def residual_z_(z, zp):
"""
z : observation, [r, alpha]
zp : predicted observation
"""
r_z = z - zp
r_z[1] = util.wrap_around(r_z[1])
return r_z
def get_control(self, odom):
R = np.array([[np.cos(self.th), -np.sin(self.th)],
[np.sin(self.th), np.cos(self.th)]])
p_b = -np.matmul(R.T, self.init_org) + np.matmul(R.T, odom[:2])
p_b_tp1 = [
p_b[0] + self.x_interval,
self.b * np.sin(self.a * (p_b[0] + self.x_interval))
]
p_g_tp1 = self.init_org + np.matmul(R, p_b_tp1)
th_b_tp1 = np.arctan(self.b * self.a * np.cos(p_b_tp1[0] * self.a))
th_g_tp1 = util.wrap_around(th_b_tp1 + self.th)
ang_vel = util.wrap_around(th_g_tp1 - odom[2]) / self.sampling_period
lin_vel = np.sqrt(np.sum(
(p_g_tp1 - odom[:2])**2)) / self.sampling_period
return np.clip(np.array([lin_vel, ang_vel]), self.limit[0],
self.limit[1])
def residual_x_(x, xp):
"""
x : state, [x, y, theta]
xp : predicted state
"""
if dim == 3 or dim == 5:
r_x = x - xp
r_x[2] = util.wrap_around(r_x[2])
return r_x
else:
return None
def get_control(self, odom):
th = self.d_th / 180.0 * np.pi
r = np.sqrt((odom[0] - self.maporigin[0])**2 +
(odom[1] - self.maporigin[1])**2)
alpha = np.arctan2(odom[1] - self.maporigin[0],
odom[0] - self.maporigin[1])
x = r * np.cos(alpha +
th) + self.maporigin[0] + np.random.random() - 0.5
y = r * np.sin(alpha +
th) + self.maporigin[1] + np.random.random() - 0.5
v = np.sqrt((x - odom[0])**2 + (y - odom[1])**2) / self.sampling_period
w = util.wrap_around(np.arctan2(y - odom[1], x - odom[0]) -
odom[2]) / self.sampling_period
return np.clip(np.array([v, w]), self.limit[0], self.limit[1])
def SE2Dynamics(x, dt, u):
assert (len(x) == 3)
tw = dt * u[1]
# Update the agent state
if abs(tw) < 0.001:
diff = np.array([
dt * u[0] * np.cos(x[2] + tw / 2),
dt * u[0] * np.sin(x[2] + tw / 2), tw
])
else:
diff = np.array([
u[0] / u[1] * (np.sin(x[2] + tw) - np.sin(x[2])),
u[0] / u[1] * (np.cos(x[2]) - np.cos(x[2] + tw)), tw
])
new_x = x + diff
new_x[2] = util.wrap_around(new_x[2])
return new_x
def update(self, z_t, x_t):
"""
Parameters
--------
z_t : observation - radial and angular distances from the agent.
x_t : agent state (x, y, orientation) in the global frame.
"""
# Kalman Filter Update
r_pred, alpha_pred = util.relative_distance_polar(
self.state[:2], x_t[:2], x_t[2])
diff_pred = self.state[:2] - x_t[:2]
if self.dim == 2:
Hmat = np.array([[diff_pred[0], diff_pred[1]],
[-diff_pred[1] / r_pred, diff_pred[0] / r_pred]
]) / r_pred
elif self.dim == 4:
Hmat = np.array([
[diff_pred[0], diff_pred[1], 0.0, 0.0],
[-diff_pred[1] / r_pred, diff_pred[0] / r_pred, 0.0, 0.0]
]) / r_pred
else:
raise ValueError('target dimension for KF must be either 2 or 4')
innov = z_t - np.array([r_pred, alpha_pred])
innov[1] = util.wrap_around(innov[1])
R = np.matmul(np.matmul(Hmat, self.cov), Hmat.T) \
+ self.obs_noise_func((r_pred, alpha_pred))
K = np.matmul(np.matmul(self.cov, Hmat.T), LA.inv(R))
C = np.eye(self.dim) - np.matmul(K, Hmat)
cov_new = np.matmul(C, self.cov)
state_new = self.state + np.matmul(K, innov)
if True: #LA.det(cov_new) < 1e6:
self.cov = cov_new
self.state = np.clip(state_new, self.limit[0], self.limit[1])
def collision(self, odom):
self.init_org = odom[:2]
self.th = util.wrap_around(odom[2] + np.random.random() * np.pi / 2 -
np.pi / 4)