-
Notifications
You must be signed in to change notification settings - Fork 1
/
Estimator.py.orig
328 lines (233 loc) · 11.4 KB
/
Estimator.py.orig
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
#(c) 2019 Brian Kilberg
from __future__ import division, print_function
import numpy as np
from py3dmath import Vec3, Rotation # get from https://github.com/muellerlab/py3dmath
import copy
import math
from utils import DroneType, StepDynamics
'''
class Estimator3Dof:
def __init__(self, pos = Vec3(0,0,0), vel = Vec3(0,0,0), att=Rotation.identity(),d = Vec3(0,0,0), drone_type= DroneType.robot_drone):
self._stateHistP = []
self._posNoise = [0.05,0.05]
self._velNoise = [0.001,0.001]
self._thetNoise = 0.01
self._Pm = np.diag([self._posNoise[0], self._posNoise[1], self._thetNoise, self._velNoise[0], self._velNoise[1]])
self._Pp = np.zeros((5,5))
self._drone_type = drone_type
self._state_m= np.array([pos[0] + np.random.rand() * self._posNoise[0], pos[1] + np.random_rand() * self._posNoise[2], att.to_euler_YPR()[0] + np.random.rand() * self._thetNoise, vel[0] + np.random.rand() * self._velNoise[0], vel[1] + np.random.rand() + self._velNoise[1]])
self._state_p = self._state_m
def linearizeDynamics(self, accImu, omegaImu, magImu, dt):
A = [[1, 0, 0, dt, 0],
[0, 1, 0, 0, dt],
[0, 0, 1, 0, 0],
[0, 0, (-math.sin(self._state_m[2]) * accImu[0] - math.cos(self._state_m[2]) *
accImu[1]) * dt, 1, 0],
[0, 0, (math.cos(self._state_m[2]) * accImu[0] - math.sin(self._state_m[2]) *
accImu[1]) * dt, 0, 1]]
return A
def kalmanPredict(self, accImu, omegaImu, magImu, dt, measurement):
if self._drone_type == DroneType.lighthouse_drone or self._drone_type == DroneType.robot_drone:
self.kalmanPredictLighthouse(accImu, omegaImu, dt, measurement)
elif self._drone_type == DroneType.anchor_drone:
self.kalmandPredictAnchor(accImu, omegaImu, dt, measurement)
def kalmanPredictLighthouse(self, accImu, omegaImu, magImu, dt, measurement):
#linearize A matrix
A = self.linearizeDynamics(accImu, gyroImu,dt)
#process noise
L = [[dt, 0, 0, 0, 0],
[0, dt, 0, 0, 0],
[0, 0, dt, 0, 0],
[0, 0, 0, -dt * math.sin(self._state_m[2]), -dt * math.cos(self._state_m[2])],
[0, 0, 0, dt * math.cos(self._state_m[2]), -dt * math.sin(self._state_m[2])]]
Q = np.diag(np.concat(self._posNoise, [self._thetNoise], self._velNoise))
#covariance update
self._Pp = A * self._Pm * A.T + L * Q * L.T
if measurement != None:
# lighthouse measurement is available
x_a = measurement[1][0]
y_a = measurement[1][1]
# calculate noise corrupted measurement
c_noise = np.random.randn() * .001 # compass noise
l_noise = np.random.randn() * math.sqrt(self._Pp[2][2]) # lighthouse noise
sig_l = math.sqrt(self._Pp[2][2])
# l_noise = randn * compass_n
# l_noise = xp_obj.theta - phi # this is the actual error of lighthouse i believe
<<<<<<< HEAD
z = measurement[0]
=======
z = [measurement[0], magImu]
>>>>>>> ae99e20c0a1b27a37a187f627d25d3b140ba85cb
# calculate H
# TODO: Switch to transposing function
r = np.linalg.norm(np.array([self._state_p[0:2]]).T - [[x_a], [y_a]])
angle = np.arctan2(self._state_p[1] - y_a, self._state_p[0] - x_a)
H_mat = np.array([[-np.sin(angle) / r, np.cos(angle) / r, 0, 0, 0],
[0, 0, 1, 0, 0]])
# calculate M. I could potentially add two more entries: var(x_a), var(y_a)
M = np.array([[1, 0], [0, 1]])
# calculate zhat
zhat = np.array([[angle], [self._state_p[2]]])
# calculate R(noise covariance matrix)
compass_w = 0.001 * 0.001
R_mat = [[sig_l * sig_l, 0], [0, compass_w]]
# Kalman gain
# TODO: Switch to transposing function
kalman_gain = np.dot(np.dot(self._Pp, H_mat.T), np.linalg.inv(
np.dot(np.dot(H_mat, self._Pp), H_mat.T) + np.dot(np.dot(M, R_mat), M.T)))
diff = []
if measurement:
diff.append(z[0] - zhat[0])
diff.append(z[1] - zhat[1])
diff[0] = ((diff[0] + PI) % (2 * PI)) - PI
diff[1] = ((diff[1] + PI) % (2 * PI)) - PI
else:
diff.append(z[0] - zhat[0])
diff[0] = ((diff[0] + PI) % (2 * PI)) - PI
# TODO: Switch to transposing function
xp_vec_trans = np.array([self._state_p][:]).T
k_diff_trans = np.dot(kalman_gain, diff)
# TODO: Switch to transposing function
if len(np.shape(k_diff_trans)) == 1:
k_diff_trans = np.array([k_diff_trans]).T
add_xm_vec = np.array(xp_vec_trans + k_diff_trans)
self._state_m = add_xm_vec
# Pm
self._Pm = np.dot(np.eye(5) - np.dot(kalman_gain, H_mat), self._Pp)
