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/* Authors: Jonathan Gammell */

//My definition:
#include "ompl/geometric/planners/bitstar/BITstar.h"

//For, you know, math
#include <cmath>
//For stringstreams
#include <sstream>
//For stream manipulations
#include <iomanip>
//For boost make_shared
#include <boost/make_shared.hpp>
//For boost::bind
#include <boost/bind.hpp>
//For boost math constants
#include <boost/math/constants/constants.hpp>
//For boost::math::isfinite
#include <boost/math/special_functions/fpclassify.hpp>

//For OMPL_INFORM et al.
#include "ompl/util/Console.h"
//For exceptions:
#include "ompl/util/Exception.h"
// For geometric equations like unitNBallMeasure
#include "ompl/util/GeometricEquations.h"
//For ompl::base::GoalSampleableRegion, which both GoalState and GoalStates derive from:
#include "ompl/base/goals/GoalSampleableRegion.h"
//For getDefaultNearestNeighbors
#include "ompl/tools/config/SelfConfig.h"
//For ompl::geometric::path
#include "ompl/geometric/PathGeometric.h"
//For the default optimization objective:
#include "ompl/base/objectives/PathLengthOptimizationObjective.h"



namespace ompl
{
    namespace geometric
    {
        /////////////////////////////////////////////////////////////////////////////////////////////
        //Public functions:
        BITstar::BITstar(const ompl::base::SpaceInformationPtr& si, const std::string& name /*= "BITstar"*/)
            : ompl::base::Planner(si, name),
            sampler_(),
            opt_(),
            startVertices_(),
            goalVertices_(),
            prunedStartVertices_(),
            prunedGoalVertices_(),
            curGoalVertex_(),
            freeStateNN_(),
            vertexNN_(),
            intQueue_(),
            numUniformStates_(0u),
            r_(0.0), //Purposeful Gibberish
            k_rgg_(0.0), //Purposeful Gibberish
            k_(0u), //Purposeful Gibberish
            bestCost_( std::numeric_limits<double>::infinity() ), //Gets set in setup to the proper calls from OptimizationObjective
            bestLength_(0u),
            prunedCost_( std::numeric_limits<double>::infinity() ), //Gets set in setup to the proper calls from OptimizationObjective
            prunedMeasure_(0.0), //Gets set in setup with the proper call to Planner::si_->getSpaceMeasure()
            minCost_( std::numeric_limits<double>::infinity() ), //Gets set in setup to the proper calls from OptimizationObjective
            costSampled_( std::numeric_limits<double>::infinity() ), //Gets set in setup to the proper calls from OptimizationObjective
            hasSolution_(false),
            stopLoop_(false),
            approximateSoln_(false),
            approximateDiff_(-1.0),
            numIterations_(0u),
            numBatches_(0u),
            numPrunings_(0u),
            numSamples_(0u),
            numVertices_(0u),
            numFreeStatesPruned_(0u),
            numVerticesDisconnected_(0u),
            numRewirings_(0u),
            numStateCollisionChecks_(0u),
            numEdgeCollisionChecks_(0u),
            numNearestNeighbours_(0u),
            numEdgesProcessed_(0u),
            useStrictQueueOrdering_(true),
            rewireFactor_(2.0),
            samplesPerBatch_(100u),
            useKNearest_(true),
            usePruning_(true),
            pruneFraction_(0.02),
            delayRewiring_(false),
            useJustInTimeSampling_(false),
            dropSamplesOnPrune_(false),
            useFailureTracking_(false),
            stopOnSolnChange_(false)
        {
            //Specify my planner specs:
            Planner::specs_.recognizedGoal = ompl::base::GOAL_SAMPLEABLE_REGION;
            Planner::specs_.multithreaded = false;
            Planner::specs_.approximateSolutions = false; //For now!
            Planner::specs_.optimizingPaths = true;
            Planner::specs_.directed = true;
            Planner::specs_.provingSolutionNonExistence = false;

            //Register my setting callbacks
            Planner::declareParam<double>("rewire_factor", this, &BITstar::setRewireFactor, &BITstar::getRewireFactor, "1.0:0.01:3.0");
            Planner::declareParam<unsigned int>("samples_per_batch", this, &BITstar::setSamplesPerBatch, &BITstar::getSamplesPerBatch, "1:1:1000000");
            Planner::declareParam<bool>("use_k_nearest", this, &BITstar::setKNearest, &BITstar::getKNearest, "0,1");
            Planner::declareParam<bool>("use_graph_pruning", this, &BITstar::setPruning, &BITstar::getPruning, "0,1");
            Planner::declareParam<double>("prune_threshold_as_fractional_cost_change", this, &BITstar::setPruneThresholdFraction, &BITstar::getPruneThresholdFraction, "0.0:0.01:1.0");
            Planner::declareParam<bool>("delay_rewiring_to_first_solution", this, &BITstar::setDelayRewiringUntilInitialSolution, &BITstar::getDelayRewiringUntilInitialSolution, "0,1");
            Planner::declareParam<bool>("use_just_in_time_sampling", this, &BITstar::setJustInTimeSampling, &BITstar::getJustInTimeSampling, "0,1");
            Planner::declareParam<bool>("drop_unconnected_samples_on_prune", this, &BITstar::setDropSamplesOnPrune, &BITstar::getDropSamplesOnPrune, "0,1");
            Planner::declareParam<bool>("stop_on_each_solution_improvement", this, &BITstar::setStopOnSolnImprovement, &BITstar::getStopOnSolnImprovement, "0,1");

            //More advanced setting callbacks that aren't necessary to be exposed to Python. Uncomment if desired.
            //Planner::declareParam<bool>("use_strict_queue_ordering", this, &BITstar::setStrictQueueOrdering, &BITstar::getStrictQueueOrdering, "0,1");
            //Planner::declareParam<bool>("use_edge_failure_tracking", this, &BITstar::setUseFailureTracking, &BITstar::getUseFailureTracking, "0,1");

