k_q = interp_data[ini_idx, 4]
dc_k_x = interp_data[ini_idx, 5]
dc_k_y = interp_data[ini_idx, 6]
dc_k_z = interp_data[ini_idx, 7]
dc_k_q = interp_data[ini_idx, 8]
h_com = interp_data[ini_idx, 3]
ddc_k_x = interp_data[ini_idx, 9]
ddc_k_y = interp_data[ini_idx, 10]
ddc_k_z = interp_data[ini_idx, 11]
ddc_k_q = interp_data[ini_idx, 12]
comx = (c_k_x, dc_k_x, ddc_k_x)
comy = (c_k_y, dc_k_y, ddc_k_y)
comq = (c_k_q, dc_k_q, ddc_k_q)
comz = h_com
foot_x = 0.0
foot_y = 0.0
foot_q = 0.0
stuff = gen.update()
foot = stuff[-2]
com_q = (0, 0, 0)
# define initial values
#gen.set_initial_values(
#)
# check if current ZMP is calculated correctly
z_k_x = interp_data[ini_idx, 13]
z_k_y = interp_data[ini_idx, 14]
assert_allclose(gen.z_k_x, z_k_x)
assert_allclose(gen.z_k_y, z_k_y)
# extract single dofs from data block
dddC_k_x = qp_dofs[:gen.N]
F_k_x = qp_dofs[gen.N:gen.N + gen.nf]
dddC_k_y = qp_dofs[-gen.N - gen.nf:-gen.nf]
F_k_y = qp_dofs[-gen.nf:]
print 'dddC_k_x\n', dddC_k_x
print 'dddC_k_y\n', dddC_k_y
print 'F_k_x \n', F_k_x
print 'F_k_y \n', F_k_y
return 0
# set reference velocities
gen.dC_kp1_x_ref[...] = 0.2
gen.dC_kp1_y_ref[...] = 0.0
gen.dC_kp1_q_ref[...] = 0.0
# initialize data
gen.set_initial_values(comx, comy, comz, supportfootx, supportfooty,
supportfootq)
def test_qp_setup_with_toy_example_hack_from_qpoases_manual(self):
gen = ClassicGenerator()
options = Options()
options.printLevel = PrintLevel.LOW
# define matrices from qpoases manual
H = numpy.array([ 1.0, 0.0, 0.0, 0.5 ]).reshape((2,2))
A = numpy.array([ 1.0, 1.0 ]).reshape((1,2))
g = numpy.array([ 1.5, 1.0 ])
lb = numpy.array([ 0.5, -2.0 ])
ub = numpy.array([ 5.0, 2.0 ])
lbA = numpy.array([ -1.0 ])
ubA = numpy.array([ 2.0 ])
x = numpy.array([0.5, -1.5])
f = -6.25e-02
# hack pattern generator
gen.ori_nv = 2
gen.ori_nc = 1
gen.ori_dofs = numpy.zeros((2,))
gen.ori_qp = SQProblem(gen.ori_nv, gen.ori_nc)
gen.ori_qp.setOptions(options)
gen.ori_H = H
gen.ori_A = A
gen.ori_g = g
gen.ori_lb = lb
gen.ori_ub = ub
gen.ori_lbA = lbA
gen.ori_ubA = ubA
gen.pos_nv = 2
gen.pos_nc = 1
gen.pos_dofs = numpy.zeros((2,))
gen.pos_qp = SQProblem(gen.pos_nv, gen.pos_nc)
gen.pos_qp.setOptions(options)
gen.pos_H = H
gen.pos_A = A
gen.pos_g = g
gen.pos_lb = lb
gen.pos_ub = ub
gen.pos_lbA = lbA
gen.pos_ubA = ubA
# test first qp solution
gen._solve_qp()
# get solution
# NOTE post_process put entries into array that dont match anymore
gen.pos_qp.getPrimalSolution(gen.pos_dofs)
gen.ori_qp.getPrimalSolution(gen.ori_dofs)
assert_allclose(gen.pos_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
# define matrices for warmstart
H_new = numpy.array([ 1.0, 0.5, 0.5, 0.5 ]).reshape((2,2))
A_new = numpy.array([ 1.0, 5.0 ]).reshape((1,2))
g_new = numpy.array([ 1.0, 1.5 ])
lb_new = numpy.array([ 0.0, -1.0 ])
ub_new = numpy.array([ 5.0, -0.5 ])
