def vel_accel_robustaccel(request):
"Velocity + Acceleration + Robust Acceleration constraint"
dtype_a, dtype_ra = request.param
vlims = np.array([[-1, 1], [-1, 2], [-1, 4]], dtype=float)
alims = np.array([[-1, 1], [-1, 2], [-1, 4]], dtype=float)
vel_cnst = constraint.JointVelocityConstraint(vlims)
accl_cnst = constraint.JointAccelerationConstraint(alims, dtype_a)
robust_accl_cnst = constraint.RobustLinearConstraint(
accl_cnst, [0.5, 0.1, 2.0], dtype_ra)
yield vel_cnst, accl_cnst, robust_accl_cnst
def basic_constraints(request):
""" Return a set of relatively simple constraints.
"""
dtype_a, dtype_ra = request.param
vlims = np.array(
[[-1, 1], [-1, 2], [-1, 4], [-3, 4], [-2, 4], [-3, 4], [-2, 5]],
dtype=float) * 10
alims = np.array(
[[-1, 1], [-1, 2], [-1, 4], [-3, 4], [-2, 4], [-3, 4], [-2, 5]],
dtype=float) * 10
vel_cnst = constraint.JointVelocityConstraint(vlims)
accl_cnst = constraint.JointAccelerationConstraint(alims, dtype_a)
robust_accl_cnst = constraint.RobustLinearConstraint(
accl_cnst, [1e-4, 1e-4, 5e-4], dtype_ra)
yield vel_cnst, accl_cnst, robust_accl_cnst
def main():
parser = argparse.ArgumentParser(
description="An example showcasing the usage of robust constraints."
"A velocity constraint and a robust acceleration constraint"
"are considered in this script.")
parser.add_argument("-N",
"--N",
type=int,
help="Number of segments in the discretization.",
default=100)
parser.add_argument("-v", "--verbose", action="store_true", default=False)
parser.add_argument("-du", "--du", default=1e-3, type=float)
parser.add_argument("-dx", "--dx", default=5e-2, type=float)
parser.add_argument("-dc", "--dc", default=9e-3, type=float)
parser.add_argument("-so", "--solver_wrapper", default='ecos')
parser.add_argument("-i", "--interpolation_scheme", default=1, type=int)
args = parser.parse_args()
if args.verbose:
ta.setup_logging("DEBUG")
else:
ta.setup_logging("INFO")
# Parameters
N_samples = 5
dof = 7
# Random waypoints used to obtain a random geometric path.
np.random.seed(9)
way_pts = np.random.randn(N_samples, dof)
# Create velocity bounds, then velocity constraint object
vlim_ = np.random.rand(dof) * 20
vlim = np.vstack((-vlim_, vlim_)).T
# Create acceleration bounds, then acceleration constraint object
alim_ = np.random.rand(dof) * 2
alim = np.vstack((-alim_, alim_)).T
path = ta.SplineInterpolator(np.linspace(0, 1, 5), way_pts)
pc_vel = constraint.JointVelocityConstraint(vlim)
pc_acc = constraint.JointAccelerationConstraint(
alim,
discretization_scheme=constraint.DiscretizationType.Interpolation)
robust_pc_acc = constraint.RobustLinearConstraint(
pc_acc, [args.du, args.dx, args.dc], args.interpolation_scheme)
instance = algo.TOPPRA([pc_vel, robust_pc_acc],
path,
gridpoints=np.linspace(0, 1, args.N + 1),
solver_wrapper=args.solver_wrapper)
X = instance.compute_feasible_sets()
K = instance.compute_controllable_sets(0, 0)
_, sd_vec, _ = instance.compute_parameterization(0, 0)
X = np.sqrt(X)
K = np.sqrt(K)
plt.plot(X[:, 0], c='green', label="Feasible sets")
plt.plot(X[:, 1], c='green')
plt.plot(K[:, 0], '--', c='red', label="Controllable sets")
plt.plot(K[:, 1], '--', c='red')
plt.plot(sd_vec, label="Velocity profile")
plt.legend()
plt.title("Path-position path-velocity plot")
plt.show()
jnt_traj, aux_traj = instance.compute_trajectory(0, 0)
ts_sample = np.linspace(0, jnt_traj.duration, 100)
qs_sample = jnt_traj.evaldd(ts_sample)
plt.plot(ts_sample, qs_sample)
plt.show()
np.random.seed(9)
way_pts = np.random.randn(N_samples, dof)
# Create velocity bounds, then velocity constraint object
vlim_ = np.random.rand(dof) * 20
vlim = np.vstack((-vlim_, vlim_)).T
# Create acceleration bounds, then acceleration constraint object
alim_ = np.random.rand(dof) * 2
alim = np.vstack((-alim_, alim_)).T
path = ta.SplineInterpolator(np.linspace(0, 1, 5), way_pts)
pc_vel = constraint.JointVelocityConstraint(vlim)
pc_acc = constraint.JointAccelerationConstraint(
alim, discretization_scheme=constraint.DiscretizationType.Interpolation)
robust_pc_acc = constraint.RobustLinearConstraint(pc_acc,
[args.du, args.dx, args.dc],
args.interpolation_scheme)
instance = algo.TOPPRA(
[pc_vel, robust_pc_acc],
path,
gridpoints=np.linspace(0, 1, args.N + 1),
solver_wrapper=args.solver_wrapper,
)
X = instance.compute_feasible_sets()
K = instance.compute_controllable_sets(0, 0)
_, sd_vec, _ = instance.compute_parameterization(0, 0)
X = np.sqrt(X)
K = np.sqrt(K)