Eigen::MatrixXf BezierQuadraticGenerator::generateGuess(std::vector<double> start, std::vector<double> end, std::vector<double> parameters){

	Eigen::Map<Eigen::VectorXd> startVec(start.data(), start.size());
	Eigen::Map<Eigen::VectorXd> endVec(end.data(), end.size());
	Eigen::Map<Eigen::VectorXd> midVec(parameters.data(), parameters.size());

	//Error Checking Crap
//	if (startVec.size() != endVec.size() || startVec.size() != midVec.size()){
//		std::cout<< "Dimensions for generateGuess are incorrect"<<std::endl;
//		std::cout<< "Size of start = " << startVec.size();
//		std::cout<< ", Size of mid = " << midVec.size();
//		std::cout<< ", Size of end = " << endVec.size();
//		std::cout<<std::endl;
//	}

	Eigen::MatrixXf trajectory(num_waypoints,start.size());

	for( int row = 0; row < num_waypoints; row++){

		double t = (double) row / (double) (num_waypoints-1);

		//Bezier curves are defined as
		// B(t) = (1-t)^2*P0 + 2*(1-t)*t*P1 +

		Eigen::VectorXd waypoint = ((1-t)*(1-t)*startVec + 2*(1-t)*t*midVec + t*t*endVec);

		trajectory.row(row) = waypoint.cast<float>().transpose();

	}

	return trajectory;

}
void DensoSetup::initialize(RobotBasePtr probot)
{
    const ob::StateSpacePtr &sSpace = getStateSpace();
    sSpace->setup();

    ob::ScopedState<> start(sSpace);
    ob::ScopedState<> goal(sSpace);
    std::vector<double> startVec(sSpace->getDimension(), 0.);
    std::vector<double> goalVec(sSpace->getDimension(), 0.);
    
    unsigned int ndof = si_->getStateSpace()->as<DensoStateSpace>()->
	_probot->GetActiveDOF();
    for (unsigned int i = 0; i < ndof; i++)
	goalVec[i] = 1.0;

    if (_useRealScene)
    {
	startVec[0] = -1.576;
	startVec[1] = 1.374;
	startVec[2] = 0.0;
	startVec[3] = 0.0;
	startVec[4] = -1.36751533;
	startVec[5] = 1.615;
	goalVec[0] = 1.55;
	goalVec[1] = 1.35;
	goalVec[2] = 0.1;
	goalVec[3] = -000057;
	goalVec[4] = -1.40754;
	goalVec[5] = 1.6;
    }
    
    sSpace->copyFromReals(start.get(), startVec);
    sSpace->copyFromReals(goal.get(), goalVec);
    /* set start, goal, and threshold*/
    setStartAndGoalStates(start, goal, threshold);
    /* set propagation step size and min-max propagation steps*/
    si_->setPropagationStepSize(propagationStepSize);
    si_->setMinMaxControlDuration(propagationMinSteps, propagationMaxSteps);
    /* initialize a new KPIECE planner */
    setPlanner(ob::PlannerPtr(new oc::KPIECE1(si_)));
    /* set default parameters */
    getPlanner()->as<oc::KPIECE1>()->setMaxCloseSamplesCount(maxCloseSamplesCount);
    getPlanner()->as<oc::KPIECE1>()->setGoalBias(goalBias);
    /* set a state validity checker */
    setStateValidityChecker(ob::StateValidityCheckerPtr
			    (new DensoStateValidityChecker(si_)));
    si_->setStateValidityCheckingResolution(0.05); //5%
    /* set a state propagator */
    setStatePropagator(oc::StatePropagatorPtr(new DensoStatePropagator(si_)));
}
Exemple #3
0
void KoulesSetup::initialize(unsigned int numKoules, const std::string& plannerName,
    const std::vector<double>& stateVec)
{
    const ob::StateSpacePtr& space = getStateSpace();
    space->setup();
    // setup start state
    ob::ScopedState<> start(space);
    if (stateVec.size() == space->getDimension())
        space->copyFromReals(start.get(), stateVec);
    else
    {
        // Pick koule positions evenly radially distributed, but at a linearly
        // increasing distance from the center. The ship's initial position is
        // at the center. Initial velocities are 0.
        std::vector<double> startVec(space->getDimension(), 0.);
        double r, theta = boost::math::constants::pi<double>(), delta = 2.*theta / numKoules;
        startVec[0] = .5 * sideLength;
        startVec[1] = .5 * sideLength;
        startVec[4] = .5 * delta;
        for (unsigned int i = 0; i < numKoules; ++i, theta += delta)
        {
            r = .1 + i * .1 / numKoules;
            startVec[4 * i + 5] = .5 * sideLength + r * cos(theta);
            startVec[4 * i + 6] = .5 * sideLength + r * sin(theta);
        }
        space->copyFromReals(start.get(), startVec);
    }
    setStartState(start);
    // set goal
    setGoal(std::make_shared<KoulesGoal>(si_));
    // set propagation step size
    si_->setPropagationStepSize(propagationStepSize);
    // set min/max propagation steps
    si_->setMinMaxControlDuration(propagationMinSteps, propagationMaxSteps);
    // set directed control sampler; when using the PDST planner, propagate as long as possible
    bool isPDST = plannerName == "pdst";
    const ompl::base::GoalPtr& goal = getGoal();
    si_->setDirectedControlSamplerAllocator(
        [&goal, isPDST](const ompl::control::SpaceInformation *si)
        {
            return KoulesDirectedControlSamplerAllocator(si,  goal, isPDST);
        });
    // set planner
    setPlanner(getConfiguredPlannerInstance(plannerName));
    // set validity checker
    setStateValidityChecker(std::make_shared<KoulesStateValidityChecker>(si_));
    // set state propagator
    setStatePropagator(std::make_shared<KoulesStatePropagator>(si_));
}
void SuperBotSetup::initialize(RobotBasePtr probot)
{
    const ob::StateSpacePtr &sSpace = getStateSpace();
    sSpace->setup();