lbA_new = numpy.array([ -2.0 ])
ubA_new = numpy.array([ 1.0 ])
x = numpy.array([0.5, -0.5])
f = -1.875e-01
# hack pattern generator
gen.ori_H = H_new
gen.ori_A = A_new
gen.ori_g = g_new
gen.ori_lb = lb_new
gen.ori_ub = ub_new
gen.ori_lbA = lbA_new
gen.pos_H = H_new
gen.pos_A = A_new
gen.pos_g = g_new
gen.pos_lb = lb_new
gen.pos_ub = ub_new
gen.pos_lbA = lbA_new
# test qp warmstart
gen._solve_qp()
# get solution
# NOTE post_process put entries into array that dont match anymore
gen.pos_qp.getPrimalSolution(gen.pos_dofs)
gen.ori_qp.getPrimalSolution(gen.ori_dofs)
assert_allclose(gen.pos_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.pos_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_dofs, x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.ori_qp.getObjVal(), f, rtol=RTOL, atol=ATOL)
def test_compare_constraint_matrices_to_classic_generator(self):
# define initial values
comx = [0.00949035, 0.0, 0.0]
comy = [0.095, 0.0, 0.0]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
# Pattern Generator Preparation
classic = ClassicGenerator(fsm_state='R/L')
# set reference velocities to zero
classic.set_velocity_reference([0.2, 0.0, -0.2])
classic.set_security_margin(0.04, 0.04)
# set initial values
classic.set_initial_values(comx,
comy,
comz,
footx,
footy,
footq,
foot='left')
# build up QP matrices
classic._preprocess_solution()
# reference them for comparison
classic_pos_A = classic.pos_A
classic_pos_lbA = classic.pos_lbA
classic_pos_ubA = classic.pos_ubA
classic_ori_A = classic.ori_A
classic_ori_lbA = classic.ori_lbA
classic_ori_ubA = classic.ori_ubA
# define initial values
comx = [0.00949035, 0.0, 0.0]
comy = [0.095, 0.0, 0.0]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
# Pattern Generator Preparation
nmpc = NMPCGenerator(fsm_state='R/L')
nmpc.set_velocity_reference([0.2, 0.0, -0.2])
nmpc.set_security_margin(0.04, 0.04)
# set initial values
nmpc.set_initial_values(comx,
comy,
comz,
footx,
footy,
footq,
foot='left')
# build up QP matrices
nmpc._preprocess_solution()
# reference them for comparison
nmpc_pos_A = nmpc.A_pos_x
nmpc_pos_lbA = nmpc.lbA_pos
nmpc_pos_ubA = nmpc.ubA_pos
nmpc_ori_A = nmpc.A_ori
nmpc_ori_lbA = nmpc.lbA_ori
nmpc_ori_ubA = nmpc.ubA_ori
nmpc_A_pos = nmpc.qp_A[:nmpc.nc_pos, :2 * (nmpc.N + nmpc.nf)]
nmpc_lbA_pos = nmpc.qp_lbA[:nmpc.nc_pos]
nmpc_ubA_pos = nmpc.qp_ubA[:nmpc.nc_pos]
nmpc_A_ori = nmpc.qp_A[-nmpc.nc_ori:, -2 * nmpc.N:]
nmpc_lbA_ori = nmpc.qp_lbA[-nmpc.nc_ori:]
nmpc_ubA_ori = nmpc.qp_ubA[-nmpc.nc_ori:]
# compare matrices
# position common sub expressions
assert_allclose(classic_pos_A, nmpc_pos_A, atol=ATOL, rtol=RTOL)
assert_allclose(classic_pos_lbA, nmpc_pos_lbA, atol=ATOL, rtol=RTOL)
assert_allclose(classic_pos_ubA, nmpc_pos_ubA, atol=ATOL, rtol=RTOL)
assert_allclose(classic_pos_A, nmpc_A_pos, atol=ATOL, rtol=RTOL)