            //Register my progress info:
            addPlannerProgressProperty("best cost DOUBLE", boost::bind(&BITstar::bestCostProgressProperty, this));
            addPlannerProgressProperty("number of segments in solution path INTEGER", boost::bind(&BITstar::bestLengthProgressProperty, this));
            addPlannerProgressProperty("current free states INTEGER", boost::bind(&BITstar::currentFreeProgressProperty, this));
            addPlannerProgressProperty("current graph vertices INTEGER", boost::bind(&BITstar::currentVertexProgressProperty, this));
            addPlannerProgressProperty("state collision checks INTEGER", boost::bind(&BITstar::stateCollisionCheckProgressProperty, this));
            addPlannerProgressProperty("edge collision checks INTEGER", boost::bind(&BITstar::edgeCollisionCheckProgressProperty, this));
            addPlannerProgressProperty("nearest neighbour calls INTEGER", boost::bind(&BITstar::nearestNeighbourProgressProperty, this));

            //Extra progress info that aren't necessary for every day use. Uncomment if desired.
            //addPlannerProgressProperty("vertex queue size INTEGER", boost::bind(&BITstar::vertexQueueSizeProgressProperty, this));
            //addPlannerProgressProperty("edge queue size INTEGER", boost::bind(&BITstar::edgeQueueSizeProgressProperty, this));
            //addPlannerProgressProperty("iterations INTEGER", boost::bind(&BITstar::iterationProgressProperty, this));
            //addPlannerProgressProperty("batches INTEGER", boost::bind(&BITstar::batchesProgressProperty, this));
            //addPlannerProgressProperty("graph prunings INTEGER", boost::bind(&BITstar::pruningProgressProperty, this));
            //addPlannerProgressProperty("total states generated INTEGER", boost::bind(&BITstar::totalStatesCreatedProgressProperty, this));
            //addPlannerProgressProperty("vertices constructed INTEGER", boost::bind(&BITstar::verticesConstructedProgressProperty, this));
            //addPlannerProgressProperty("states pruned INTEGER", boost::bind(&BITstar::statesPrunedProgressProperty, this));
            //addPlannerProgressProperty("graph vertices disconnected INTEGER", boost::bind(&BITstar::verticesDisconnectedProgressProperty, this));
            //addPlannerProgressProperty("rewiring edges INTEGER", boost::bind(&BITstar::rewiringProgressProperty, this));
        }



        BITstar::~BITstar()
        {
        }



        void BITstar::setup()
        {
            //Call the base class setup:
            Planner::setup();

            //Do some sanity checks
            //Make sure we have a problem definition
            if(static_cast<bool>(Planner::pdef_) == false)
            {
                OMPL_ERROR("%s::setup() was called without a problem definition.", Planner::getName().c_str());
                Planner::setup_ = false;
                return;
            }

            //Make sure we have an optimization objective
            if (Planner::pdef_->hasOptimizationObjective() == false)
            {
                OMPL_INFORM("%s: No optimization objective specified. Defaulting to optimizing path length.", Planner::getName().c_str());
                Planner::pdef_->setOptimizationObjective( boost::make_shared<base::PathLengthOptimizationObjective> (Planner::si_) );
            }

            //If the problem definition *has* a goal, make sure it is of appropriate type
            if (static_cast<bool>(Planner::pdef_->getGoal()) == true)
            {
                if (Planner::pdef_->getGoal()->hasType(ompl::base::GOAL_SAMPLEABLE_REGION) == false)
                {
                    OMPL_ERROR("%s::setup() BIT* currently only supports goals that can be cast to a sampleable goal region (i.e., are countable sets).", Planner::getName().c_str());
                    Planner::setup_ = false;
                    return;
                }
                //No else, of correct type.
            }
            //No else, called without a goal. Is this MoveIt?

            //Store the optimization objective for future ease of use
            opt_ = Planner::pdef_->getOptimizationObjective();

            //Configure the nearest-neighbour constructs.
            //Only allocate if they are empty (as they can be set to a specific version by a call to setNearestNeighbors)
            if (static_cast<bool>(freeStateNN_) == false)
            {
                freeStateNN_.reset( ompl::tools::SelfConfig::getDefaultNearestNeighbors<VertexPtr>(this) );
            }
            //No else, already allocated (by a call to setNearestNeighbors())

            if (static_cast<bool>(vertexNN_) == false)
            {
                vertexNN_.reset( ompl::tools::SelfConfig::getDefaultNearestNeighbors<VertexPtr>(this) );
            }
            //No else, already allocated (by a call to setNearestNeighbors())

            //Configure:
            freeStateNN_->setDistanceFunction(boost::bind(&BITstar::nnDistance, this, _1, _2));
            vertexNN_->setDistanceFunction(boost::bind(&BITstar::nnDistance, this, _1, _2));

            //Configure the queue
            //boost::make_shared can only take 9 arguments, so be careful:
            intQueue_ = boost::make_shared<IntegratedQueue> (opt_, boost::bind(&BITstar::nearestSamples, this, _1, _2), boost::bind(&BITstar::nearestVertices, this, _1, _2), boost::bind(&BITstar::lowerBoundHeuristicVertex, this, _1), boost::bind(&BITstar::currentHeuristicVertex, this, _1), boost::bind(&BITstar::lowerBoundHeuristicEdge, this, _1), boost::bind(&BITstar::currentHeuristicEdge, this, _1), boost::bind(&BITstar::currentHeuristicEdgeTarget, this, _1));
            intQueue_->setUseFailureTracking(useFailureTracking_);
            intQueue_->setDelayedRewiring(delayRewiring_);

            //Set the best-cost, pruned-cost, sampled-cost and min-cost to the proper opt_-based values:
            bestCost_ = opt_->infiniteCost();
            prunedCost_ = opt_->infiniteCost();
            minCost_ = opt_->infiniteCost();
            costSampled_ = opt_->infiniteCost();

            //Add any start and goals vertices that exist to the queue, but do NOT wait for any more goals:
            this->updateStartAndGoalStates(ompl::base::plannerAlwaysTerminatingCondition());

            //Get the measure of the problem
            prunedMeasure_ =  Planner::si_->getSpaceMeasure();

            //Does the problem have finite boundaries?
            if (boost::math::isfinite(prunedMeasure_) == false)
            {
                //It does not, so let's estimate a measure of the planning problem.
                //A not horrible place to start would be hypercube proportional to the distance between the start and goal. It's not *great*, but at least it sort of captures the order-of-magnitude of the problem.

