def lanePoseCB(self, lane_pose_msg):
self.lane_pose = lane_pose_msg
self.in_lane = lane_pose_msg.in_lane
# only execute if in_lane and if the old message exists and is not too old
if (
self.in_lane
and hasattr(self.old_lane_pose_msg, "header")
and (lane_pose_msg.header.stamp - self.old_lane_pose_msg.header.stamp).to_sec() <= self.lane_pose_max_age
):
theta_dot_sample_msg = ThetaDotSample()
(d_L, d_R) = self.findMatchingDuties(lane_pose_msg.header.stamp)
theta_dot_sample_msg.d_L = d_L
theta_dot_sample_msg.d_R = d_R
# theta_dot_sample_msg.dt = (lane_pose_msg.header.stamp.secs + lane_pose_msg.header.stamp.nsecs/1e9) - (self.old_lane_pose_msg.header.stamp.secs + self.old_lane_pose_msg.header.stamp.nsecs/1e9)
theta_dot_sample_msg.dt = (lane_pose_msg.header.stamp - self.old_lane_pose_msg.header.stamp).to_sec()
# rospy.loginfo("theta_dot_sample_msg.dt = %s"%theta_dot_sample_msg.dt)
# new - old since measure robot angle
theta_dot_sample_msg.theta_angle_pose_delta = lane_pose_msg.phi - self.old_lane_pose_msg.phi
# is actually (delta d)*cos(phi), but is probably pretty noisy since we get error from both d and phi
# theta_dot_sample_msg.x_axis_pose_delta = (lane_pose_msg.d - self.old_lane_pose_msg.d)*cos(lane_pose_msg.phi)
# theta_dot_sample_msg.y_axis_pose_delta = 0
self.pub_theta_dot_sample.publish(theta_dot_sample_msg)
self.v_sample_msg.theta_angle_pose_delta = theta_dot_sample_msg.theta_angle_pose_delta
# may need to swap signs on this one
self.v_sample_msg.y_axis_pose_delta = lane_pose_msg.d - self.old_lane_pose_msg.d
self.old_lane_pose_msg = lane_pose_msg
def lanePoseCB(self, lane_pose_msg):
self.lane_pose = lane_pose_msg
self.in_lane = lane_pose_msg.in_lane
# only execute if in_lane and if the old message exists and is not too old
if self.in_lane and hasattr(
self.old_lane_pose_msg,
'header') and (lane_pose_msg.header.stamp -
self.old_lane_pose_msg.header.stamp
).to_sec() <= self.lane_pose_max_age:
theta_dot_sample_msg = ThetaDotSample()
(d_L, d_R) = self.findMatchingDuties(lane_pose_msg.header.stamp)
theta_dot_sample_msg.d_L = d_L
theta_dot_sample_msg.d_R = d_R
# theta_dot_sample_msg.dt = (lane_pose_msg.header.stamp.secs + lane_pose_msg.header.stamp.nsecs/1e9) - (self.old_lane_pose_msg.header.stamp.secs + self.old_lane_pose_msg.header.stamp.nsecs/1e9)
theta_dot_sample_msg.dt = (
lane_pose_msg.header.stamp -
self.old_lane_pose_msg.header.stamp).to_sec()
# rospy.loginfo("theta_dot_sample_msg.dt = %s"%theta_dot_sample_msg.dt)
#new - old since measure robot angle
theta_dot_sample_msg.theta_angle_pose_delta = lane_pose_msg.phi - self.old_lane_pose_msg.phi
#is actually (delta d)*cos(phi), but is probably pretty noisy since we get error from both d and phi
#theta_dot_sample_msg.x_axis_pose_delta = (lane_pose_msg.d - self.old_lane_pose_msg.d)*cos(lane_pose_msg.phi)
#theta_dot_sample_msg.y_axis_pose_delta = 0
self.pub_theta_dot_sample.publish(theta_dot_sample_msg)
self.v_sample_msg.theta_angle_pose_delta = theta_dot_sample_msg.theta_angle_pose_delta