assert_allclose(classic_pos_lbA, nmpc_lbA_pos, atol=ATOL, rtol=RTOL)
assert_allclose(classic_pos_ubA, nmpc_ubA_pos, atol=ATOL, rtol=RTOL)
# orientation common sub expressions
assert_allclose(classic_ori_A, nmpc_ori_A, atol=ATOL, rtol=RTOL)
assert_allclose(classic_ori_lbA, nmpc_ori_lbA, atol=ATOL, rtol=RTOL)
assert_allclose(classic_ori_ubA, nmpc_ori_ubA, atol=ATOL, rtol=RTOL)
assert_allclose(classic_ori_A, nmpc_A_ori, atol=ATOL, rtol=RTOL)
assert_allclose(classic_ori_lbA, nmpc_lbA_ori, atol=ATOL, rtol=RTOL)
assert_allclose(classic_ori_ubA, nmpc_ubA_ori, atol=ATOL, rtol=RTOL)
# for j in range(RightFootBuffer.shape[0]):
# LeftFootTrajX[j] = LeftFootBuffer[j].z
# RightFootTrajX[j] = RightFootBuffer[j].z
# LeftFootTrajX.tofile("LeftFootBuffer"+format(i),sep="\n")
# RightFootTrajX.tofile("RightFootBuffer"+format(i),sep="\n")
# LFx = numpy.append(LFx,LeftFootTrajX)
# RFx = numpy.append(RFx,RightFootTrajX)
# LFx.tofile("lfttraj.txt",sep="\n")
# RFx.tofile("rfttraj.txt",sep="\n")
# print "done"
#initialize pattern generator
gen = ClassicGenerator()
comx = [0.00124774,0.0,0.0]
comy = [0.00157175,0.0,0.0]
comz = 0.814
supportfootx = 0.00949035
supportfooty = 0.095
supportfootq = 0.0
secmarginx = 0.09
secmarginy = 0.05
gen._initState(comx,comy,comz,\
supportfootx,supportfooty,supportfootq,secmarginx,secmarginy)
gen.simulate()
# initialize interpolation
samplingPeriod = 0.005
inter = Interpolation(0.005,gen)
import os, sys import time import numpy numpy.set_printoptions(threshold=numpy.nan, linewidth=numpy.nan) from walking_generator.visualization import Plotter from walking_generator.classic import ClassicGenerator from walking_generator.interpolation import Interpolation # instantiate pattern generator gen = ClassicGenerator(fsm_state='L/R') # instantiate plotter show_canvas = True save_to_file = False plot = Plotter(gen, show_canvas, save_to_file) # Pattern Generator Preparation # set reference velocities to zero velocity_reference = [0.2, 0.0, 0.2] gen.set_velocity_reference(velocity_reference) gen.set_security_margin(0.09, 0.05) # set initial values comx = [0.00949035, 0.0, 0.0] comy = [0.095, 0.0, 0.0] comz = 0.814 footx = 0.00949035 footy = 0.095 footq = 0.0 gen.set_initial_values(comx, comy, comz, footx, footy, footq, foot='left')
def test_compare_submatrices_to_classic_generator(self):
# define initial values
comx = [0.00949035, 0.0, 0.0]
comy = [0.095, 0.0, 0.0]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
# Pattern Generator Preparation
classic = ClassicGenerator()
# set reference velocities to zero
classic.set_velocity_reference([0.2, 0.0, -0.2])
classic.set_security_margin(0.04, 0.04)
# set initial values
classic.set_initial_values(comx,
comy,
comz,
footx,
footy,
footq,
foot='left')
# build up QP matrices
classic._preprocess_solution()