                //First, some asserts.
                //Check that JIT sampling is on, which is required for infinite problems
                if (useJustInTimeSampling_ == false)
                {
                    throw ompl::Exception("For unbounded planning problems, just-in-time sampling must be enabled before calling setup.");
                }
                //No else

                //Check that we have a start and goal
                if (startVertices_.empty() == true || goalVertices_.empty() == true)
                {
                    throw ompl::Exception("For unbounded planning problems, at least one start and one goal must exist before calling setup.");
                }
                //No else

                //Variables
                //The maximum distance between start and goal:
                double maxDist = 0.0;
                //The scale on the maximum distance, i.e. the width of the hypercube is equal to this value times the distance between start and goal.
                //This number is completely made up.
                double distScale = 2.0;

                //Find the max distance
                for (std::list<VertexPtr>::const_iterator sIter = startVertices_.begin(); sIter != startVertices_.end(); ++sIter)
                {
                    for (std::list<VertexPtr>::const_iterator gIter = goalVertices_.begin(); gIter != goalVertices_.end(); ++gIter)
                    {
                        maxDist = std::max(maxDist, Planner::si_->distance((*sIter)->stateConst(), (*gIter)->stateConst()));
                    }
                }

                //Calculate an estimate of the problem measure by (hyper)cubing the max distance
                prunedMeasure_ = std::pow(distScale*maxDist, Planner::si_->getStateDimension());
            }
            //No else, finite problem dimension

            //Finally initialize the nearestNeighbour terms:
            this->initializeNearestTerms();

            //Debug: Output an estimate of the state measure:
            //this->estimateMeasures();
        }



        void BITstar::clear()
        {
            //Clear all the variables.
            //Keep this in the order of the constructors list:

            //The various convenience pointers:
            sampler_.reset();
            opt_.reset();
            startVertices_.clear();
            goalVertices_.clear();
            prunedStartVertices_.clear();
            prunedGoalVertices_.clear();
            curGoalVertex_.reset();

            //The list of samples
            if (static_cast<bool>(freeStateNN_) == true)
            {
                freeStateNN_->clear();
                freeStateNN_.reset();
            }
            //No else, not allocated

            //The list of vertices
            if (static_cast<bool>(vertexNN_) == true)
            {
                vertexNN_->clear();
                vertexNN_.reset();
            }

            //The queue:
            if (static_cast<bool>(intQueue_) == true)
            {
                intQueue_->clear();
                intQueue_.reset();
            }

            //DO NOT reset the parameters:
            //useStrictQueueOrdering_
            //rewireFactor_
            //samplesPerBatch_
            //useKNearest_
            //usePruning_
            //pruneFraction_
            //delayRewiring_
            //useJustInTimeSampling_
            //dropSamplesOnPrune_
            //useFailureTracking_
            //stopOnSolnChange_

            //Reset the various calculations? TODO: Should I recalculate them?
            numUniformStates_ = 0u;
            r_ = 0.0;
            k_rgg_ = 0.0; //This is a double for better rounding later
            k_ = 0u;
            bestCost_ = ompl::base::Cost(std::numeric_limits<double>::infinity());
            bestLength_ = 0u;
            prunedCost_ = ompl::base::Cost(std::numeric_limits<double>::infinity());
            prunedMeasure_ = Planner::si_->getSpaceMeasure();
            minCost_ = ompl::base::Cost(0.0);
            costSampled_ = ompl::base::Cost(0.0);
            hasSolution_ = false;
            stopLoop_ = false;
            approximateSoln_ = false;
            approximateDiff_ = -1.0;
            numIterations_ = 0u;
            numSamples_ = 0u;
            numVertices_ = 0u;
            numFreeStatesPruned_ = 0u;
            numVerticesDisconnected_ = 0u;
            numStateCollisionChecks_ = 0u;
            numEdgeCollisionChecks_ = 0u;
            numNearestNeighbours_ = 0u;
            numEdgesProcessed_ = 0u;
            numRewirings_ = 0u;
            numBatches_ = 0u;
            numPrunings_ = 0u;

            //Mark as not setup:
            Planner::setup_ = false;

            //Call my base clear:
            Planner::clear();
        }



        ompl::base::PlannerStatus BITstar::solve(const ompl::base::PlannerTerminationCondition& ptc)
        {
            Planner::checkValidity();
            OMPL_INFORM("%s: Searching for a solution to the given planning problem.", Planner::getName().c_str());

            //Reset the manual stop to the iteration loop:
            stopLoop_ = false;

            //If we don't have a goal yet, recall updateStartAndGoalStates, but wait for the first goal (or until the PTC comes true and we give up):
            if (goalVertices_.empty() == true)
            {
                this->updateStartAndGoalStates(ptc);
            }

            //Run the outerloop until we're stopped, a suitable cost is found, or until we find the minimum possible cost within tolerance:
            while (opt_->isSatisfied(bestCost_) == false && ptc == false && (opt_->isCostBetterThan(minCost_, bestCost_) == true || Planner::pis_.haveMoreStartStates() == true || Planner::pis_.haveMoreGoalStates() == true) && stopLoop_ == false)
            {
                this->iterate();
            }

            if (hasSolution_ == true)
            {
                this->endSuccessMessage();

                this->publishSolution();
            }
            else
            {
                this->endFailureMessage();
            }

            //PlannerStatus(addedSolution, approximate)
            return ompl::base::PlannerStatus(hasSolution_, approximateSoln_);
        }



        void BITstar::getPlannerData(ompl::base::PlannerData& data) const
        {
            //Get the base planner class data:
            Planner::getPlannerData(data);

            //Add samples
            if (freeStateNN_)
            {
                //Variables:
                //The list of unused samples:
                std::vector<VertexPtr> samples;

                //Get the list of samples
                freeStateNN_->list(samples);

                //Iterate through it turning each into a disconnected vertex
                for (std::vector<VertexPtr>::const_iterator sIter = samples.begin(); sIter != samples.end(); ++sIter)
                {
                    //No, add as a regular vertex:
                    data.addVertex(ompl::base::PlannerDataVertex((*sIter)->stateConst()));
                }
            }
            //No else.