    ob::ScopedState<> start(sSpace);
    ob::ScopedState<> goal(sSpace);
    std::vector<double> startVec(sSpace->getDimension(), 0.);
    std::vector<double> goalVec(sSpace->getDimension(), 0.);
    
    unsigned int ndof = si_->getStateSpace()->as<SuperBotStateSpace>()->_probot->GetActiveDOF();
    for (unsigned int i = 0; i < ndof; i++)
	goalVec[i] = 1.0;
    
    sSpace->copyFromReals(start.get(), startVec);
    sSpace->copyFromReals(goal.get(), goalVec);
    // set start, goal, and threshold
    setStartAndGoalStates(start, goal, threshold);
    
    // set propagation step size and min-max propagation steps
    si_->setPropagationStepSize(propagationStepSize);
    si_->setMinMaxControlDuration(propagationMinSteps, propagationMaxSteps);
    
    // initialize a new KPIECE planner
    setPlanner(ob::PlannerPtr(new oc::KPIECE1(si_)));
    
    // set default parameters
    getPlanner()->as<oc::KPIECE1>()->setMaxCloseSamplesCount(maxCloseSamplesCount);
    getPlanner()->as<oc::KPIECE1>()->setGoalBias(goalBias);
    
    // set a state validity checker
    setStateValidityChecker(ob::StateValidityCheckerPtr(new SuperBotStateValidityChecker(si_)));
    si_->setStateValidityCheckingResolution(0.05); //5%
    
    // set a state propagator
    setStatePropagator(oc::StatePropagatorPtr(new SuperBotStatePropagator(si_)));
}
int main(int argc, char **argv)
{
    if (argc < 2)
    {
        std::cout << "Usage:\n" << argv[0] << " <num_links>\n";
        exit(0);
    }

    unsigned int numLinks = boost::lexical_cast<unsigned int>(std::string(argv[1]));
    Environment env = createHornEnvironment(numLinks, log((double)numLinks) / (double)numLinks);
    ompl::base::StateSpacePtr chain(new KinematicChainSpace(numLinks, 1. / (double)numLinks, &env));
    ompl::geometric::SimpleSetup ss(chain);

    ss.setStateValidityChecker(ompl::base::StateValidityCheckerPtr(
        new KinematicChainValidityChecker(ss.getSpaceInformation())));

    ompl::base::ScopedState<> start(chain), goal(chain);
    std::vector<double> startVec(numLinks, boost::math::constants::pi<double>() / (double)numLinks);
    std::vector<double> goalVec(numLinks, 0.);

    startVec[0] = 0.;
    goalVec[0] = boost::math::constants::pi<double>() - .001;
    chain->setup();
    chain->copyFromReals(start.get(), startVec);
    chain->copyFromReals(goal.get(), goalVec);
    ss.setStartAndGoalStates(start, goal);

    // SEKRIT BONUS FEATURE:
    // if you specify a second command line argument, it will solve the
    // problem just once with STRIDE and print out the solution path.
    if (argc > 2)
    {
        ss.setPlanner(ompl::base::PlannerPtr(new ompl::geometric::STRIDE(ss.getSpaceInformation())));
        ss.setup();
        ss.print();
        ss.solve(3600);
        ss.simplifySolution();

        ompl::geometric::PathGeometric path = ss.getSolutionPath();
        std::vector<double> v;
        for(unsigned int i = 0; i < path.getStateCount(); ++i)
        {
            chain->copyToReals(v, path.getState(i));
            std::copy(v.begin(), v.end(), std::ostream_iterator<double>(std::cout, " "));
            std::cout << std::endl;
        }
        exit(0);
    }

    // by default, use the Benchmark class
    double runtime_limit = 60, memory_limit = 1024;
    int run_count = 20;
    ompl::tools::Benchmark::Request request(runtime_limit, memory_limit, run_count, 0.5);
    ompl::tools::Benchmark b(ss, "KinematicChain");
    b.addExperimentParameter("num_links", "INTEGER", std::to_string(numLinks));

    b.addPlanner(ompl::base::PlannerPtr(new ompl::geometric::STRIDE(ss.getSpaceInformation())));
    b.addPlanner(ompl::base::PlannerPtr(new ompl::geometric::EST(ss.getSpaceInformation())));
    b.addPlanner(ompl::base::PlannerPtr(new ompl::geometric::KPIECE1(ss.getSpaceInformation())));
    b.addPlanner(ompl::base::PlannerPtr(new ompl::geometric::RRT(ss.getSpaceInformation())));
    b.addPlanner(ompl::base::PlannerPtr(new ompl::geometric::PRM(ss.getSpaceInformation())));
    b.benchmark(request);
    b.saveResultsToFile(boost::str(boost::format("kinematic_%i.log") % numLinks).c_str());

    exit(0);
}