/usr/share/ompl/demos/RandomWalkPlanner.py is in ompl-demos 1.0.0+ds2-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | #!/usr/bin/env python
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# Author: Mark Moll
try:
from ompl import util as ou
from ompl import base as ob
from ompl import geometric as og
except:
# if the ompl module is not in the PYTHONPATH assume it is installed in a
# subdirectory of the parent directory called "py-bindings."
from os.path import abspath, dirname, join
import sys
sys.path.insert(0, join(dirname(dirname(abspath(__file__))),'py-bindings'))
from ompl import util as ou
from ompl import base as ob
from ompl import geometric as og
from random import choice
## @cond IGNORE
class RandomWalkPlanner(ob.Planner):
def __init__(self, si):
super(RandomWalkPlanner, self).__init__(si, "RandomWalkPlanner")
self.states_ = []
self.sampler_ = si.allocStateSampler()
def solve(self, ptc):
pdef = self.getProblemDefinition()
goal = pdef.getGoal()
si = self.getSpaceInformation()
pi = self.getPlannerInputStates()
st = pi.nextStart()
while st:
self.states_.append(st)
st = pi.nextStart()
solution = None
approxsol = 0
approxdif = 1e6
while not ptc():
rstate = si.allocState()
# pick a random state in the state space
self.sampler_.sampleUniform(rstate)
# check motion
if si.checkMotion(self.states_[-1], rstate):
self.states_.append(rstate)
sat = goal.isSatisfied(rstate)
dist = goal.distanceGoal(rstate)
if sat:
approxdif = dist
solution = len(self.states_)
break
if dist < approxdif:
approxdif = dist
approxsol = len(self.states_)
solved = False
approximate = False
if not solution:
solution = approxsol
approximate = True
if solution:
path = og.PathGeometric(si)
for s in self.states_[:solution]:
path.append(s)
pdef.addSolutionPath(path)
solved = True
return ob.PlannerStatus(solved, approximate)
def clear(self):
super(RandomWalkPlanner, self).clear()
self.states_ = []
## @endcond
def isStateValid(state):
x = state[0]
y = state[1]
z = state[2]
return x*x + y*y + z*z > .8
def plan():
# create an R^3 state space
space = ob.RealVectorStateSpace(3)
# set lower and upper bounds
bounds = ob.RealVectorBounds(3)
bounds.setLow(-1)
bounds.setHigh(1)
space.setBounds(bounds)
# create a simple setup object
ss = og.SimpleSetup(space)
ss.setStateValidityChecker(ob.StateValidityCheckerFn(isStateValid))
start = ob.State(space)
start()[0] = -1.
start()[1] = -1.
start()[2] = -1.
goal = ob.State(space)
goal()[0] = 1.
goal()[1] = 1.
goal()[2] = 1.
ss.setStartAndGoalStates(start, goal, .05)
# set the planner
planner = RandomWalkPlanner(ss.getSpaceInformation())
ss.setPlanner(planner)
result = ss.solve(10.0)
if result:
if result.getStatus() == ob.PlannerStatus.APPROXIMATE_SOLUTION:
print("Solution is approximate")
# try to shorten the path
ss.simplifySolution()
# print the simplified path
print(ss.getSolutionPath())
if __name__ == "__main__":
plan()
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