            //Add vertices
            if (vertexNN_)
            {
                //Variables:
                //The list of vertices in the graph:
                std::vector<VertexPtr> vertices;

                //Get the list of vertices
                vertexNN_->list(vertices);

                //Iterate through it turning each into a vertex with an edge:
                for (std::vector<VertexPtr>::const_iterator vIter = vertices.begin(); vIter != vertices.end(); ++vIter)
                {
                    //Is the vertex the start?
                    if ((*vIter)->isRoot() == true)
                    {
                        //Yes, add as a start vertex:
                        data.addStartVertex(ompl::base::PlannerDataVertex((*vIter)->stateConst()));
                    }
                    else
                    {
                        //No, add as a regular vertex:
                        data.addVertex(ompl::base::PlannerDataVertex((*vIter)->stateConst()));

                        //And as an incoming edge
                        data.addEdge(ompl::base::PlannerDataVertex((*vIter)->getParentConst()->stateConst()), ompl::base::PlannerDataVertex((*vIter)->stateConst()));
                    }
                }
            }
            //No else.

            //Did we find a solution?
            if (hasSolution_ == true)
            {
                data.markGoalState(curGoalVertex_->stateConst());
            }
        }



        std::pair<ompl::base::State const*, ompl::base::State const*> BITstar::getNextEdgeInQueue()
        {
            //Variable:
            //The next edge as a basic pair of states
            std::pair<ompl::base::State const*, ompl::base::State const*> nextEdge;

            //If we're using strict queue ordering, make sure the queue is up to date
            if(useStrictQueueOrdering_ == true)
            {
                //Resort the queues as necessary (if the graph has been rewired).
                this->resort();
            }

            if (intQueue_->isEmpty() == false)
            {
                //The next edge in the queue:
                nextEdge = std::make_pair(intQueue_->frontEdge().first->state(), intQueue_->frontEdge().second->state());
            }
            else
            {
                //An empty edge:
                nextEdge = std::make_pair<ompl::base::State*, ompl::base::State*>(NULL, NULL);
            }

            return nextEdge;
        }



        ompl::base::Cost BITstar::getNextEdgeValueInQueue()
        {
            //Variable
            //The cost of the next edge
            ompl::base::Cost nextCost;

            //If we're using strict queue ordering, make sure the queue is up to date
            if(useStrictQueueOrdering_ == true)
            {
                //Resort the queues as necessary (if the graph has been rewired).
                this->resort();
            }

            if (intQueue_->isEmpty() == false)
            {
                //The next cost in the queue:
                nextCost = intQueue_->frontEdgeValue().first;
            }
            else
            {
                //An infinite cost:
                nextCost =  opt_->infiniteCost();
            }

            return nextCost;
        }



        void BITstar::getEdgeQueue(std::vector<std::pair<VertexConstPtr, VertexConstPtr> >* edgesInQueue)
        {
            intQueue_->listEdges(edgesInQueue);
        }



        void BITstar::getVertexQueue(std::vector<VertexConstPtr>* verticesInQueue)
        {
            intQueue_->listVertices(verticesInQueue);
        }



        template<template<typename T> class NN>
        void BITstar::setNearestNeighbors()
        {
            //Check if the problem is already setup, if so, the NN structs have data in them and you can't really change them:
            if (Planner::setup_ == true)
            {
                throw ompl::Exception("The type of nearest neighbour datastructure cannot be changed once a planner is setup. ");
            }
            else
            {
                //The problem isn't setup yet, create NN structs of the specified type:
                freeStateNN_ = boost::make_shared< NN<VertexPtr> >();
                vertexNN_ = boost::make_shared< NN<VertexPtr> >();
            }
        }
        /////////////////////////////////////////////////////////////////////////////////////////////



        /////////////////////////////////////////////////////////////////////////////////////////////
        //Protected functions:
        void BITstar::estimateMeasures()
        {
            OMPL_INFORM("%s: Estimating the measure of the planning domain. This is a debugging function that does not have any effect on the planner.", Planner::getName().c_str());
            //Variables:
            //The total number of samples:
            unsigned int numTotalSamples;
            //The resulting samples in free:
            unsigned int numFreeSamples;
            //The resulting samples in obs:
            unsigned int numObsSamples;
            //The sample fraction of free:
            double fractionFree;
            //The sample fraction of obs:
            double fractionObs;
            //The total measure of the space:
            double totalMeasure;
            //The resulting estimate of the free measure
            double freeMeasure;
            //The resulting estimate of the obs measure
            double obsMeasure;

            //Set the total number of samples
            numTotalSamples = 100000u;
            numFreeSamples = 0u;
            numObsSamples = 0u;

            //Draw samples, classifying each one
            for (unsigned int i = 0u; i < numTotalSamples; ++i)
            {
                //Allocate a state
                ompl::base::State* aState = Planner::si_->allocState();

                //Sample:
                sampler_->sampleUniform(aState, bestCost_);

                //Check if collision free
                if (Planner::si_->isValid(aState) == true)
                {
                    ++numFreeSamples;
                }
                else
                {
                    ++numObsSamples;
                }
            }

            //Calculate the fractions:
            fractionFree = static_cast<double>(numFreeSamples)/static_cast<double>(numTotalSamples);

            fractionObs = static_cast<double>(numObsSamples)/static_cast<double>(numTotalSamples);

            //Get the total measure of the space
            totalMeasure = Planner::si_->getSpaceMeasure();

            //Calculate the measure of the free space
            freeMeasure = fractionFree*totalMeasure;

            //Calculate the measure of the obs space
            obsMeasure = fractionObs*totalMeasure;

            //Announce
            OMPL_INFORM("%s: %u samples (%u free, %u in collision) from a space with measure %.4f estimates %.2f%% free and %.2f%% in collision (measures of %.4f and %.4f, respectively).", Planner::getName().c_str(), numTotalSamples, numFreeSamples, numObsSamples, totalMeasure, 100.0*fractionFree, 100.0*fractionObs, freeMeasure, obsMeasure);
        }


        void BITstar::iterate()
        {
            //Info:
            ++numIterations_;

            //If we're using strict queue ordering, make sure the queues are up to date
            if(useStrictQueueOrdering_ == true)
            {
                //The queues will be resorted if the graph has been rewired.
                this->resort();
            }

            //Is the edge queue empty
            if (intQueue_->isEmpty() == true)
            {
                //Is it also unsorted?
                if (intQueue_->isSorted() == false)
                {
                    //If it is, then we've hit a rare condition where we emptied it without having to sort it, so address that
                    this->resort();
                }
                else
                {
                    //If not, then we're either just starting the problem, or just finished a batch. Either way, make a batch of samples and fill the queue for the first time:
                    this->newBatch();
                }
            }
            else
            {
                //If the edge queue is not empty, then there is work to do!

