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/* Author: Ioan Sucan */

#ifndef OMPL_GEOMETRIC_PLANNERS_KPIECE_KPIECE1_
#define OMPL_GEOMETRIC_PLANNERS_KPIECE_KPIECE1_

#include "ompl/geometric/planners/PlannerIncludes.h"
#include "ompl/geometric/planners/kpiece/Discretization.h"

namespace ompl
{
    namespace geometric
    {
        /**
           @anchor gKPIECE1
           @par Short description
           KPIECE is a tree-based planner that uses a discretization
           (multiple levels, in general) to guide the exploration of
           the continuous space. This implementation is a simplified
           one, using a single level of discretization: one grid. The
           grid is imposed on a projection of the state space. When
           exploring the space, preference is given to the boundary of
           this grid. The boundary is computed to be the set of grid
           cells that have less than 2n non-diagonal neighbors in an
           n-dimensional projection space.
           It is important to set the projection the algorithm uses
           (setProjectionEvaluator() function). If no projection is
           set, the planner will attempt to use the default projection
           associated to the state space. An exception is thrown if
           no default projection is available either.
           @par External documentation
           I.A. Şucan and L.E. Kavraki, Kinodynamic motion planning by interior-exterior cell exploration,
           in <em>Workshop on the Algorithmic Foundations of Robotics</em>, Dec. 2008.<br>
           [[PDF]](http://ioan.sucan.ro/files/pubs/wafr2008.pdf)
        */

        /** \brief Kinematic Planning by Interior-Exterior Cell Exploration */
        class KPIECE1 : public base::Planner
        {
        public:
            /** \brief Constructor */
            KPIECE1(const base::SpaceInformationPtr &si);

            ~KPIECE1() override;

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

            void clear() override;

            /** \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 fraction of time for focusing on the
                border (between 0 and 1). This is the minimum fraction
                used to select cells that are exterior (minimum
                because if 95% of cells are on the border, they will
                be selected with 95% chance, even if this fraction is
                set to 90%)*/
            void setBorderFraction(double bp)
            {
                disc_.setBorderFraction(bp);
            }

            /** \brief Get the fraction of time to focus exploration
                on boundary */
            double getBorderFraction() const
            {
                return disc_.getBorderFraction();
            }

            /** \brief When extending a motion, the planner can decide
                to keep the first valid part of it, even if invalid
                states are found, as long as the valid part represents
                a sufficiently large fraction from the original
                motion. This function sets the minimum acceptable
                fraction (between 0 and 1). */
            void setMinValidPathFraction(double fraction)
            {
                minValidPathFraction_ = fraction;
            }

            /** \brief Get the value of the fraction set by setMinValidPathFraction() */
            double getMinValidPathFraction() const
            {
                return minValidPathFraction_;
            }

            /** \brief When extending a motion from a cell, the
                extension can be successful or it can fail. If the
                extension fails, the score of the cell is multiplied
                by \e factor. These number should be in the range (0, 1]. */
            void setFailedExpansionCellScoreFactor(double factor)
            {
                failedExpansionScoreFactor_ = factor;
            }

            /** \brief Get the factor that is multiplied to a cell's
                score if extending a motion from that cell failed. */
            double getFailedExpansionCellScoreFactor() const
            {
                return failedExpansionScoreFactor_;
            }

            /** \brief Set the projection evaluator. This class is
                able to compute the projection of a given state. */
            void setProjectionEvaluator(const base::ProjectionEvaluatorPtr &projectionEvaluator)
            {
                projectionEvaluator_ = projectionEvaluator;
            }

            /** \brief Set the projection evaluator (select one from
                the ones registered with the state space). */
            void setProjectionEvaluator(const std::string &name)
            {
                projectionEvaluator_ = si_->getStateSpace()->getProjection(name);
            }

            /** \brief Get the projection evaluator */
            const base::ProjectionEvaluatorPtr &getProjectionEvaluator() const
            {
                return projectionEvaluator_;
            }

            void setup() override;

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

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

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

                ~Motion() = default;

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

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

            /** \brief Free the memory for a motion */
            void freeMotion(Motion *motion);

            /** \brief A state space sampler */
            base::StateSamplerPtr sampler_;

            /** \brief The tree datastructure and the grid that covers it */
            Discretization<Motion> disc_;

            /** \brief This algorithm uses a discretization (a grid)
                to guide the exploration. The exploration is imposed
                on a projection of the state space. */
            base::ProjectionEvaluatorPtr projectionEvaluator_;

            /** \brief When extending a motion from a cell, the
                extension can fail. If it is, the score of the cell is
                multiplied by this factor. */
            double failedExpansionScoreFactor_;

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

            /** \brief When extending a motion, the planner can decide
                to keep the first valid part of it, even if invalid
                states are found, as long as the valid part represents
                a sufficiently large fraction from the original
                motion */
            double minValidPathFraction_;

            /** \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
