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plan_adjuster.py
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plan_adjuster.py
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import numpy as np
import kalman as kalman
from librobot import v_string, v_double, v2_double
class PlanAdjuster:
def __init__(self):
pass
def setup(self,
robot,
M,
H,
W,
N,
C,
D,
dynamic_problem,
enforce_control_constraints):
self.M = M
self.H = H
self.W = W
self.N = N
self.C = C
self.D = D
self.dynamic_problem = dynamic_problem
self.control_constraints_enforced = enforce_control_constraints
active_joints = v_string()
robot.getActiveJoints(active_joints)
lower_position_constraints = v_double()
upper_position_constraints = v_double()
robot.getJointLowerPositionLimits(active_joints, lower_position_constraints)
robot.getJointUpperPositionLimits(active_joints, upper_position_constraints)
torque_limits = v_double()
robot.getJointTorqueLimits(active_joints, torque_limits)
torque_limits = [torque_limits[i] for i in xrange(len(torque_limits))]
torque_limits.extend([0.0 for i in xrange(len(active_joints))])
self.torque_limits = [torque_limits[i] for i in xrange(len(torque_limits))]
def set_simulation_step_size(self, simulation_step_size):
self.simulation_step_size = simulation_step_size
def set_max_joint_velocities_linear_problem(self, max_joint_velocities):
self.max_joint_velocities = max_joint_velocities
def set_model_matrices(self, A, B, V):
self.A = A
self.B = B
self.V = V
def enforce_control_constraints(self, u):
""" Enforces the control constraints on control 'u' and return
the enforced control and enforced control deviation 'u_dash'
"""
if self.dynamic_problem:
for i in xrange(len(u)):
if u[i] > self.torque_limits[i]:
u[i] = self.torque_limits[i]
elif u[i] < -self.torque_limits[i]:
u[i] = -self.torque_limits[i]
else:
for i in xrange(len(u)):
if u[i] > self.max_joint_velocities[i]:
u[i] = self.max_joint_velocities[i]
elif u[i] < -self.max_joint_velocities[i]:
u[i] = -self.max_joint_velocities[i]
return u
def get_linear_model_matrices(self, robot, xs, us, control_durations):
if self.dynamic_problem:
return kalman.get_linear_model_matrices(robot,
xs,
us,
control_durations,
True,
self.M,
self.H,
self.W,
self.N)
else:
As = []
Bs = []
Vs = []
Ms = []
Hs = []
Ws = []
Ns = []
for i in xrange(len(xs) + 1):
As.append(self.A)
Bs.append(self.B)
Vs.append(self.V)
Ms.append(self.M)
Hs.append(self.H)
Ws.append(self.W)
Ns.append(self.N)
return As, Bs, Vs, Ms, Hs, Ws, Ns
def adjust_plan(self,
robot,
plan,
x_estimated,
P_t):
xs = [plan[0][i] for i in xrange(1, len(plan[0]))]
us = [plan[1][i] for i in xrange(1, len(plan[1]))]
zs = [plan[2][i] for i in xrange(1, len(plan[2]))]
control_durations = [plan[3][i] for i in xrange(1, len(plan[3]))]
As, Bs, Vs, Ms, Hs, Ws, Ns = self.get_linear_model_matrices(robot,
xs,
us,
control_durations)
Ls = kalman.compute_gain(As, Bs, self.C, self.D, len(xs) - 1)
xs_adjusted = []
us_adjusted = []
zs_adjusted = []
xs_adjusted.append(xs[0])
zs_adjusted.append(zs[0])
try:
x_tilde = x_estimated - xs[0]
except Exception as e:
print e
print "==================="
print "x_estimated " + str(x_estimated)
print "xs[0] " + str(xs[0])
print "xs " + str(xs)
return (None, None, None, None, True)
for i in xrange(len(xs) - 1):
x_predicted = np.array([xs[i][k] for k in xrange(len(xs[i]))])
u = np.dot(Ls[i], x_estimated - x_predicted) + us[i]
if self.control_constraints_enforced:
u = self.enforce_control_constraints(u)
if self.dynamic_problem:
current_state = v_double()
current_state[:] = [xs_adjusted[i][j] for j in xrange(len(xs_adjusted[i]))]
control = v_double()
control[:] = u
control_error = v_double()
control_error[:] = [0.0 for k in xrange(len(u))]
result = v_double()
robot.propagate(current_state,
control,
control_error,
self.simulation_step_size,
control_durations[i],
result)
xs_adjusted.append(np.array([result[k] for k in xrange(len(result))]))
us_adjusted.append(np.array([u[k] for k in xrange(len(u))]))
zs_adjusted.append(np.array([result[k] for k in xrange(len(result))]))
#print "xs_adjusted: " + str(np.array([result[k] for k in xrange(len(result))]))
#print "xs_prior: " + str(xs[i + 1])
else:
current_state = np.array([xs_adjusted[i][j] for j in xrange(len(xs_adjusted[i]))])
result = np.dot(As[i], current_state) + np.dot(Bs[i], u)
xs_adjusted.append(np.array([result[k] for k in xrange(len(result))]))
us_adjusted.append(np.array([u[k] for k in xrange(len(u))]))
zs_adjusted.append(np.array([result[k] for k in xrange(len(result))]))
u_dash = u - us[i]
z_dash = np.array([0.0 for k in xrange(len(zs_adjusted[-1]))])
"""
Maximum likelikhood observation
"""
""" Kalman prediction and update """
x_tilde_dash, P_dash = kalman.kalman_predict(x_tilde, u_dash, As[i], Bs[i], P_t, Vs[i], Ms[i])
x_tilde, P_t = kalman.kalman_update(x_tilde_dash,
z_dash,
Hs[i],
P_dash,
Ws[i],
Ns[i])
x_estimated = x_tilde + xs[i + 1]
#x_estimated = x_tilde + xs_adjusted[-1]
us_adjusted.append(np.array([0.0 for i in xrange(len(us[0]))]))
control_durations_adjusted = [control_durations[i] for i in xrange(len(control_durations))]
control_durations_adjusted.append(0.0)
return (xs_adjusted, us_adjusted, zs_adjusted, control_durations_adjusted, True)
if __name__ == "__main__":
PlanAdjuster()