                //Variables:
                //The current edge:
                VertexPtrPair bestEdge;

                //Pop the minimum edge
                ++numEdgesProcessed_;
                intQueue_->popFrontEdge(&bestEdge);

                //In the best case, can this edge improve our solution given the current graph?
                //g_t(v) + c_hat(v,x) + h_hat(x) < g_t(x_g)
                if (opt_->isCostBetterThan( this->combineCosts(bestEdge.first->getCost(), this->edgeCostHeuristic(bestEdge), this->costToGoHeuristic(bestEdge.second)), bestCost_ ) == true)
                {
                    //Variables:
                    //The true cost of the edge:
                    ompl::base::Cost trueEdgeCost;

                    //Get the true cost of the edge
                    trueEdgeCost = this->trueEdgeCost(bestEdge);

                    //Can this actual edge ever improve our solution?
                    //g_hat(v) + c(v,x) + h_hat(x) < g_t(x_g)
                    if (opt_->isCostBetterThan( this->combineCosts(this->costToComeHeuristic(bestEdge.first), trueEdgeCost, this->costToGoHeuristic(bestEdge.second)),  bestCost_ ) == true)
                    {
                        //Does this edge have a collision?
                        if (this->checkEdge(bestEdge) == true)
                        {
                            //Does the current edge improve our graph?
                            //g_t(v) + c(v,x) < g_t(x)
                            if (opt_->isCostBetterThan( opt_->combineCosts(bestEdge.first->getCost(), trueEdgeCost), bestEdge.second->getCost() ) == true)
                            {
                                //YAAAAH. Add the edge! Allowing for the sample to be removed from free if it is not currently connected and otherwise propagate cost updates to descendants.
                                //addEdge will update the queue and handle the extra work that occurs if this edge improves the solution.
                                this->addEdge(bestEdge, trueEdgeCost, true, true);

                                //Prune the edge queue of any unnecessary incoming edges
                                intQueue_->pruneEdgesTo(bestEdge.second);

                                //We will only prune the whole graph/samples on a new batch.
                            }
                            //No else, this edge may be useful at some later date.
                        }
                        else if (useFailureTracking_ == true)
                        {
                            //If the edge failed, and we're tracking failures, record.
                            //This edge has a collision and can never be helpful. Poor edge. Add the target to the list of failed children for the source:
                            bestEdge.first->markAsFailedChild(bestEdge.second);
                        }
                        //No else, we failed and we're not tracking those
                    }
                    else if (useFailureTracking_ == true)
                    {
                        //If the edge failed, and we're tracking failures, record.
                        //This edge either has a very high edge cost and can never be helpful. Poor edge. Add the target to the list of failed children for the source
                        bestEdge.first->markAsFailedChild(bestEdge.second);
                    }
                    //No else, we failed and we're not tracking those
                }
                else if (intQueue_->isSorted() == false)
                {
                    //The edge cannot improve our solution, but the queue is imperfectly sorted, so we must resort before we give up.
                    this->resort();
                }
                else
                {
                    //Else, I cannot improve the current solution, and as the queue is perfectly sorted and I am the best edge, no one can improve the current solution . Give up on the batch:
                    intQueue_->finish();
                }
            } //Integrated queue not empty.
        }



        void BITstar::newBatch()
        {
            //Info:
            ++numBatches_;

            //Reset the queue:
            intQueue_->reset();

            //Do we need to update our starts or goals?
            if (Planner::pis_.haveMoreStartStates() == true || Planner::pis_.haveMoreGoalStates() == true)
            {
                //There are new starts/goals to get.
                this->updateStartAndGoalStates(ompl::base::plannerAlwaysTerminatingCondition());
            }
            //No else, we have enough of a problem to do some work, and everything's up to date.

            //Prune the graph (if enabled)
            this->prune();

            //Set the cost sampled to the minimum
            costSampled_ = minCost_;

            //Finally, update the nearest-neighbour terms for the number of samples we *will* have.
            this->updateNearestTerms();
        }



        void BITstar::updateSamples(const VertexPtr& vertex)
        {
            //Variable
            //The required cost to contain the neighbourhood of this vertex:
            ompl::base::Cost costReqd = neighbourhoodCost(vertex);

            //Check if we need to generate new samples inorder to completely cover the neighbourhood of the vertex
            if (opt_->isCostBetterThan(costSampled_, costReqd))
            {
                //Variable
                //The total number of samples we wish to have.
                unsigned int totalReqdSamples;

                //Get the measure of what we're sampling
                if (useJustInTimeSampling_ == true)
                {
                    //Variables
                    //The sample density for this slice of the problem.
                    double sampleDensity;
                    //The resulting number of samples needed for this slice as a *double*
                    double dblNum;

                    //Calculate the sample density given the number of samples per batch and the measure of this batch by assuming that this batch will fill the same measure as the previous
                    sampleDensity = static_cast<double>(samplesPerBatch_)/prunedMeasure_;

                    //Convert that into the number of samples needed for this slice.
                    dblNum = sampleDensity * sampler_->getInformedMeasure(costSampled_, costReqd);

                    //The integer of the double are definitely sampled
                    totalReqdSamples = numSamples_ + static_cast<unsigned int>(dblNum);