# reference them for comparison
pos_H = classic.pos_H
classic_Q_k_x = pos_H[:classic.N + classic.nf, :classic.N + classic.nf]
classic_Q_k_y = pos_H[-(classic.N + classic.nf):,
-(classic.N + classic.nf):]
pos_g = classic.pos_g
classic_p_k_x = pos_g[:classic.N + classic.nf]
classic_p_k_y = pos_g[-(classic.N + classic.nf):]
ori_H = classic.ori_H
classic_Q_k_qR = ori_H[:classic.N, :classic.N]
classic_Q_k_qL = ori_H[-classic.N:, -classic.N:]
ori_g = classic.ori_g
classic_p_k_qR = ori_g[:classic.N]
classic_p_k_qL = ori_g[-classic.N:]
# define initial values
comx = [0.00949035, 0.0, 0.0]
comy = [0.095, 0.0, 0.0]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
# Pattern Generator Preparation
nmpc = NMPCGenerator()
nmpc.set_velocity_reference([0.2, 0.0, -0.2])
nmpc.set_security_margin(0.04, 0.04)
# set initial values
nmpc.set_initial_values(comx,
comy,
comz,
footx,
footy,
footq,
foot='left')
# build up QP matrices
nmpc._preprocess_solution()
# reference them for comparison
nmpc_Q_k_x = nmpc.Q_k_x
nmpc_p_k_x = nmpc.p_k_x
nmpc_p_k_y = nmpc.p_k_y
nmpc_Q_k_qR = nmpc.Q_k_qR
nmpc_Q_k_qL = nmpc.Q_k_qL
nmpc_p_k_qR = nmpc.p_k_qR
nmpc_p_k_qL = nmpc.p_k_qL
# compare matrices
# position common sub expressions
assert_allclose(classic_Q_k_x, nmpc_Q_k_x, atol=ATOL, rtol=RTOL)
assert_allclose(classic_Q_k_y, nmpc_Q_k_x, atol=ATOL, rtol=RTOL)
assert_allclose(classic_p_k_x, nmpc_p_k_x, atol=ATOL, rtol=RTOL)
assert_allclose(classic_p_k_y, nmpc_p_k_y, atol=ATOL, rtol=RTOL)
# orientation common sub expressions
assert_allclose(classic_Q_k_qR, nmpc_Q_k_qR, atol=ATOL, rtol=RTOL)
assert_allclose(classic_Q_k_qL, nmpc_Q_k_qL, atol=ATOL, rtol=RTOL)
assert_allclose(classic_p_k_qR, nmpc_p_k_qR, atol=ATOL, rtol=RTOL)
assert_allclose(classic_p_k_qL, nmpc_p_k_qL, atol=ATOL, rtol=RTOL)
def test_against_real_pattern_genererator_walkForward2m_s(self):
# get test data
data = numpy.loadtxt(os.path.join(BASEDIR, "data",
"walkForward2m_s.dat")
)
# instantiate pattern generator
gen = ClassicGenerator()
secmargin = 0.04
gen.set_security_margin(secmargin, secmargin)
comx = [0.06591456,0.07638739,-0.1467377]
comy = [2.49008564e-02,6.61665254e-02,6.72712187e-01]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
gen.set_initial_values(comx, comy, comz, footx, footy, footq)
# define reference velocity
dC_kp1_x_ref = numpy.zeros((data.shape[0], gen.N), dtype=float)
dC_kp1_y_ref = numpy.zeros((data.shape[0], gen.N), dtype=float)
dC_kp1_q_ref = numpy.zeros((data.shape[0], gen.N), dtype=float)
dC_kp1_x_ref[50:,:] = 0.2 #m/s
dC_kp1_y_ref[50:,:] = 0.2 #m/s
gen.dC_kp1_x_ref[:] = dC_kp1_x_ref[0,:]
gen.dC_kp1_y_ref[:] = dC_kp1_y_ref[0,:]
gen.solve()
for i in range(100):
print 'iteration: i = ', i
gen.dC_kp1_x_ref[:] = dC_kp1_x_ref[i,:]
gen.dC_kp1_y_ref[:] = dC_kp1_y_ref[i,:]
gen.c_k_x[0] = gen. C_kp1_x[0]