# calculates anchor measurements based on the true theta state of the
# robot. X_a is the set of anchor point locations.
def kalmanPredictAnchor(self, accImu, omegaImu, magImu, dt, measurement):
if measurement != None:
h = np.arctan2(self._state_p[1] - self.measurement[1][0], self._state_p[0] - self.measurement[1][2])
lighthouse_xy = self.measurement[1]
r = np.linalg.norm(self._state_p[0:2] - lighthouse_xy)
angle = ((np.arctan2(self._state_p[1] - lighthouse_xy[1], self._state_p[0] - lighthouse_xy[0]) + PI) % (2*PI)) - PI
H = (1/r) * np.array([-np.sin(angle), np.cos(angle)])
# H = [-(x_p(2,i)-y_l(i))/norm(x_p(:,i)-X_l(:,i))^2 , (x_p(1,i)-x_l(i))/norm(x_p(:,i)-X_l(:,i))^2;
# -10*(x_p(1,i)-x_l(i))/(log(10)* norm(x_p(:,i)-X_l(:,i))^2), -10*(x_p(2,i)-y_l(i))/(log(10)* norm(x_p(:,i)-X_l(:,i))^2)];
W = np.array([[(self._state_p[1] - lighthouse_xy[1]) / np.power(np.linalg.norm(self._state_p - lighthouse_xy), 2), -(self._state_p[0] - lighthouse_xy[0]) / np.power(np.linalg.norm(self._state_p[0:2] - lighthouse_xy), 2), 1, 0],
[10 * (self._state_p[0] - lighthouse_xy[0]) / (np.log(10) * np.power(np.linalg.norm(self._state_p[0:2] - lighthouse_xy), 2)), 10*(self._state_p[1]-lighthouse_xy[1]) / (np.log(10) * np.power(np.linalg.norm(self._state_p[0:2] - lighthouse_xy), 2)), 0 ,1]])
P_l = np.diag([.05**2, .05**2, (1.5 * 3.1415 / 180)**2])
R = np.array([np.append(P_l[0,:],[0]),
np.append(P_l[1,:], [0]),
np.append(P_l[2,:], [0]),
[0,0,0,10**2]])
K = self._Pp[0:2, 0:2] @ H.T @ np.linalg.inv(H @ self._Pp[0:2, 0:2] @ H.T + W @ R @ W.T)
K = K[:,None]
# is the kalman gain helpful?
z_h_diff = ((z-h + PI) % (2*PI)) - PI
new_xm_xy = np.array(self._state_p[0:2][:,None] + K * z_h_diff)
self._state_p[0:2] = new_xm_xy
new_Pm = np.zeros((5,5))
new_Pm[0:2, 0:2] = np.array((np.identity(2) - K @ np.array([H])) @ self._Pp[0:2, 0:2])
self._Pm = new_Pm
def kalmanUpdate(self, accImu, omegaImu, dt):
pos, vel, theta = Vehicle.step_dynamics(self._state_m[0:2], self._state_m[3:], self._state_m[2], accImu, omegaImu, dt)
self._state_p = pos + [theta] + vel
self._stateHistP.append(self._state_p)
self._Pp = copy.deepcopy(self._Pm)
'''
class Estimator6Dof:
#maps state to A matrix
stateDict = {"x": 0,
"y": 1,
"z": 2,
"vx": 3,
"vy": 4,
"vz": 5,
"d0": 6,
"d1": 7,
"d2": 8}
def __init__(self,mass, inertiaMatrix,omegaSqrToDragTorque, disturbanceTorqueStdDev,pos = Vec3(0,0,0), vel = Vec3(0,0,0), att=Rotation.identity(),d = Vec3(0,0,0)):
self._mass = mass
self._inertia = inertiaMatrix
self._omegaSqrToDragTorque = omegaSqrToDragTorque
self._disturbanceTorqueStdDev = disturbanceTorqueStdDev
self._state_m = State(pos,vel,d,att)
self._state_p = State(pos,vel,d,att)
self._Pp = None
self._Pm = np.eye(9)*0.01
self._stateHistP = []
self._posNoise = [0.01,0.01,0.01]
self._velNoise = [0.01,0.01,0.01]
self._dNoise = [0.01,0.01,0.01]
self._debug = False
#state vars
def debugLinearization(self):
if self._debug == False:
self._debug = True
def linearizeDynamics(self,accImu, gyroImu, dt):
#create linearized A matrix
A = np.zeros((9,9))
A_pos = np.eye(3)
A_d = np.eye(3)
#position to velocity model
A_posvel = self._state_m.att.to_rotation_matrix()*dt
#print(self._state_m.att.to_rotation_matrix)
#print(A_posvel)
#position to attitude model