                    //And the fractional part represents the probability of one more sample. I like being pedantic.
                    if (rng_.uniform01() <= (dblNum - static_cast<double>(totalReqdSamples)))
                    {
                        //One more please
                        ++totalReqdSamples;
                    }
                    //No else.
                }
                else
                {
                    //We're generating all our samples in one batch. Do it to it.
                    totalReqdSamples = numSamples_ + samplesPerBatch_;
                }

                //Actually generate the new samples
                while (numSamples_ < totalReqdSamples)
                {
                    //Variable
                    //The new state:
                    VertexPtr newState = boost::make_shared<Vertex>(Planner::si_, opt_);

                    //Sample in the interval [costSampled_, costReqd):
                    sampler_->sampleUniform(newState->state(), costSampled_, costReqd);

                    //If the state is collision free, add it to the list of free states
                    ++numStateCollisionChecks_;
                    if (Planner::si_->isValid(newState->stateConst()) == true)
                    {
                        //Add the new state as a sample
                        this->addSample(newState);

                        //Update the number of uniformly distributed states
                        ++numUniformStates_;

                        //Update the number of sample
                        ++numSamples_;
                    }
                    //No else
                }

                //Mark that we've sampled all cost spaces (This is in preparation for JIT sampling)
                costSampled_ = costReqd;
            }
            //No else, the samples are up to date
        }



        bool BITstar::prune()
        {
            //Variable:
            //Whether or not we pruned, start as unpruned
            bool vertexPruned = false;

            //Test if we should we do a little tidying up:
            //Is pruning enabled? Do we have a solution? Has the solution changed enough?
            if ( (usePruning_ == true) && (hasSolution_ == true) && (std::abs(this->fractionalChange(bestCost_, prunedCost_)) > pruneFraction_) )
            {
                //Variables:
                //The current measure of the problem space:
                double informedMeasure;

                informedMeasure = sampler_->getInformedMeasure(bestCost_);

                //Is there good reason to prune? I.e., is the informed subset measurably less than the total problem domain? If an informed measure is not available, we'll assume yes:
                if ( (sampler_->hasInformedMeasure() == true && informedMeasure < si_->getSpaceMeasure()) || (sampler_->hasInformedMeasure() == false) )
                {
                    //Variable:
                    //The number of vertices and samples pruned
                    std::pair<unsigned int, unsigned int> numPruned;

                    OMPL_INFORM("%s: Pruning the planning problem from a solution of %.4f to %.4f, resulting in a change of problem size from %.4f to %.4f.", Planner::getName().c_str(), prunedCost_.value(), bestCost_.value(), prunedMeasure_, informedMeasure);

                    //Increment the pruning counter:
                    ++numPrunings_;

                    //First, prune the starts/goals:
                    this->pruneStartsGoals();

                    //Prune the samples
                    this->pruneSamples();

                    //Prune the graph. This can be done extra efficiently by using some info in the integrated queue.
                    //This requires access to the nearest neighbour structures so vertices can be moved to free states.s
                    numPruned = intQueue_->prune(curGoalVertex_, vertexNN_, freeStateNN_);

                    //The number of vertices and samples pruned are incrementally updated.
                    numVerticesDisconnected_ = numVerticesDisconnected_ + numPruned.first;
                    numFreeStatesPruned_ = numFreeStatesPruned_ + numPruned.second;

                    //Store the cost at which we pruned:
                    prunedCost_ = bestCost_;

                    //And the measure:
                    prunedMeasure_ = informedMeasure;

                    //Check if any states have actually been pruned
                    vertexPruned = (numPruned.second > 0u);
                }
                //No else, it's not worth the work to prune...
            }
            //No else, why was I called?

            return vertexPruned;
        }



        bool BITstar::resort()
        {
            //Variable:
            //The number of vertices and samples pruned
            std::pair<unsigned int, unsigned int> numPruned;

            //Resorting requires access to the nearest neighbour structures so vertices can be pruned instead of resorted.
            //The number of vertices pruned is also incrementally updated.
            numPruned = intQueue_->resort(vertexNN_, freeStateNN_);

            //The number of vertices and samples pruned are incrementally updated.
            numVerticesDisconnected_ = numVerticesDisconnected_ + numPruned.first;
            numFreeStatesPruned_ = numFreeStatesPruned_ + numPruned.second;

            return (numPruned.second > 0u);
        }



        void BITstar::publishSolution()
        {
            //Variable
            //The path geometric
            boost::shared_ptr<ompl::geometric::PathGeometric> pathGeoPtr;
            //The reverse path of state pointers
            std::vector<const ompl::base::State*> reversePath;

            //Allocate the pathGeoPtr
            pathGeoPtr = boost::make_shared<ompl::geometric::PathGeometric>(Planner::si_);

            //Get the reversed path
            reversePath = this->bestPathFromGoalToStart();

            //Now iterate that vector in reverse, putting the states into the path geometric
            for (std::vector<const ompl::base::State*>::const_reverse_iterator sIter = reversePath.rbegin(); sIter != reversePath.rend(); ++sIter)
            {
                pathGeoPtr->append(*sIter);
            }

            //Now create the solution
            ompl::base::PlannerSolution soln(pathGeoPtr);

            //Mark the name:
            soln.setPlannerName(Planner::getName());

            //Mark as exact or approximate:
            if (approximateSoln_ == true)
            {
                soln.setApproximate(approximateDiff_);
            }

            //Mark whether the solution met the optimization objective:
            soln.optimized_ = opt_->isSatisfied(bestCost_);

            //Add the solution to the Problem Definition:
            Planner::pdef_->addSolutionPath(soln);
        }



        std::vector<const ompl::base::State*> BITstar::bestPathFromGoalToStart() const
        {
            //A vector of states from goal->start:
            std::vector<const ompl::base::State*> reversePath;

            //Iterate up the chain from the goal, creating a backwards vector:
            reversePath.push_back(curGoalVertex_->stateConst());

