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/* Author: Ioan Sucan */

#ifndef OMPL_GEOMETRIC_PLANNERS_RRT_pRRT_
#define OMPL_GEOMETRIC_PLANNERS_RRT_pRRT_

#include "ompl/geometric/planners/PlannerIncludes.h"
#include "ompl/base/StateSamplerArray.h"
#include "ompl/datastructures/NearestNeighbors.h"
#include <boost/thread/mutex.hpp>

namespace ompl
{

    namespace geometric
    {

        /**
           @anchor gpRRT
           @par Short description
           RRT is a tree-based motion planner that uses the following
           idea: RRT samples a random state @b qr in the state space,
           then finds the state @b qc among the previously seen states
           that is closest to @b qr and expands from @b qc towards @b
           qr, until a state @b qm is reached. @b qm is then added to
           the exploration tree.
           @par External documentation
           J. Kuffner and S.M. LaValle, RRT-connect: An efficient approach to single-query path planning, in <em>Proc. 2000 IEEE Intl. Conf. on Robotics and Automation</em>, pp. 995–1001, Apr. 2000. DOI: [10.1109/ROBOT.2000.844730](http://dx.doi.org/10.1109/ROBOT.2000.844730)<br>
           [[PDF]](http://ieeexplore.ieee.org/ielx5/6794/18246/00844730.pdf?tp=&arnumber=844730&isnumber=18246)
           [[more]](http://msl.cs.uiuc.edu/~lavalle/rrtpubs.html)
        */

        /** \brief Parallel RRT */
        class pRRT : public base::Planner
        {
        public:

            pRRT(const base::SpaceInformationPtr &si);

            virtual ~pRRT();

            virtual void getPlannerData(base::PlannerData &data) const;

            virtual base::PlannerStatus solve(const base::PlannerTerminationCondition &ptc);

            virtual void clear();

            /** \brief Set the goal bias.

                In the process of randomly selecting states in the state
                space to attempt to go towards, the algorithm may in fact
                choose the actual goal state, if it knows it, with some
                probability. This probability is a real number between 0.0
                and 1.0; its value should usually be around 0.05 and
                should not be too large. It is probably a good idea to use
                the default value. */
            void setGoalBias(double goalBias)
            {
                goalBias_ = goalBias;
            }

            /** \brief Get the goal bias the planner is using */
            double getGoalBias() const
            {
                return goalBias_;
            }

            /** \brief Set the range the planner is supposed to use.

                This parameter greatly influences the runtime of the
                algorithm. It represents the maximum length of a
                motion to be added in the tree of motions. */
            void setRange(double distance)
            {
                maxDistance_ = distance;
            }

            /** \brief Get the range the planner is using */
            double getRange() const
            {
                return maxDistance_;
            }

            /** \brief Set the number of threads the planner should use. Default is 2. */
            void setThreadCount(unsigned int nthreads);

            unsigned int getThreadCount() const
            {
                return threadCount_;
            }

            /** \brief Set a different nearest neighbors datastructure */
            template<template<typename T> class NN>
            void setNearestNeighbors()
            {
                nn_.reset(new NN<Motion*>());
            }

            virtual void setup();

        protected:

            class Motion
            {
            public:

                Motion() : state(NULL), parent(NULL)
                {
                }

                Motion(const base::SpaceInformationPtr &si) : state(si->allocState()), parent(NULL)
                {
                }

                ~Motion()
                {
                }

                base::State       *state;
                Motion            *parent;

            };

            struct SolutionInfo
            {
                Motion      *solution;
                Motion      *approxsol;
                double       approxdif;
                boost::mutex lock;
            };

            void threadSolve(unsigned int tid, const base::PlannerTerminationCondition &ptc, SolutionInfo *sol);
            void freeMemory();

            double distanceFunction(const Motion *a, const Motion *b) const
            {
                return si_->distance(a->state, b->state);
            }

            base::StateSamplerArray<base::StateSampler>         samplerArray_;
            boost::shared_ptr< NearestNeighbors<Motion*> >      nn_;
            boost::mutex                                        nnLock_;

            unsigned int                                        threadCount_;

            double                                              goalBias_;
            double                                              maxDistance_;

            /** \brief The most recent goal motion.  Used for PlannerData computation */
            Motion                                              *lastGoalMotion_;
        };

    }
}

#endif
