RRTstar.h

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/* Authors: Alejandro Perez, Sertac Karaman, Ryan Luna, Luis G. Torres, Ioan Sucan, Javier V Gomez, Jonathan Gammell */

#ifndef OMPL_GEOMETRIC_PLANNERS_RRT_RRTSTAR_
#define OMPL_GEOMETRIC_PLANNERS_RRT_RRTSTAR_

#include "ompl/geometric/planners/PlannerIncludes.h"
#include "ompl/base/OptimizationObjective.h"
#include "ompl/datastructures/NearestNeighbors.h"

#include <limits>
#include <vector>
#include <queue>
#include <deque>
#include <utility>
#include <list>


namespace ompl
{

    namespace geometric
    {

        class RRTstar : public base::Planner
        {
        public:

            RRTstar(const base::SpaceInformationPtr &si);

            virtual ~RRTstar();

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

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

            virtual void clear();

            virtual void setup();

            void setGoalBias(double goalBias)
            {
                goalBias_ = goalBias;
            }

            double getGoalBias() const
            {
                return goalBias_;
            }

            void setRange(double distance)
            {
                maxDistance_ = distance;
            }

            double getRange() const
            {
                return maxDistance_;
            }

            void setRewireFactor(double rewireFactor)
            {
                rewireFactor_ = rewireFactor;
                calculateRewiringLowerBounds();
            }

            double getRewireFactor() const
            {
                return rewireFactor_;
            }

            template<template<typename T> class NN>
            void setNearestNeighbors()
            {
                nn_.reset(new NN<Motion*>());
            }

            void setDelayCC(bool delayCC)
            {
                delayCC_ = delayCC;
            }

            bool getDelayCC() const
            {
                return delayCC_;
            }

            void setTreePruning(const bool prune);

            bool getTreePruning() const
            {
                return useTreePruning_;
            }

            void setPruneThreshold(const double pp)
            {
                pruneThreshold_ = pp;
            }

            double getPruneThreshold() const
            {
                return pruneThreshold_;
            }

            void setPrunedMeasure(bool informedMeasure);

            bool getPrunedMeasure() const
            {
                return usePrunedMeasure_;
            }

            void setInformedSampling(bool informedSampling);

            bool getInformedSampling() const
            {
                return useInformedSampling_;
            }

            void setSampleRejection(const bool reject);

            bool getSampleRejection() const
            {
                return useRejectionSampling_;
            }

            void setNewStateRejection(const bool reject)
            {
                useNewStateRejection_ = reject;
            }

            bool getNewStateRejection() const
            {
                return useNewStateRejection_;
            }

            void setAdmissibleCostToCome(const bool admissible)
            {
                useAdmissibleCostToCome_ = admissible;
            }

            bool getAdmissibleCostToCome() const
            {
                return useAdmissibleCostToCome_;
            }

            void setFocusSearch(const bool focus)
            {
                setInformedSampling(focus);
                setTreePruning(focus);
                setPrunedMeasure(focus);
                setNewStateRejection(focus);
            }

            bool getFocusSearch() const
            {
                return getInformedSampling() && getPrunedMeasure() && getTreePruning() && getNewStateRejection();
            }

            void setKNearest(bool useKNearest)
            {
                useKNearest_ = useKNearest;
            }

            bool getKNearest() const
            {
                return useKNearest_;
            }

            void setNumSamplingAttempts(unsigned int numAttempts)
            {
                numSampleAttempts_ = numAttempts;
            }

            unsigned int getNumSamplingAttempts() const
            {
                return numSampleAttempts_ ;
            }

            unsigned int numIterations() const
            {
                return iterations_;
            }

            ompl::base::Cost bestCost() const
            {
                return bestCost_;
            }

        protected:

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

                ~Motion()
                {
                }

                base::State       *state;

                Motion            *parent;

                base::Cost        cost;

                base::Cost        incCost;

                std::vector<Motion*> children;
            };

            void allocSampler();

            bool sampleUniform(base::State *statePtr);

            void freeMemory();

            // For sorting a list of costs and getting only their sorted indices
            struct CostIndexCompare
            {
                CostIndexCompare(const std::vector<base::Cost>& costs,
                                 const base::OptimizationObjective &opt) :
                    costs_(costs), opt_(opt)
                {}
                bool operator()(unsigned i, unsigned j)
                {
                    return opt_.isCostBetterThan(costs_[i],costs_[j]);
                }
                const std::vector<base::Cost>& costs_;
                const base::OptimizationObjective &opt_;
            };

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

            void getNeighbors(Motion *motion, std::vector<Motion*> &nbh) const;

            void removeFromParent(Motion *m);

            void updateChildCosts(Motion *m);

            int pruneTree(const base::Cost& pruneTreeCost);

            base::Cost solutionHeuristic(const Motion *motion) const;

            void addChildrenToList(std::queue<Motion*, std::deque<Motion*> > *motionList, Motion* motion);

            bool keepCondition(const Motion* motion, const base::Cost& threshold) const;

            void calculateRewiringLowerBounds();

            base::StateSamplerPtr                          sampler_;

            base::InformedSamplerPtr                       infSampler_;

            std::shared_ptr< NearestNeighbors<Motion*> > nn_;

            double                                         goalBias_;

            double                                         maxDistance_;

            RNG                                            rng_;

            bool                                           useKNearest_;

            double                                         rewireFactor_;

            double                                         k_rrg_;
            double                                         r_rrg_;

            bool                                           delayCC_;

            base::OptimizationObjectivePtr                 opt_;

            Motion                                         *lastGoalMotion_;

            std::vector<Motion*>                           goalMotions_;

            bool                                           useTreePruning_;

            double                                         pruneThreshold_;

            bool                                           usePrunedMeasure_;

            bool                                           useInformedSampling_;

            bool                                           useRejectionSampling_;

            bool                                           useNewStateRejection_;

            bool                                           useAdmissibleCostToCome_;

            unsigned int                                   numSampleAttempts_;

            std::vector<Motion*>                           startMotions_;

            base::Cost                                     bestCost_;

            base::Cost                                     prunedCost_;

            double                                         prunedMeasure_;

            unsigned int                                   iterations_;

            // Planner progress property functions
            std::string numIterationsProperty() const
            {
                return std::to_string(numIterations());
            }
            std::string bestCostProperty() const
            {
                return std::to_string(bestCost().value());
            }
        };
    }
}

#endif