            //Then, use a vertex pointer like an iterator. Starting at the goal, we iterate up the chain pushing the *parent* of the iterator into the vector until the vertex has no parent.
            //This will allows us to add the start (as the parent of the first child) and then stop when we get to the start itself, avoiding trying to find its nonexistent child
            for (VertexConstPtr curVertex = curGoalVertex_; curVertex->isRoot() == false; curVertex = curVertex->getParentConst())
            {
                //Check the case where the chain ends incorrectly. This is unnecessary but sure helpful in debugging:
                if (curVertex->hasParent() == false)
                {
                    throw ompl::Exception("The path to the goal does not originate at a start state. Something went wrong.");
                }

                //Push back the parent into the vector as a state pointer:
                reversePath.push_back(curVertex->getParentConst()->stateConst());
            }
            return reversePath;
        }



        void BITstar::updateStartAndGoalStates(const base::PlannerTerminationCondition& ptc)
        {
            //Variable
            //Whether we've added a start or goal:
            bool addedGoal = false;
            bool addedStart = false;
            //Whether we have to rebuid the queue, i.e.. whether we've called updateStartAndGoalStates before
            bool rebuildQueue = false;

            //Add the new starts and goals to the lists of said vertices.
            //Do goals first, as they are only added as samples.
            //We do this as nested conditions so we always call nextGoal(ptc) at least once (regardless of whether there are moreGoalStates or not)
            //in case we have been given a non trivial PTC that wants us to wait, but do *not* call it again if there are no more goals
            //(as in the nontrivial PTC case, doing so would cause us to wait out the ptc and never try to solve anything)
            do
            {
                //Variable
                //A new goal pointer, if there are none, it will be NULL.
                //We will wait for the duration of PTC for a new goal to appear.
                const ompl::base::State* newGoal = Planner::pis_.nextGoal(ptc);

                //Check if it's valid
                if (static_cast<bool>(newGoal) == true)
                {
                    //It is valid and we are adding a goal, we will need to rebuild the queue if any starts have previously been added as their (and any descendents') heuristic cost-to-go may change:
                    rebuildQueue = (startVertices_.size() > 0u);

                    //Allocate the vertex pointer
                    goalVertices_.push_back(boost::make_shared<Vertex>(Planner::si_, opt_));

                    //Copy the value into the state
                    Planner::si_->copyState(goalVertices_.back()->state(), newGoal);

                    //And add this goal to the set of samples:
                    this->addSample(goalVertices_.back());

                    //Mark that we've added:
                    addedGoal = true;
                }
                //No else, there was no goal.
            }
            while (Planner::pis_.haveMoreGoalStates() == true);

            //And then do the for starts. We do this last as the starts are added to the queue, which uses a cost-to-go heuristic in it's ordering, and for that we want all the goals updated.
            //As there is no way to wait for new *start* states, this loop can be cleaner
            //There is no need to rebuild the queue when we add start vertices, as the queue is ordered on current cost-to-come, and adding a start doesn't change that.
            while (Planner::pis_.haveMoreStartStates() == true)
            {
                //Variable
                //A new start pointer
                const ompl::base::State* newStart = Planner::pis_.nextStart();

                //Allocate the vertex pointer:
                startVertices_.push_back(boost::make_shared<Vertex>(Planner::si_, opt_, true));

                //Copy the value into the state:
                Planner::si_->copyState(startVertices_.back()->state(), newStart);

                //Add this start to the queue. It is not a sample, so skip that step:
                this->addVertex(startVertices_.back(), false);

                //Mark that we've added:
                addedStart = true;
            }

            //Now, if we added a new start and have previously pruned goals, we may want to readd them.
            if (addedStart == true && prunedGoalVertices_.empty() == false)
            {
                //Variable
                //An iterator to the list of pruned goals
                std::list<VertexPtr>::iterator pgIter = prunedGoalVertices_.begin();

                //Consider each one
                while (pgIter != prunedGoalVertices_.end())
                {
                    //Mark as unpruned
                    (*pgIter)->markUnpruned();

                    //Check if it should be readded (i.e., would it be pruned *now*?)
                    if (intQueue_->vertexPruneCondition(*pgIter)  == true)
                    {
                        //It would be pruned, so remark as pruned
                        (*pgIter)->markPruned();

                        //and move onto the next:
                        ++pgIter;
                    }
                    else
                    {
                        //It would not be pruned now, so readd it!
                        //Add back to the list:
                        goalVertices_.push_back(*pgIter);

                        //Add as a sample
                        this->addSample(*pgIter);

                        //Mark what we've added:
                        addedGoal = true;

                        //Remove the start from the list, this returns the next iterator
                        pgIter = prunedGoalVertices_.erase(pgIter);

                        //Just like the other new goals, we will need to rebuild the queue if any starts have previously been added. Which was a condition to be here in the first place
                        rebuildQueue = true;
                    }
                }
            }

            //Now, if we added a goal and have previously pruned starts, we will have to do the same on those
            if (addedGoal == true && prunedStartVertices_.empty() == false)
            {
                //Variable
                //An iterator to the list of pruned starts
                std::list<VertexPtr>::iterator psIter = prunedStartVertices_.begin();

                //Consider each one
                while (psIter != prunedStartVertices_.end())
                {
                    //Mark as unpruned
                    (*psIter)->markUnpruned();

                    //Check if it should be readded (i.e., would it be pruned *now*?)
                    if (intQueue_->vertexPruneCondition(*psIter)  == true)
                    {
                        //It would be pruned, so remark as pruned
                        (*psIter)->markPruned();

                        //and move onto the next:
                        ++psIter;
                    }
                    else
                    {
                        //It would not be pruned, readd it!
                        //Add it back to the list
                        startVertices_.push_back(*psIter);

                        //Add to the queue as a vertex. It is not a sample, so skip that step:
                        this->addVertex(*psIter, false);

                        //Mark what we've added:
                        addedStart = true;

                        //Remove the start from the list, this returns the next iterator
                        psIter = prunedStartVertices_.erase(psIter);
                    }
                }
            }

            //If we've added a state, we have some updating to do.
            if (addedGoal == true || addedStart == true)
            {
                //Update the minimum cost
                for (std::list<VertexPtr>::const_iterator sIter = startVertices_.begin(); sIter != startVertices_.end(); ++sIter)
                {
                    //Take the better of the min cost so far and the cost-to-go from this start
                    minCost_ = opt_->betterCost(minCost_, this->costToGoHeuristic(*sIter));
                }

                //If we have at least one start and goal, allocate a sampler
                if (startVertices_.size() > 0u && goalVertices_.size() > 0u)
                {
                    //There is a start and goal, allocate
                    sampler_ = opt_->allocInformedStateSampler(Planner::pdef_, std::numeric_limits<unsigned int>::max());
                }
                //No else, this will get allocated when we get the updated start/goal.