gen.c_k_x[1] = gen. dC_kp1_x[0]
gen.c_k_x[2] = gen.ddC_kp1_x[0]
gen.c_k_y[0] = gen. C_kp1_y[0]
gen.c_k_y[1] = gen. dC_kp1_y[0]
gen.c_k_y[2] = gen.ddC_kp1_y[0]
gen.c_k_q[0] = gen. C_kp1_q[0]
gen.c_k_q[1] = gen. dC_kp1_q[0]
gen.c_k_q[2] = gen.ddC_kp1_q[0]
gen.update()
gen.solve()
# get reference from data
dddC_k_x = data[i, 0:16]
dddC_k_y = data[i, 18:34]
F_k_x = data[i, 16:18]
F_k_y = data[i, 34:36]
assert_allclose(gen.dddC_k_x, dddC_k_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.dddC_k_y, dddC_k_y, rtol=RTOL, atol=ATOL)
assert_allclose(gen.dddC_k_y, 0.0, rtol=RTOL, atol=ATOL)
assert_allclose(gen.F_k_x, F_k_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.F_k_y, F_k_y, rtol=RTOL, atol=ATOL)
assert_allclose(gen.F_k_q, 0.0, rtol=RTOL, atol=ATOL)
def test_against_real_pattern_genererator_walkForward2m_s(self):
# get test data
data = numpy.loadtxt(
os.path.join(BASEDIR, "data", "walkForward2m_s.dat"))
# instantiate pattern generator
gen = ClassicGenerator()
secmargin = 0.04
gen.set_security_margin(secmargin, secmargin)
comx = [0.06591456, 0.07638739, -0.1467377]
comy = [2.49008564e-02, 6.61665254e-02, 6.72712187e-01]
comz = 0.814
footx = 0.00949035
footy = 0.095
footq = 0.0
gen.set_initial_values(comx, comy, comz, footx, footy, footq)
# define reference velocity
dC_kp1_x_ref = numpy.zeros((data.shape[0], gen.N), dtype=float)
dC_kp1_y_ref = numpy.zeros((data.shape[0], gen.N), dtype=float)
dC_kp1_q_ref = numpy.zeros((data.shape[0], gen.N), dtype=float)
dC_kp1_x_ref[50:, :] = 0.2 #m/s
dC_kp1_y_ref[50:, :] = 0.2 #m/s
gen.dC_kp1_x_ref[:] = dC_kp1_x_ref[0, :]
gen.dC_kp1_y_ref[:] = dC_kp1_y_ref[0, :]
gen.solve()
for i in range(100):
print 'iteration: i = ', i
gen.dC_kp1_x_ref[:] = dC_kp1_x_ref[i, :]
gen.dC_kp1_y_ref[:] = dC_kp1_y_ref[i, :]
gen.c_k_x[0] = gen.C_kp1_x[0]
gen.c_k_x[1] = gen.dC_kp1_x[0]
gen.c_k_x[2] = gen.ddC_kp1_x[0]
gen.c_k_y[0] = gen.C_kp1_y[0]
gen.c_k_y[1] = gen.dC_kp1_y[0]
gen.c_k_y[2] = gen.ddC_kp1_y[0]
gen.c_k_q[0] = gen.C_kp1_q[0]
gen.c_k_q[1] = gen.dC_kp1_q[0]
gen.c_k_q[2] = gen.ddC_kp1_q[0]
gen.update()
gen.solve()
# get reference from data
dddC_k_x = data[i, 0:16]
dddC_k_y = data[i, 18:34]
F_k_x = data[i, 16:18]
F_k_y = data[i, 34:36]
assert_allclose(gen.dddC_k_x, dddC_k_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.dddC_k_y, dddC_k_y, rtol=RTOL, atol=ATOL)
assert_allclose(gen.dddC_k_y, 0.0, rtol=RTOL, atol=ATOL)
assert_allclose(gen.F_k_x, F_k_x, rtol=RTOL, atol=ATOL)
assert_allclose(gen.F_k_y, F_k_y, rtol=RTOL, atol=ATOL)
assert_allclose(gen.F_k_q, 0.0, rtol=RTOL, atol=ATOL)
def test_against_real_pattern_genererator_walkForward2m_s_while_walking(
self):
"""
Take data where robot is walking with constant CoM velocity and start
test from there. This surpasses FSM implementation and starting and
stopping maneuvers.
