StateSampling.py

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#!/usr/bin/env python

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# Author: Mark Moll

try:
    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 basename, abspath, dirname, join
    import sys
    sys.path.insert(0, join(dirname(dirname(abspath(__file__))),'py-bindings'))
    from ompl import base as ob
    from ompl import geometric as og
from time import sleep
from math import fabs


# This is a problem-specific sampler that automatically generates valid
# states; it doesn't need to call SpaceInformation::isValid. This is an
# example of constrained sampling. If you can explicitly describe the set valid
# states and can draw samples from it, then this is typically much more
# efficient than generating random samples from the entire state space and
# checking for validity.
class MyValidStateSampler(ob.ValidStateSampler):
    def __init__(si):
        super(MyValidStateSampler, self).__init__(si)
        self.name_ = "my sampler"
        self.rng_ = RNG()

    # Generate a sample in the valid part of the R^3 state space.
    # Valid states satisfy the following constraints: 
    # -1<= x,y,z <=1
    # if .25 <= z <= .5, then |x|>.8 and |y|>.8
    def sample(state):
        z = self.rng_.uniformReal(-1,1)

        if z>.25 and z<.5:
            x = self.rng_.uniformReal(0,1.8)
            y = self.rng_.uniformReal(0,.2)
            i = self.rng_.uniformInt(0,3)
            if i==0:
                state[0]=x-1
                state[1]=y-1
            elif i==1:
                state[0]=x-.8
                state[1]=y+.8
            elif i==2:
                state[0]=y-1
                state[1]=x-1
            elif i==3:
                state[0]=y+.8
                state[1]=x-.8
        else:
            state[0] = self.rng_.uniformReal(-1,1)
            state[1] = self.rng_.uniformReal(-1,1)
        state[2] = z
        return True

# This function is needed, even when we can write a sampler like the one
# above, because we need to check path segments for validity
def isStateValid(spaceInformation, state):
    # Let's pretend that the validity check is computationally relatively
    # expensive to emphasize the benefit of explicitly generating valid
    # samples
    sleep(.001)
    # Valid states satisfy the following constraints: 
    # -1<= x,y,z <=1
    # if .25 <= z <= .5, then |x|>.8 and |y|>.8
    return not (fabs(state[0]<.8) and fabs(state[1]<.8) and
        state[2]>.25 and state[2]<.5)

# return an obstacle-based sampler
def allocOBValidStateSampler(si):
    # we can perform any additional setup / configuration of a sampler here, 
    # but there is nothing to tweak in case of the ObstacleBasedValidStateSampler.
    return ob.ValidStateSamplerPtr(ob.ObstacleBasedValidStateSampler(si))

# return an instance of my sampler
def allocMyValidStateSampler(si):
    return ob.ValidStateSamplerPtr(MyValidStateSampler(si))

def plan(samplerIndex):
    # construct the state space we are planning in
    space = ob.RealVectorStateSpace(3)

    # set the bounds
    bounds = ob.RealVectorBounds(3)
    bounds.setLow(-1)
    bounds.setHigh(1)
    space.setBounds(bounds)

    # define a simple setup class
    ss = og.SimpleSetup(space)

    # set state validity checking for this space
    ss.setStateValidityChecker(isStateValid)

    # create a start state
    start = ob.State(space)
    start[0] = 0
    start[1] = 0
    start[2] = 0

    # create a goal state
    goal = ob.State(space)
    goal[0] = 0
    goal[1] = 0
    goal[2] = 1

    # set the start and goal states;
    ss.setStartAndGoalStates(start, goal)

    # set sampler (optional; the default is uniform sampling)
    if samplerIndex==1:
        # use obstacle-based sampling
        ss.getSpaceInformation().setValidStateSamplerAllocator(allocOBValidStateSampler)
    elif samplerIndex==2:
        # use my sampler
        ss.getSpaceInformation().setValidStateSamplerAllocator(allocMyValidStateSampler)

    # set the planner (optional)
    # create a planner for the defined space
    planner = og.RRTConnect(ss.getSpaceInformation())
    ss.setPlanner(planner)

    # attempt to solve the problem within ten seconds of planning time
    solved = ss.solve(10.0)
    if (solved):
        print "Found solution:"
        # print the path to screen
        ss.simplifySolution()
        print ss.getSolutionPath()
    else:
        print "No solution found"


if __name__ == '__main__':
    print "Using default uniform sampler:"
    plan(0)
    print "\nUsing obstacle-based sampler:"
    plan(1)
    print "\nUsing my sampler:"
    plan(2)

---

ompl
Author(s): Ioan Sucan/isucan@rice.edu, Mark Moll/mmoll@rice.edu, Lydia Kavraki/kavraki@rice.edu
autogenerated on Fri Jan 11 09:33:20 2013