Switch branches/tags
Nothing to show
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
87 lines (70 sloc) 3.06 KB
Copyright (C) 2016 Travis DeWolf
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <>.
import numpy as np
import matplotlib.pyplot as plt
import pydmps.dmp_discrete
beta = 20.0 / np.pi
gamma = 100
R_halfpi = np.array([[np.cos(np.pi / 2.0), -np.sin(np.pi / 2.0)],
[np.sin(np.pi / 2.0), np.cos(np.pi / 2.0)]])
num_obstacles = 5
obstacles = np.random.random((num_obstacles, 2))*2 - 1
def avoid_obstacles(y, dy, goal):
p = np.zeros(2)
for obstacle in obstacles:
# based on (Hoffmann, 2009)
# if we're moving
if np.linalg.norm(dy) > 1e-5:
# get the angle we're heading in
phi_dy = -np.arctan2(dy[1], dy[0])
R_dy = np.array([[np.cos(phi_dy), -np.sin(phi_dy)],
[np.sin(phi_dy), np.cos(phi_dy)]])
# calculate vector to object relative to body
obj_vec = obstacle - y
# rotate it by the direction we're going
obj_vec =, obj_vec)
# calculate the angle of obj relative to the direction we're going
phi = np.arctan2(obj_vec[1], obj_vec[0])
dphi = gamma * phi * np.exp(-beta * abs(phi))
R =, np.outer(obstacle - y, dy))
pval = -np.nan_to_num(, dy) * dphi)
# check to see if the distance to the obstacle is further than
# the distance to the target, if it is, ignore the obstacle
if np.linalg.norm(obj_vec) > np.linalg.norm(goal - y):
pval = 0
p += pval
return p
# test normal run
dmp = pydmps.dmp_discrete.DMPs_discrete(n_dmps=2, n_bfs=10,
y_track = np.zeros((dmp.timesteps, dmp.n_dmps))
dy_track = np.zeros((dmp.timesteps, dmp.n_dmps))
ddy_track = np.zeros((dmp.timesteps, dmp.n_dmps))
goals = [[np.cos(theta), np.sin(theta)] for theta in np.linspace(0, 2*np.pi, 20)[:-1]]
for goal in goals:
dmp.goal = goal
for t in range(dmp.timesteps):
y_track[t], dy_track[t], ddy_track[t] = \
dmp.step(external_force=avoid_obstacles(dmp.y, dmp.dy, goal))
plt.figure(1, figsize=(6,6))
plot_goal, = plt.plot(dmp.goal[0], dmp.goal[1], 'gx', mew=3)
for obstacle in obstacles:
plot_obs, = plt.plot(obstacle[0], obstacle[1], 'rx', mew=3)
plot_path, = plt.plot(y_track[:,0], y_track[:, 1], 'b', lw=2)
plt.title('DMP system - obstacle avoidance')