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/* Author: Ioan Sucan */

#ifndef OMPL_GEOMETRIC_PLANNERS_RRT_LAZY_RRT_
#define OMPL_GEOMETRIC_PLANNERS_RRT_LAZY_RRT_

#include "ompl/geometric/planners/PlannerIncludes.h"
#include "ompl/datastructures/NearestNeighbors.h"
#include <vector>

namespace ompl
{
    namespace geometric
    {
        /**
           @anchor gLazyRRT
           @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.
           The difference between \ref gRRT "RRT" and LazyRRT is that when moving
           towards the new state @b qm, LazyRRT does not check to make
           sure the path is valid. Instead, it is optimistic and
           attempts to find a path as soon as possible. Once a path is
           found, it is checked for collision. If collisions are
           found, the invalid path segments are removed and the search
           process is continued.
           @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)
           - R. Bohlin and L.E. Kavraki, A Randomized Algorithm for Robot Path Planning Based on Lazy Evaluation, in
           <em>Handbook on Randomized Computing</em>, pp. 221–249, 2001.<br>
           [[PDF]](http://www.kavrakilab.org/sites/default/files/bohlin2001lazy-evaluation.pdf)
           - R. Bohlin and L.E. Kavraki, Path planning using lazy PRM, in <em>Proc. 2000 IEEE Intl. Conf. on Robotics
           and Automation</em>, pp. 521–528, 2000. DOI:
           [10.1109/ROBOT.2000.844107](http://dx.doi.org/10.1109/ROBOT.2000.844107)<br>
           [[PDF]](http://ieeexplore.ieee.org/ielx5/6794/18235/00844107.pdf?tp=&arnumber=844107&isnumber=18235)
        */

        /** \brief Lazy RRT */
        class LazyRRT : public base::Planner
        {
        public:
            /** \brief Constructor */
            LazyRRT(const base::SpaceInformationPtr &si);

            ~LazyRRT() override;

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

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

            void clear() override;

            /** \brief Set the goal biasing.

                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 a different nearest neighbors datastructure */
            template <template <typename T> class NN>
            void setNearestNeighbors()
            {
                if (nn_ && nn_->size() != 0)
                    OMPL_WARN("Calling setNearestNeighbors will clear all states.");
                clear();
                nn_ = std::make_shared<NN<Motion *>>();
                setup();
            }

            void setup() override;

        protected:
            /** \brief Representation of a motion */
            class Motion
            {
            public:
                Motion() : state(nullptr), parent(nullptr), valid(false)
                {
                }

                /** \brief Constructor that allocates memory for the state */
                Motion(const base::SpaceInformationPtr &si) : state(si->allocState()), parent(nullptr), valid(false)
                {
                }

                ~Motion() = default;

                /** \brief The state contained by the motion */
                base::State *state;

                /** \brief The parent motion in the exploration tree */
                Motion *parent;

                /** \brief Flag indicating whether this motion has been validated */
                bool valid;

                /** \brief The set of motions that descend from this one */
                std::vector<Motion *> children;
            };

            /** \brief Free the memory allocated by this planner */
            void freeMemory();

            /** \brief Remove a motion from the tree datastructure */
            void removeMotion(Motion *motion);

            /** \brief Compute distance between motions (actually distance between contained states) */
            double distanceFunction(const Motion *a, const Motion *b) const
            {
                return si_->distance(a->state, b->state);
            }

            /** \brief State sampler */
            base::StateSamplerPtr sampler_;

            /** \brief A nearest-neighbors datastructure containing the tree of motions */
            std::shared_ptr<NearestNeighbors<Motion *>> nn_;

            /** \brief The fraction of time the goal is picked as the state to expand towards (if such a state is
             * available) */
            double goalBias_;

            /** \brief The maximum length of a motion to be added to a tree */
            double maxDistance_;

            /** \brief The random number generator */
            RNG rng_;

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

#endif