#may need to swap signs on this one
self.v_sample_msg.y_axis_pose_delta = lane_pose_msg.d - self.old_lane_pose_msg.d
self.old_lane_pose_msg = lane_pose_msg
def test_learning_from_generated_samples(self):
self.setup_publishers_and_subscribers()
# Generate random samples and publish
msg_v_sample = Vsample()
msg_theta_dot_sample = ThetaDotSample()
theta_dot_weights = np.array([-1, 1])
v_weights = np.array([1,1])
rate = rospy.Rate(30)
timeout = time.time()+10.0
while (not rospy.is_shutdown()) and (not (self.msg_theta_dot_received and self.msg_v_received)) and time.time()<timeout:
sample = self.getRandomSamplePair(theta_dot_weights, v_weights)
msg_v_sample.d_L = sample[0]
msg_v_sample.d_R = sample[1]
msg_v_sample.dt = sample[2]
msg_v_sample.theta_angle_pose_delta = sample[3]
msg_v_sample.x_axis_pose_delta = sample[4]
msg_v_sample.y_axis_pose_delta = sample[5]
msg_theta_dot_sample.d_L = sample[0]
msg_theta_dot_sample.d_R = sample[1]
msg_theta_dot_sample.dt = sample[2]
msg_theta_dot_sample.theta_angle_pose_delta = sample[3]
self.pub_v_sample.publish(msg_v_sample)
self.pub_theta_dot_sample.publish(msg_theta_dot_sample)
rate.sleep()
# Notify if the timeout struck
self.assertLess(time.time(),timeout,msg="WARNING: There was not enough time to receive the weights. Either increase the timeout or decrease the number of points needed by the learner.")
# Check that the new weights are close to the generating ones
self.assertAlmostEqual(self.msg_v_weights.weights[0],1, 0)
self.assertAlmostEqual(self.msg_v_weights.weights[1],1, 0)
self.assertAlmostEqual(self.msg_theta_dot_weights.weights[0],-1, 0)
self.assertAlmostEqual(self.msg_theta_dot_weights.weights[1],1, 0)
def poseCallback(self, msg_pose):
self.pose_list.append(msg_pose)
n = 0
# make sure we have at least two wheels commands and two poses
while len(self.wheels_cmd_list) > 1 and len(self.pose_list) > 1+n:
# discard any pose messages received before the first wheel command plus timeOffset
while len(self.pose_list) > 1+n and (self.pose_list[0].header.stamp.to_sec() + self.timeOffset < self.wheels_cmd_list[0].header.stamp.to_sec()):
del self.pose_list[0]
# need at least two poses to calculate delta
if len(self.pose_list) > 1+n:
# make sure that the second pose is not beyond the duration of the current wheel command
if self.pose_list[1+n].header.stamp.to_sec() < (self.wheels_cmd_list[1].header.stamp.to_sec() + self.timeOffset):
# Calculate the change in pose
#print 'pose', self.pose_list[1]
dx = self.pose_list[0].pose.position.x - self.pose_list[1+n].pose.position.x
dy = self.pose_list[0].pose.position.y - self.pose_list[1+n].pose.position.y
q = [self.pose_list[1+n].pose.orientation.x, self.pose_list[1+n].pose.orientation.y, self.pose_list[1+n].pose.orientation.z, self.pose_list[1+n].pose.orientation.w]
q = q/linalg.norm(q)
cur_phi = arctan2(2*(q[0]*q[1]+q[2]*q[3]), 1.0-2.0*(q[1]*q[1]+q[2]*q[2]))
cur_theta = arcsin(2*(q[0]*q[2]-q[3]*q[1]))
