StateSampling.cpp

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/* Author: Mark Moll */
 
#include <ompl/base/SpaceInformation.h>
#include <ompl/base/spaces/SE3StateSpace.h>
#include <ompl/base/samplers/ObstacleBasedValidStateSampler.h>
#include <ompl/geometric/planners/prm/PRM.h>
#include <ompl/geometric/SimpleSetup.h>
 
#include <ompl/config.h>
#include <iostream>
#include <thread>
 
namespace ob = ompl::base;
namespace og = ompl::geometric;

 
// 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 : public ob::ValidStateSampler
{
public:
    MyValidStateSampler(const ob::SpaceInformation *si) : ValidStateSampler(si)
    {
        name_ = "my sampler";
    }
    // 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
    bool sample(ob::State *state) override
    {
        double *val = static_cast<ob::RealVectorStateSpace::StateType *>(state)->values;
        double z = rng_.uniformReal(-1, 1);
 
        if (z > .25 && z < .5)
        {
            double x = rng_.uniformReal(0, 1.8), y = rng_.uniformReal(0, .2);
            switch (rng_.uniformInt(0, 3))
            {
                case 0:
                    val[0] = x - 1;
                    val[1] = y - 1;
                    break;
                case 1:
                    val[0] = x - .8;
                    val[1] = y + .8;
                    break;
                case 2:
                    val[0] = y - 1;
                    val[1] = x - 1;
                    break;
                case 3:
                    val[0] = y + .8;
                    val[1] = x - .8;
                    break;
            }
        }
        else
        {
            val[0] = rng_.uniformReal(-1, 1);
            val[1] = rng_.uniformReal(-1, 1);
        }
        val[2] = z;
        assert(si_->isValid(state));
        return true;
    }
    // We don't need this in the example below.
    bool sampleNear(ob::State * /*state*/, const ob::State * /*near*/, const double /*distance*/) override
    {
        throw ompl::Exception("MyValidStateSampler::sampleNear", "not implemented");
        return false;
    }
 
protected:
    ompl::RNG rng_;
};

 
// this function is needed, even when we can write a sampler like the one
// above, because we need to check path segments for validity
bool isStateValid(const ob::State *state)
{
    const ob::RealVectorStateSpace::StateType &pos = *state->as<ob::RealVectorStateSpace::StateType>();
    // Let's pretend that the validity check is computationally relatively
    // expensive to emphasize the benefit of explicitly generating valid
    // samples
    std::this_thread::sleep_for(ompl::time::seconds(.0005));
    // Valid states satisfy the following constraints:
    // -1<= x,y,z <=1
    // if .25 <= z <= .5, then |x|>.8 and |y|>.8
    return !(fabs(pos[0]) < .8 && fabs(pos[1]) < .8 && pos[2] > .25 && pos[2] < .5);
}
 
// return an obstacle-based sampler
ob::ValidStateSamplerPtr allocOBValidStateSampler(const ob::SpaceInformation *si)
{
    // we can perform any additional setup / configuration of a sampler here,
    // but there is nothing to tweak in case of the ObstacleBasedValidStateSampler.
    return std::make_shared<ob::ObstacleBasedValidStateSampler>(si);
}
 
// return an instance of my sampler
ob::ValidStateSamplerPtr allocMyValidStateSampler(const ob::SpaceInformation *si)
{
    return std::make_shared<MyValidStateSampler>(si);
}
 
void plan(int samplerIndex)
{
    // construct the state space we are planning in
    auto space(std::make_shared<ob::RealVectorStateSpace>(3));
 
    // set the bounds
    ob::RealVectorBounds bounds(3);
    bounds.setLow(-1);
    bounds.setHigh(1);
    space->setBounds(bounds);
 
    // define a simple setup class
    og::SimpleSetup ss(space);
 
    // set state validity checking for this space
    ss.setStateValidityChecker(isStateValid);
 
    // create a start state
    ob::ScopedState<> start(space);
    start[0] = start[1] = start[2] = 0;
 
    // create a goal state
    ob::ScopedState<> goal(space);
    goal[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);
    else if (samplerIndex == 2)
        // use my sampler
        ss.getSpaceInformation()->setValidStateSamplerAllocator(allocMyValidStateSampler);
 
    // create a planner for the defined space
    auto planner(std::make_shared<og::PRM>(ss.getSpaceInformation()));
    ss.setPlanner(planner);
 
    // attempt to solve the problem within ten seconds of planning time
    ob::PlannerStatus solved = ss.solve(10.0);
    if (solved)
    {
        std::cout << "Found solution:" << std::endl;
        // print the path to screen
        ss.getSolutionPath().print(std::cout);
    }
    else
        std::cout << "No solution found" << std::endl;
}
 
int main(int /*argc*/, char ** /*argv*/)
{
    std::cout << "Using default uniform sampler:" << std::endl;
    plan(0);
    std::cout << "\nUsing obstacle-based sampler:" << std::endl;
    plan(1);
    std::cout << "\nUsing my sampler:" << std::endl;
    plan(2);
 
    return 0;
}