|
6 | 6 |
|
7 | 7 | """ |
8 | 8 |
|
9 | | -import numpy as np |
10 | | -import matplotlib.pyplot as plt |
11 | 9 | import math |
| 10 | + |
| 11 | +import matplotlib.pyplot as plt |
| 12 | +import numpy as np |
| 13 | + |
12 | 14 | import motion_model |
13 | 15 |
|
14 | 16 | # optimization parameter |
@@ -37,7 +39,7 @@ def calc_diff(target, x, y, yaw): |
37 | 39 | return d |
38 | 40 |
|
39 | 41 |
|
40 | | -def calc_J(target, p, h, k0): |
| 42 | +def calc_j(target, p, h, k0): |
41 | 43 | xp, yp, yawp = motion_model.generate_last_state( |
42 | 44 | p[0, 0] + h[0], p[1, 0], p[2, 0], k0) |
43 | 45 | dp = calc_diff(target, [xp], [yp], [yawp]) |
@@ -68,7 +70,6 @@ def calc_J(target, p, h, k0): |
68 | 70 |
|
69 | 71 |
|
70 | 72 | def selection_learning_param(dp, p, k0, target): |
71 | | - |
72 | 73 | mincost = float("inf") |
73 | 74 | mina = 1.0 |
74 | 75 | maxa = 2.0 |
@@ -110,7 +111,7 @@ def optimize_trajectory(target, k0, p): |
110 | 111 | print("path is ok cost is:" + str(cost)) |
111 | 112 | break |
112 | 113 |
|
113 | | - J = calc_J(target, p, h, k0) |
| 114 | + J = calc_j(target, p, h, k0) |
114 | 115 | try: |
115 | 116 | dp = - np.linalg.inv(J) @ dc |
116 | 117 | except np.linalg.linalg.LinAlgError: |
|
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