cur_psi = arctan2(2*(q[0]*q[3]+q[1]*q[2]), 1-2.0*(q[2]*q[2]+q[3]*q[3]))
q = [self.pose_list[0].pose.orientation.x, self.pose_list[0].pose.orientation.y, self.pose_list[0].pose.orientation.z, self.pose_list[0].pose.orientation.w]
q = q/linalg.norm(q)
delta_phi = cur_phi - arctan2(2*(q[0]*q[1]+q[2]*q[3]), 1.0-2.0*(q[1]*q[1]+q[2]*q[2]))
while delta_phi > pi:
delta_phi = delta_phi - 2.0*pi
while delta_phi <= -pi:
delta_phi = delta_phi + 2.0*pi
# delta_theta = cur_theta - arcsin(2*(q[0]*q[2]-q[3]*q[1]))
# delta_psi = cur_psi - arctan2(2*(q[0]*q[3]+q[1]*q[2]), 1-2.0*(q[2]*q[2]+q[3]*q[3]))
# print 'delta_phi', delta_phi, 'delta_theta', delta_theta, 'delta_psi', delta_psi
#dw = euler_from_matrix(trans_diff)[2] #Only the z axis
dw = -delta_phi
dt = self.pose_list[1+n].header.stamp.to_sec() - self.pose_list[0].header.stamp.to_sec()
#print 'dt', dt
# Stuff the measurements into messages and publish
msg_theta_dot = ThetaDotSample()
msg_theta_dot.d_L = self.wheels_cmd_list[0].vel_left
msg_theta_dot.d_R = self.wheels_cmd_list[0].vel_right
msg_theta_dot.dt = dt
msg_theta_dot.theta_angle_pose_delta = dw
self.pub_theta_dot.publish(msg_theta_dot)
msg_v = Vsample()
msg_v.d_L = self.wheels_cmd_list[0].vel_left
msg_v.d_R = self.wheels_cmd_list[0].vel_right
msg_v.dt = dt
msg_v.x_axis_pose_delta = -dy # The vicon frame is setup with -y facing forward on the duckiecar
msg_v.y_axis_pose_delta = dx
msg_v.theta_angle_pose_delta = dw
self.pub_v.publish(msg_v)
else:
del self.wheels_cmd_list[0]
del self.pose_list[0]
def test_learning_from_generated_samples(self):
self.setup_publishers_and_subscribers()
# Generate random samples and publish
msg_v_sample = Vsample()
msg_theta_dot_sample = ThetaDotSample()
theta_dot_weights = np.array([-1, 1])
v_weights = np.array([1, 1])
rate = rospy.Rate(30)
timeout = time.time() + 10.0
while (
(not rospy.is_shutdown())
and (not (self.msg_theta_dot_received and self.msg_v_received))
and time.time() < timeout
):
sample = self.getRandomSamplePair(theta_dot_weights, v_weights)
msg_v_sample.d_L = sample[0]
msg_v_sample.d_R = sample[1]
msg_v_sample.dt = sample[2]
msg_v_sample.theta_angle_pose_delta = sample[3]
msg_v_sample.x_axis_pose_delta = sample[4]
msg_v_sample.y_axis_pose_delta = sample[5]
msg_theta_dot_sample.d_L = sample[0]
msg_theta_dot_sample.d_R = sample[1]
msg_theta_dot_sample.dt = sample[2]
msg_theta_dot_sample.theta_angle_pose_delta = sample[3]
self.pub_v_sample.publish(msg_v_sample)
self.pub_theta_dot_sample.publish(msg_theta_dot_sample)
rate.sleep()
# Notify if the timeout struck
self.assertLess(
time.time(),
timeout,
msg="WARNING: There was not enough time to receive the weights. Either increase the timeout or decrease the number of points needed by the learner.",
)
# Check that the new weights are close to the generating ones
self.assertAlmostEqual(self.msg_v_weights.weights[0], 1, 0)
self.assertAlmostEqual(self.msg_v_weights.weights[1], 1, 0)
self.assertAlmostEqual(self.msg_theta_dot_weights.weights[0], -1, 0)
