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);
 
            ~RRTstar() override;
 
            void getPlannerData(base::PlannerData &data) const override;
 
            base::PlannerStatus solve(const base::PlannerTerminationCondition &ptc) override;
 
            void clear() override;
 
            void setup() override;
 
            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()
            {
                if (nn_ && nn_->size() != 0)
                    OMPL_WARN("Calling setNearestNeighbors will clear all states.");
                clear();
                nn_ = std::make_shared<NN<Motion *>>();
                setup();
            }
 
            void setDelayCC(bool delayCC)
            {
                delayCC_ = delayCC;
            }
 
            bool getDelayCC() const
            {
                return delayCC_;
            }
 
            void setTreePruning(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(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 setOrderedSampling(bool orderSamples);
 
            bool getOrderedSampling() const
            {
                return useOrderedSampling_;
            }
 
            void setBatchSize(unsigned int batchSize)
            {
                batchSize_ = batchSize;
            }
 
            unsigned int getBatchSize() const
            {
                return batchSize_;
            }
 
            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), inGoal(false)
                {
                }
 
                ~Motion() = default;
 
                base::State *state;
 
                Motion *parent;
 
                bool inGoal;
 
                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_{.05};
 
            double maxDistance_{0.};
 
            RNG rng_;
 
            bool useKNearest_{true};
 
            double rewireFactor_{1.1};
 
            double k_rrt_{0u};
 
            double r_rrt_{0.};
 
            bool delayCC_{true};
 
            base::OptimizationObjectivePtr opt_;
 
            Motion *bestGoalMotion_{nullptr};
 
            std::vector<Motion *> goalMotions_;
 
            bool useTreePruning_{false};
 
            double pruneThreshold_{.05};
 
            bool usePrunedMeasure_{false};
 
            bool useInformedSampling_{false};
 
            bool useRejectionSampling_{false};
 
            bool useNewStateRejection_{false};
 
            bool useAdmissibleCostToCome_{true};
 
            unsigned int numSampleAttempts_{100u};
 
            bool useOrderedSampling_{false};
 
            unsigned int batchSize_{1u};
 
            std::vector<Motion *> startMotions_;
 
            base::Cost bestCost_{std::numeric_limits<double>::quiet_NaN()};
 
            base::Cost prunedCost_{std::numeric_limits<double>::quiet_NaN()};
 
            double prunedMeasure_{0.};
 
            unsigned int iterations_{0u};
 
            // Planner progress property functions
            std::string numIterationsProperty() const
            {
                return std::to_string(numIterations());
            }
            std::string bestCostProperty() const
            {
                return std::to_string(bestCost().value());
            }
        };
    }
}
 
#endif