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Atsushi Sakai
2017-07-15 13:04:49 -07:00
parent b926a80286
commit 99332fd019
2 changed files with 5 additions and 79 deletions
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79
PathPlanning/ModelPredictiveTrajectoryGenerator/neuralnet_init_pram_regressor.py
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@@ -1,79 +0,0 @@
"""
author: Atsushi Sakai
"""
from sklearn.neural_network import MLPRegressor
from matplotlib import pyplot as plt
import numpy as np
import math
import motion_model
def calc_motion_param(maxs, ds, maxk, dk):
s = np.arange(1.0, maxs, ds)
km = np.arange(-maxk, maxk, dk)
kf = np.arange(-maxk, maxk, dk)
motion_param = []
for p1 in s:
for p2 in km:
for p3 in kf:
motion_param.append([p1, p2, p3])
print("motion_param size is:", len(motion_param))
return motion_param
def calc_state_param(motion_param):
print("calc_motion_param")
state_param = []
for p in motion_param:
x, y, yaw = motion_model.generate_last_state(p[0], p[1], p[2], 0.0)
state_param.append([x, y, yaw])
print("calc_state_param done", len(state_param))
return state_param
def learn_motion_model():
# calc motion param
# s, k0, km, kf
maxk = math.radians(45.0)
dk = math.radians(1.0)
maxs = 50.0
ds = 5.0
motion_param = calc_motion_param(maxs, ds, maxk, dk)
state_param = calc_state_param(motion_param)
reg = MLPRegressor(hidden_layer_sizes=(100, 100, 100))
reg.fit(state_param, motion_param)
print(reg.score(state_param, motion_param))
x = 10.0
y = 5.0
yaw = math.radians(00.0)
predict = reg.predict([[x, y, yaw]])
print(predict)
x, y, yaw = motion_model.generate_trajectory(
predict[0, 0], predict[0, 1], predict[0, 2], 0.0)
plt.plot(x, y, "-r", label="trajectory")
plt.axis("equal")
plt.legend()
plt.grid(True)
plt.show()
print("Done")
def main():
learn_motion_model()
if __name__ == '__main__':
main()

5
README.md
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@@ -20,9 +20,14 @@ Path planning algorithm.
This script is a path planning code with model predictive trajectory generator.
Path optimization sample:
![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif)
Loopup table generation sample:
![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png)
see:
- [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)