d0 = Vec3(1,0,0).to_cross_product_matrix()
d1 = Vec3(0,1,0).to_cross_product_matrix()
d2 = Vec3(0,0,1).to_cross_product_matrix()
#dynamics are R*(I + |_del_|) * vel
#print(self._state_m.vel.to_array())
grad_d0 = self._state_m.att.to_rotation_matrix()*d0*self._state_m.vel.to_array() * dt
grad_d1 = self._state_m.att.to_rotation_matrix()*d1*self._state_m.vel.to_array() * dt
grad_d2 = self._state_m.att.to_rotation_matrix()*d2*self._state_m.vel.to_array() * dt
A_pos_att = np.column_stack((grad_d0,grad_d1,grad_d2))
#velocity to velocity = Rot(omega)_T
A_velvel = gyroImu.to_cross_product_matrix().T
#velocity from attitude error = -g*(-[del x])*R_T * e3
grad_d0 = 9.81*d0*self._state_m.att.to_rotation_matrix().T*Vec3(0,0,1).to_array() * dt
grad_d1 = 9.81*d1*self._state_m.att.to_rotation_matrix().T*Vec3(0,0,1).to_array() * dt
grad_d2 = 9.81*d2*self._state_m.att.to_rotation_matrix().T*Vec3(0,0,1).to_array() * dt
A_velatt = np.column_stack((grad_d0,grad_d1,grad_d2))
#att error from att error. this is from the covariance update paper
gyroCrossRod = gyroImu.to_cross_product_matrix()/2 * dt
mat = np.eye(3)+gyroCrossRod.T + gyroCrossRod.T*gyroCrossRod.T/2
A_attatt = mat
#create A matrix
A = np.block([[A_pos, A_posvel, A_pos_att],
[np.zeros((3,3)), A_velvel, A_velatt],
[np.zeros((3,3)), np.zeros((3,3)), A_attatt]])
return A
def kalmanPredict(self, accImu, gyroImu, dt):
#linearize A matrix
A = self.linearizeDynamics(accImu, gyroImu,dt)
#process noise
L = np.diag([dt, dt, dt, dt, dt, dt, dt, dt, dt])
Q = np.diag(np.concatenate((self._posNoise, self._velNoise, self._dNoise)))
#covariance update
self._Pp = A * self._Pm * A.T + L * Q * L.T
#print(A)
#position prediction
dposBody = self._state_m.vel * dt + Vec3(0,0,accImu[2]*dt*dt / 2.0)
#self._state_p.pos = self._state_m.att * dposBody - Vec3(0,0,9.81*dt*dt / 2.0)
self._state_p.pos = self._state_m.pos + self._state_m.att * dposBody - Vec3(0,0,9.81*dt*dt / 2.0)
#velocity prediction: zacc - gyro x vel - g_body
gyroCross = gyroImu.to_cross_product_matrix()
self._state_p.vel = self._state_m.vel + dt*(Vec3(0,0,accImu[2])-gyroCross*self._state_m.vel - self._state_m.att.inverse() * Vec3(0,0,9.81))
#attitude update
gyroRot = Rotation.from_rotation_vector(gyroImu*dt)
self._state_p.att = self._state_m.att*gyroRot
self._stateHistP.append(self._state_p)
#compare linearized dynamics to actual dynamics
#gyroRot = Rotation.from_rotation_vector(gyroImu*dt)
#self._state_p.devectorize(A*self._state_m.getVector(),self._state_m.att*gyroRot)
def kalmanUpdate(self, dt):
#no measurement update created yet
self._state_p = copy.deepcopy(self._state_m)
self._Pm = self._Pp
class State:
def __init__(self, pos, vel, d, att):
self.pos = pos #position as Vec3
self.vel = vel #velocity as Vec3
self.d = d #attitude error as Vec3 (rodrigues params)
self.att = att #reference attitude
def getVector(self):
return np.array([[self.pos[0], self.pos[1], self.pos[2], self.vel[0], self.vel[1], self.vel[2], self.d[0], self.d[1], self.d[2]]]).T
def devectorize(self,state_vector,att):
self.pos = Vec3(state_vector[0,0], state_vector[1,0], state_vector[2,0])
self.vel = Vec3(state_vector[3,0], state_vector[4,0],state_vector[5,0])
self.d = Vec3(state_vector[6,0], state_vector[7,0], state_vector[8,0])
self.att = att