"""
interp_data = numpy.loadtxt(
os.path.join(BASEDIR, "data", "walkForward2m_sInterpolation.dat"))
if True:
for i in range(interp_data.shape[0])[1:]:
if interp_data[i, 0] == 7.4:
print 'i = ', i
# instantiate pattern generator in walking mode,
# i.e. in single support mode
gen = ClassicGenerator(fsm_state='R/L')
# define constant x CoM velocity to track
gen.dC_kp1_x_ref[...] = 0.2
gen.dC_kp1_y_ref[...] = 0.0
gen.dC_kp1_q_ref[...] = 0.0
# take walking initial state from interpolation data
# NOTE we take the second step as initial state for the test, because
# this is a nearly periodic solution
# idx is index on states used from interpolation data
idx = 1479
comx = (interp_data[idx, 1], interp_data[idx, 5], interp_data[idx, 9])
comy = (interp_data[idx, 2], interp_data[idx, 6], interp_data[idx, 10])
comz = interp_data[idx, 3]
supportfootx = interp_data[idx, 15]
supportfooty = interp_data[idx, 16]
supportfootq = interp_data[idx, 24]
gen.set_initial_values(comx, comy, comz, supportfootx, supportfooty,
supportfootq)
return 0
idx = 69
for i in range(10):
print 'iteration = ', i
# solve QP for solution
gen.solve()
# get reference values
dddC_k_x_ref = qp_data[idx + i, :gen.N]
F_k_x_ref = qp_data[idx + i, gen.N:gen.N + gen.nf]
dddC_k_y_ref = qp_data[idx + i, gen.N + gen.nf:2 * gen.N + gen.nf]
F_k_y_ref = qp_data[idx + i, -gen.nf:]
# check orientation values, should be constant zero, because we
# assume that robot does not rotate
#assert_allclose(gen.dddC_k_q, 0.0)
#assert_allclose(gen.F_k_q, 0.0)
# check position DoFs against data from C++ implementation
assert_allclose(gen.dddC_k_x, dddC_k_x_ref)
assert_allclose(gen.dddC_k_y, dddC_k_y_ref)
assert_allclose(gen.F_k_x, F_k_x_ref)
assert_allclose(gen.F_k_y, F_k_y_ref)
# simulate to get new values
gen.simulate()
# TODO shifting do I use interpolation or just take the value from
# simulation
gen.c_k_x[...] = (gen.C_kp1_x[0], gen.dC_kp1_x[0],
gen.ddC_kp1_x[0])
gen.c_k_y[...] = (gen.C_kp1_y[0], gen.dC_kp1_y[0],
gen.ddC_kp1_y[0])
gen.c_k_q[...] = (gen.C_kp1_q[0], gen.dC_kp1_q[0],
gen.ddC_kp1_q[0])
gen.update()
def test_real_pattern_genererator_data(self):
""" verify if data is reproducible """
interp_data = numpy.loadtxt(
os.path.join(BASEDIR, "data", "walkForward2m_sInterpolation.dat")
)
# instantiate pattern generator
gen = ClassicGenerator()
# index of 3 step after idle motion
# 5.0s : idle time
# 5.8s : first step
# 6.6s : second step
# 7.4s : third step ('left')
index = 1479
for i in range(1):
qp_idx = index + i
ini_idx = index + i
qp_dofs = interp_data[qp_idx, -(2*gen.N+2*gen.nf):]
# extract states
c_k_x = interp_data[ini_idx, 1]
c_k_y = interp_data[ini_idx, 2]
c_k_q = interp_data[ini_idx, 4]
dc_k_x = interp_data[ini_idx, 5]
dc_k_y = interp_data[ini_idx, 6]
dc_k_z = interp_data[ini_idx, 7]
dc_k_q = interp_data[ini_idx, 8]
h_com = interp_data[ini_idx, 3]
ddc_k_x = interp_data[ini_idx, 9]
ddc_k_y = interp_data[ini_idx, 10]
ddc_k_z = interp_data[ini_idx, 11]
ddc_k_q = interp_data[ini_idx, 12]
comx = (c_k_x, dc_k_x, ddc_k_x)
comy = (c_k_y, dc_k_y, ddc_k_y)
comq = (c_k_q, dc_k_q, ddc_k_q)
comz = h_com
foot_x = 0.0
foot_y = 0.0
foot_q = 0.0
