links = Links(x0)
s.add_robot(links)
T = 50
X_bar = np.mat(np.zeros((links.NX, T)))
U_bar = np.mat(np.random.random_sample((links.NU, T - 1))) / 2
print U_bar
for t in xrange(1, T):
X_bar[:, t] = links.dynamics(X_bar[:, t - 1], U_bar[:, t - 1])
# Plot nominal trajectory
ax = plt.gca()
s.draw(ax=ax)
links.draw_trajectory(mat2tuple(X_bar.T))
#plt.show()
#stop
'''
X = np.mat(np.zeros((car.NX, T)))
As, Bs, Cs = car.linearize_dynamics_trajectory(X_bar, U_bar)
for t in xrange(T-1):
X[:,t+1] = As[:,:,t]*(X[:,t]-X_bar[:,t]) + Bs[:,:,t]*(U_bar[:,t]-U_bar[:,t]) +\
Cs[:,t]
s.draw()
car.draw_trajectory(mat2tuple(X.T))
plt.show()
'''
exit()
#U_bar = 10*np.mat(np.random.random_sample((links.NU, T-1))) - 5
for t in xrange(1,T):
U_bar[0, t-1] = 1*float(t)/T
U_bar[1, t-1] = 1.2
X_bar[:,t] = links.dynamics(X_bar[:,t-1], U_bar[:, t-1])
mus[:,t], Sigmas[:,:,t] = ekf_update(links.dynamics,
lambda x: links.observe(s, x=x),
Q, R, mus[:,t-1], Sigmas[:,:,t-1],
U_bar[:,t-1], None)
# Plot nominal trajectory
#ax = plt.subplot(121)
ax = plt.gca()
s.draw(ax=ax)
links.draw_trajectory(mat2tuple(X_bar.T), mus=X_bar, Sigmas=Sigmas[0:2,0:2,:], color='red')
#plt.show()
#stop
Bel_bar = np.mat(np.zeros((links.NB, T)))
for t in xrange(T):
Bel_bar[:,t] = np.vstack((X_bar[:,t], cov2vec(Sigmas[:,:,t])))
goal_bel = np.copy(Bel_bar[:,-1])
#goal_bel[0:links.NX] = xN;
goal_bel[links.NX:] = 0
# Apply SCP
rho_bel = 0.1
rho_u = 0.1
links = Links(x0)
s.add_robot(links)
T = 50
X_bar = np.mat(np.zeros((links.NX, T)))
U_bar = np.mat(np.random.random_sample((links.NU, T-1)))/2
print U_bar
for t in xrange(1,T):
X_bar[:,t] = links.dynamics(X_bar[:,t-1], U_bar[:, t-1])
# Plot nominal trajectory
ax = plt.gca()
s.draw(ax=ax)
links.draw_trajectory(mat2tuple(X_bar.T))
#plt.show()
#stop
'''
X = np.mat(np.zeros((car.NX, T)))
As, Bs, Cs = car.linearize_dynamics_trajectory(X_bar, U_bar)
for t in xrange(T-1):
X[:,t+1] = As[:,:,t]*(X[:,t]-X_bar[:,t]) + Bs[:,:,t]*(U_bar[:,t]-U_bar[:,t]) +\
Cs[:,t]
s.draw()
car.draw_trajectory(mat2tuple(X.T))
plt.show()
'''
exit()
U_bar[0, t - 1] = 1 * float(t) / T
U_bar[1, t - 1] = 1.2
X_bar[:, t] = links.dynamics(X_bar[:, t - 1], U_bar[:, t - 1])
mus[:, t], Sigmas[:, :, t] = ekf_update(links.dynamics,
lambda x: links.observe(s, x=x), Q,
R, mus[:, t - 1], Sigmas[:, :,
t - 1],
U_bar[:, t - 1], None)
# Plot nominal trajectory
#ax = plt.subplot(121)
ax = plt.gca()
s.draw(ax=ax)
links.draw_trajectory(mat2tuple(X_bar.T),
mus=X_bar,
Sigmas=Sigmas[0:2, 0:2, :],
color='red')
#plt.show()
#stop
Bel_bar = np.mat(np.zeros((links.NB, T)))
for t in xrange(T):
Bel_bar[:, t] = np.vstack((X_bar[:, t], cov2vec(Sigmas[:, :, t])))
goal_bel = np.copy(Bel_bar[:, -1])
#goal_bel[0:links.NX] = xN;
goal_bel[links.NX:] = 0
# Apply SCP
rho_bel = 0.1
rho_u = 0.1
