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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 <thread>
  #include <mutex>
  
  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);
  
              ~pRRT() override;
  
              void getPlannerData(base::PlannerData &data) const 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 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()
              {
                  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:
              class Motion
              {
              public:
                  Motion() = default;
  
                  Motion(const base::SpaceInformationPtr &si) : state(si->allocState())
                  {
                  }
  
                  ~Motion() = default;
  
                  base::State *state{nullptr};
                  Motion *parent{nullptr};
              };
  
              struct SolutionInfo
              {
                  Motion *solution;
                  Motion *approxsol;
                  double approxdif;
                  std::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_;
              std::shared_ptr<NearestNeighbors<Motion *>> nn_;
              std::mutex nnLock_;
  
              unsigned int threadCount_;
  
              double goalBias_{.05};
              double maxDistance_{0.};
  
              /** \brief The most recent goal motion.  Used for PlannerData computation */
              Motion *lastGoalMotion_{nullptr};
          };
      }
  }
  
  #endif
  