stuff = gen.update()
foot = stuff[-2]
com_q=(0,0,0)
# define initial values
#gen.set_initial_values(
#)
# check if current ZMP is calculated correctly
z_k_x = interp_data[ini_idx, 13]
z_k_y = interp_data[ini_idx, 14]
assert_allclose(gen.z_k_x, z_k_x)
assert_allclose(gen.z_k_y, z_k_y)
# extract single dofs from data block
dddC_k_x = qp_dofs[ :gen.N ]
F_k_x = qp_dofs[ gen.N:gen.N+gen.nf]
dddC_k_y = qp_dofs[-gen.N-gen.nf: -gen.nf]
F_k_y = qp_dofs[ -gen.nf: ]
print 'dddC_k_x\n', dddC_k_x
print 'dddC_k_y\n', dddC_k_y
print 'F_k_x \n', F_k_x
print 'F_k_y \n', F_k_y
return 0
# set reference velocities
gen.dC_kp1_x_ref[...] = 0.2
gen.dC_kp1_y_ref[...] = 0.0
gen.dC_kp1_q_ref[...] = 0.0
# initialize data
gen.set_initial_values(
comx,comy,comz,
supportfootx,supportfooty,supportfootq
)
# for j in range(RightFootBuffer.shape[0]):
# LeftFootTrajX[j] = LeftFootBuffer[j].z
# RightFootTrajX[j] = RightFootBuffer[j].z
# LeftFootTrajX.tofile("LeftFootBuffer"+format(i),sep="\n")
# RightFootTrajX.tofile("RightFootBuffer"+format(i),sep="\n")
# LFx = numpy.append(LFx,LeftFootTrajX)
# RFx = numpy.append(RFx,RightFootTrajX)
# LFx.tofile("lfttraj.txt",sep="\n")
# RFx.tofile("rfttraj.txt",sep="\n")
# print "done"
#initialize pattern generator
gen = ClassicGenerator()
comx = [0.00124774, 0.0, 0.0]
comy = [0.00157175, 0.0, 0.0]
comz = 0.814
supportfootx = 0.00949035
supportfooty = 0.095
supportfootq = 0.0
secmarginx = 0.09
secmarginy = 0.05
gen._initState(comx,comy,comz,\
supportfootx,supportfooty,supportfootq,secmarginx,secmarginy)
gen.simulate()
# initialize interpolation
samplingPeriod = 0.005
inter = Interpolation(0.005, gen)
def test_against_real_pattern_genererator_walkForward2m_s_while_walking(self):
"""
Take data where robot is walking with constant CoM velocity and start
test from there. This surpasses FSM implementation and starting and
stopping maneuvers.
"""
interp_data = numpy.loadtxt(
os.path.join(BASEDIR, "data", "walkForward2m_sInterpolation.dat")
)
if True:
for i in range(interp_data.shape[0])[1:]:
if interp_data[i,0] == 7.4:
print 'i = ', i
# instantiate pattern generator in walking mode,
# i.e. in single support mode
gen = ClassicGenerator(fsm_state='R/L')
# define constant x CoM velocity to track
gen.dC_kp1_x_ref[...] = 0.2
gen.dC_kp1_y_ref[...] = 0.0
gen.dC_kp1_q_ref[...] = 0.0
# take walking initial state from interpolation data
# NOTE we take the second step as initial state for the test, because
# this is a nearly periodic solution
# idx is index on states used from interpolation data
idx = 1479
comx = (interp_data[idx,1], interp_data[idx,5], interp_data[idx, 9])
comy = (interp_data[idx,2], interp_data[idx,6], interp_data[idx,10])
comz = interp_data[idx,3]
supportfootx = interp_data[idx, 15]
supportfooty = interp_data[idx, 16]
supportfootq = interp_data[idx, 24]
gen.set_initial_values(
comx,comy,comz,
supportfootx,supportfooty,supportfootq
)
return 0
idx = 69
for i in range(10):
print 'iteration = ', i
# solve QP for solution
gen.solve()
# get reference values
dddC_k_x_ref = qp_data[idx+i, :gen.N ]
F_k_x_ref = qp_data[idx+i,gen.N :gen.N+gen.nf]
dddC_k_y_ref = qp_data[idx+i,gen.N+gen.nf:2*gen.N+gen.nf]
