def callback(self, msg):
if self.pose_0 == None:
self.pose_0 = [
msg.position.x, msg.position.y, msg.position.z,
msg.orientation.x, msg.orientation.y, msg.orientation.z,
msg.orientation.w
]
self.pose_1 = [
msg.position.x, msg.position.y, msg.position.z,
msg.orientation.x, msg.orientation.y, msg.orientation.z,
msg.orientation.w
]
else:
self.pose_0 = self.pose_1
self.pose_1 = [
msg.position.x, msg.position.y, msg.position.z,
msg.orientation.x, msg.orientation.y, msg.orientation.z,
msg.orientation.w
]
if np.sum(
np.abs(np.array(self.pose_0) - np.array(self.pose_1))) > 0.01:
print('Moving')
self.traj.append(self.pose_1)
else:
if len(self.traj) > 5:
# Fit DMP to motion segment
path = np.array(self.traj)
dmp = DMP(path[0, :],
path[-1, :],
Nb=500,
dt=0.01,
d=path.shape[1],
jnames=self.name)
params = dmp.imitate_path(
path +
1e-5 * np.random.randn(path.shape[0], path.shape[1]))
#print(params)
dmp.reset_state()
self.dmp_count += 1
with open("./dmp%05d.npy" % self.dmp_count, "w") as f:
pickle.dump(dmp, f, pickle.HIGHEST_PROTOCOL)
y_r, dy_r, ddy_r = dmp.rollout()
plt.subplot(1, 2, 1)
plt.cla()
plt.plot(y_r)
plt.subplot(1, 2, 2)
plt.cla()
plt.plot(path)
plt.draw()
plt.pause(0.1)
self.traj = []
print('Stationary')
def callback(self, msg):
if self.pose_prev is not None:
self.pose_prev = [msg.position.x, msg.position.y, msg.position.z,
msg.orientation.x, msg.orientation.y, msg.orientation.z,
msg.orientation.w]
self.pose_current = [msg.position.x, msg.position.y, msg.position.z,
msg.orientation.x, msg.orientation.y, msg.orientation.z,
msg.orientation.w]
else:
self.pose_prev = self.pose_current
self.pose_current = [msg.position.x, msg.position.y, msg.position.z,
msg.orientation.x, msg.orientation.y, msg.orientation.z,
msg.orientation.w]
if np.sum(np.abs(np.array(self.pose_prev) - np.array(self.pose_current))) > 0.01:
print('Moving')
self.traj.append(self.pose_current)
else:
if len(self.traj) > 5:
# Fit DMP to motion segment
path = np.array(self.traj)
dmp = DMP(path[0, :], path[-1, :], num_basis_funcs=500, dt=0.01,
d=path.shape[1], jnames=self.name)
params = dmp.imitate_path(path+1e-5*np.random.randn(path.shape[0], path.shape[1]))
print(params)
self.dmp_count += 1
with open("./dmp%05d.npy" % self.dmp_count, "w") as f:
pickle.dump(dmp, f, pickle.HIGHEST_PROTOCOL)
y_r, _, _ = dmp.rollout()
plot_rollout(y_r, path)
self.traj = []
print('Stationary')
def callback(self, msg):
if self.js_0 == None:
self.js_0 = msg
self.js_1 = msg
else:
self.js_0 = self.js_1
self.js_1 = msg
if np.sum(
np.abs(
np.array(self.js_0.position) -
np.array(self.js_1.position))) > 0.01:
print('Moving')
j_list = []
for j, joint in enumerate(self.joint_names):
i = msg.name.index(joint)
if i:
j_list.append(msg.position[i])
self.traj.append(j_list)
else:
if len(self.traj) > 0:
# Fit DMP to motion segment
path = np.array(self.traj)
dmp = DMP(path[0, :],
path[-1, :],
Nb=500,
dt=0.01,
d=path.shape[1],
jnames=self.joint_names)
params = dmp.imitate_path(
path +
1e-5 * np.random.randn(path.shape[0], path.shape[1]))
#print(params)
dmp.reset_state()
self.dmp_count += 1
with open("./dmp%05d.npy" % self.dmp_count, "w") as f:
pickle.dump(dmp, f, pickle.HIGHEST_PROTOCOL)
y_r, dy_r, ddy_r = dmp.rollout()
plt.subplot(1, 2, 1)
plt.cla()
plt.plot(y_r)
plt.subplot(1, 2, 2)
plt.cla()
plt.plot(path)
plt.draw()
plt.pause(0.1)
self.jt.header = msg.header
self.jt.joint_names = self.joint_names
jtp = JointTrajectoryPoint()
for i in range(y_r.shape[0]):
jtp.positions = y_r[i, :].tolist()
jtp.velocities = dy_r[i, :].tolist()
jtp.time_from_start = rospy.Duration(1.0)
self.jt.points.append(jtp)
self.traj_pub.publish(self.jt)
self.traj = []
print('Stationary')
self.jt = JointTrajectory()