self.assertAlmostEqual(self.msg_theta_dot_weights.weights[1], 1, 0)
def poseCallback(self, msg_pose):
self.pose_list.append(msg_pose)
n = 0
# make sure we have at least two wheels commands and two poses
while len(self.wheels_cmd_list) > 1 and len(self.pose_list) > 1 + n:
# discard any pose messages received before the first wheel command plus timeOffset
while len(self.pose_list) > 1 + n and (
self.pose_list[0].header.stamp.to_sec() + self.timeOffset <
self.wheels_cmd_list[0].header.stamp.to_sec()):
del self.pose_list[0]
# need at least two poses to calculate delta
if len(self.pose_list) > 1 + n:
# make sure that the second pose is not beyond the duration of the current wheel command
if self.pose_list[1 + n].header.stamp.to_sec() < (
self.wheels_cmd_list[1].header.stamp.to_sec() +
self.timeOffset):
# Calculate the change in pose
#print 'pose', self.pose_list[1]
dx = self.pose_list[0].pose.position.x - self.pose_list[
1 + n].pose.position.x
dy = self.pose_list[0].pose.position.y - self.pose_list[
1 + n].pose.position.y
q = [
self.pose_list[1 + n].pose.orientation.x,
self.pose_list[1 + n].pose.orientation.y,
self.pose_list[1 + n].pose.orientation.z,
self.pose_list[1 + n].pose.orientation.w
]
q = q / linalg.norm(q)
cur_phi = arctan2(2 * (q[0] * q[1] + q[2] * q[3]),
1.0 - 2.0 * (q[1] * q[1] + q[2] * q[2]))
cur_theta = arcsin(2 * (q[0] * q[2] - q[3] * q[1]))
cur_psi = arctan2(2 * (q[0] * q[3] + q[1] * q[2]),
1 - 2.0 * (q[2] * q[2] + q[3] * q[3]))
q = [
self.pose_list[0].pose.orientation.x,
self.pose_list[0].pose.orientation.y,
self.pose_list[0].pose.orientation.z,
self.pose_list[0].pose.orientation.w
]
q = q / linalg.norm(q)
delta_phi = cur_phi - arctan2(
2 * (q[0] * q[1] + q[2] * q[3]), 1.0 - 2.0 *
(q[1] * q[1] + q[2] * q[2]))
while delta_phi > pi:
delta_phi = delta_phi - 2.0 * pi
while delta_phi <= -pi:
delta_phi = delta_phi + 2.0 * pi
# delta_theta = cur_theta - arcsin(2*(q[0]*q[2]-q[3]*q[1]))
# delta_psi = cur_psi - arctan2(2*(q[0]*q[3]+q[1]*q[2]), 1-2.0*(q[2]*q[2]+q[3]*q[3]))
# print 'delta_phi', delta_phi, 'delta_theta', delta_theta, 'delta_psi', delta_psi
#dw = euler_from_matrix(trans_diff)[2] #Only the z axis
dw = -delta_phi
dt = self.pose_list[1 + n].header.stamp.to_sec(
) - self.pose_list[0].header.stamp.to_sec()
#print 'dt', dt
# Stuff the measurements into messages and publish
msg_theta_dot = ThetaDotSample()
msg_theta_dot.d_L = self.wheels_cmd_list[0].vel_left
msg_theta_dot.d_R = self.wheels_cmd_list[0].vel_right
msg_theta_dot.dt = dt
msg_theta_dot.theta_angle_pose_delta = dw
self.pub_theta_dot.publish(msg_theta_dot)
msg_v = Vsample()
msg_v.d_L = self.wheels_cmd_list[0].vel_left
msg_v.d_R = self.wheels_cmd_list[0].vel_right
msg_v.dt = dt
msg_v.x_axis_pose_delta = -dy # The vicon frame is setup with -y facing forward on the duckiecar
msg_v.y_axis_pose_delta = dx
msg_v.theta_angle_pose_delta = dw
self.pub_v.publish(msg_v)
else:
del self.wheels_cmd_list[0]
del self.pose_list[0]