F_k_y_ref = qp_data[idx+i, -gen.nf: ]
# check orientation values, should be constant zero, because we
# assume that robot does not rotate
#assert_allclose(gen.dddC_k_q, 0.0)
#assert_allclose(gen.F_k_q, 0.0)
# check position DoFs against data from C++ implementation
assert_allclose(gen.dddC_k_x, dddC_k_x_ref)
assert_allclose(gen.dddC_k_y, dddC_k_y_ref)
assert_allclose(gen.F_k_x, F_k_x_ref)
assert_allclose(gen.F_k_y, F_k_y_ref)
# simulate to get new values
gen.simulate()
# TODO shifting do I use interpolation or just take the value from
# simulation
gen.c_k_x[...] = (gen.C_kp1_x[0], gen.dC_kp1_x[0], gen.ddC_kp1_x[0])
gen.c_k_y[...] = (gen.C_kp1_y[0], gen.dC_kp1_y[0], gen.ddC_kp1_y[0])
gen.c_k_q[...] = (gen.C_kp1_q[0], gen.dC_kp1_q[0], gen.ddC_kp1_q[0])
gen.update()
def test_generator_with_zero_reference_velocity(self):
gen = ClassicGenerator()
# set reference velocities to zero
gen.dC_kp1_x_ref[...] = 0.0
gen.dC_kp1_y_ref[...] = 0.0
gen.dC_kp1_q_ref[...] = 0.0
for i in range(100):
print 'iteration: i = ', i
gen.solve()
gen.update()
gen.simulate()
gen.c_k_x[0] = gen.C_kp1_x[0]
gen.c_k_x[1] = gen.dC_kp1_x[0]
gen.c_k_x[2] = gen.ddC_kp1_x[0]
gen.c_k_y[0] = gen.C_kp1_y[0]
gen.c_k_y[1] = gen.dC_kp1_y[0]
gen.c_k_y[2] = gen.ddC_kp1_y[0]
gen.c_k_q[0] = gen.C_kp1_q[0]
gen.c_k_q[1] = gen.dC_kp1_q[0]
gen.c_k_q[2] = gen.ddC_kp1_q[0]
RTOL = 1e-05
ATOL = 1e-05
import os, sys
import time
import numpy
numpy.set_printoptions(threshold=numpy.nan, linewidth =numpy.nan)
from walking_generator.visualization import Plotter
from walking_generator.classic import ClassicGenerator
from walking_generator.combinedqp import NMPCGenerator
from walking_generator.interpolation import Interpolation
# instantiate pattern generator
nmpc = NMPCGenerator(fsm_state='L/R')
classic = ClassicGenerator(fsm_state='L/R')
# Pattern Generator Preparation
nmpc. set_security_margin(0.09, 0.05)
classic.set_security_margin(0.09, 0.05)
# instantiate plotter
show_canvas = False
save_to_file = False
fmt='jpeg'
dpi=200
limits = None#((-0.8, 3.5), (-0.5, 0.8))
nmpc_p = Plotter(nmpc, show_canvas=show_canvas, save_to_file=save_to_file,
filename='./nmpc/nmpc.png', fmt=fmt, dpi=dpi, limits=limits
)
classic_p = Plotter(classic, show_canvas=show_canvas, save_to_file=save_to_file,
filename='./classic/classic.png', fmt=fmt, dpi=dpi, limits=limits
)
import os, sys
import time
import numpy
numpy.set_printoptions(threshold=numpy.nan, linewidth=numpy.nan)
from walking_generator.visualization import Plotter
from walking_generator.classic import ClassicGenerator
from walking_generator.combinedqp import NMPCGenerator
from walking_generator.interpolation import Interpolation
# instantiate pattern generator
nmpc = NMPCGenerator(fsm_state='L/R')
classic = ClassicGenerator(fsm_state='L/R')
# Pattern Generator Preparation
nmpc.set_security_margin(0.09, 0.05)
classic.set_security_margin(0.09, 0.05)
# instantiate plotter
show_canvas = False
save_to_file = False
fmt = 'jpeg'
dpi = 200
limits = None #((-0.8, 3.5), (-0.5, 0.8))
nmpc_p = Plotter(nmpc,
show_canvas=show_canvas,
save_to_file=save_to_file,
filename='./nmpc/nmpc.png',
fmt=fmt,
dpi=dpi,
limits=limits)