                //Was there an existing queue that needs to be rebuilt?
                if (rebuildQueue == true)
                {
                    //There was, inform
                    OMPL_INFORM("%s: Updating starts/goals and rebuilding the queue.", Planner::getName().c_str());

                    //Flag the queue as unsorted downstream from every existing start.
                    for (std::list<VertexPtr>::const_iterator sIter = startVertices_.begin(); sIter != startVertices_.end(); ++sIter)
                    {
                        intQueue_->markVertexUnsorted(*sIter);
                    }

                    //Resort the queue.
                    this->resort();
                }
                //No else
            }
            //No else, why were we called?

            //Make sure that if we have a goal, we also have a start, since there's no way to wait for more *starts*
            if (goalVertices_.empty() == false && startVertices_.empty() == true)
            {
                OMPL_WARN("%s, The problem has a goal but not a start. As PlannerInputStates provides no method to wait for a _start_ state, this will likely be problematic.", Planner::getName().c_str());
            }
            //No else
        }



        void BITstar::pruneStartsGoals()
        {
            //Are there superfluous starts to prune?
            if (startVertices_.size() > 1u)
            {
                //Yes, Iterate through the list

                //Variable
                //The iterator to the start:
                std::list<VertexPtr>::iterator startIter = startVertices_.begin();

                //Run until at the end:
                while (startIter != startVertices_.end())
                {
                    //Check if this start has met the criteria to be pruned
                    if (intQueue_->vertexPruneCondition(*startIter)  == true)
                    {
                        //It has, update counters. By definition of the heuristics, start vertices are either in the tree or are deleted. Since we're pruning this one, that means we're going all the way to remove it:
                        ++numVerticesDisconnected_;
                        ++numFreeStatesPruned_;

                        //Remove the start vertex completely from the queue, they don't have parents
                        intQueue_->eraseVertex(*startIter, false, vertexNN_, freeStateNN_);

                        //Store the start vertex in the pruned list, in case it later needs to be readded:
                        prunedStartVertices_.push_back(*startIter);

                        //Remove from the list, this returns the next iterator
                        startIter = startVertices_.erase(startIter);
                    }
                    else
                    {
                        //Still valid, move to the next one:
                        ++startIter;
                    }
                }
            }
            //No else, can't prune 1 start.

            //Are there superfluous goals to prune?
            if (goalVertices_.size() > 1u)
            {
                //Yes, Iterate through the list

                //Variable
                //The iterator to the start:
                std::list<VertexPtr>::iterator goalIter = goalVertices_.begin();

                //Run until at the end:
                while (goalIter != goalVertices_.end())
                {
                    //Check if this start has met the criteria to be pruned
                    if (intQueue_->vertexPruneCondition(*goalIter)  == true)
                    {
                        //It has, remove the goal vertex completely
                        //Check if this vertex is in the tree
                        if ((*goalIter)->isInTree() == true)
                        {
                            //It is, increment the counters
                            ++numVerticesDisconnected_;
                            ++numFreeStatesPruned_;

                            //And erase it from the queue:
                            intQueue_->eraseVertex(*goalIter, (*goalIter)->hasParent(), vertexNN_, freeStateNN_);

                            //Store the start vertex in the pruned list, in case it later needs to be readded:
                            prunedGoalVertices_.push_back(*goalIter);

                            //Remove from the list, this returns the next iterator
                            goalIter = goalVertices_.erase(goalIter);
                        }
                        else
                        {
                            //It is not, so we just delete it like a sample
                            this->dropSample(*goalIter);

                            //Remove from the list, this returns the next iterator
                            goalIter = goalVertices_.erase(goalIter);
                        }
                    }
                    else
                    {
                        //The goal is still valid, get the next
                        ++goalIter;
                    }
                }
            }
            //No else, can't prune 1 goal.
        }



        void BITstar::pruneSamples()
        {
            //Are we dropping samples anytime we prune?
            if (dropSamplesOnPrune_ == true)
            {
                //We are, update the pruned counter
                numFreeStatesPruned_ = numFreeStatesPruned_ + freeStateNN_->size();

                //and the number of uniform samples
                numUniformStates_ = 0u;

                //Then remove all of the samples
                freeStateNN_->clear();
            }
            else
            {
                //Variable:
                //The list of samples:
                std::vector<VertexPtr> samples;

                //Get the list of samples
                freeStateNN_->list(samples);

                //Iterate through the list and remove any samples that have a heuristic larger than the bestCost_
                for (unsigned int i = 0u; i < samples.size(); ++i)
                {
                    //Check if this state should be pruned:
                    if (intQueue_->samplePruneCondition(samples.at(i)) == true)
                    {
                        //Yes, remove it
                        this->dropSample(samples.at(i));
                    }
                    //No else, keep.
                }
            }
        }



        bool BITstar::checkEdge(const VertexPtrPair& edge)
        {
            ++numEdgeCollisionChecks_;
            return Planner::si_->checkMotion(edge.first->state(), edge.second->state());
        }



        void BITstar::dropSample(VertexPtr oldSample)
        {
            //Update the counter:
            ++numFreeStatesPruned_;

            //Remove from the list of samples
            freeStateNN_->remove(oldSample);

            //Mark the sample as pruned
            oldSample->markPruned();
        }



        void BITstar::addEdge(const VertexPtrPair& newEdge, const ompl::base::Cost& edgeCost, const bool& removeFromFree, const bool& updateDescendants)
        {
            if (newEdge.first->isInTree() == false)
            {
                throw ompl::Exception("Adding an edge from a vertex not connected to the graph");
            }

            //This should be a debug-level-only assert some day:
            /*
            if (opt_->isCostEquivalentTo(this->trueEdgeCost(newEdge), edgeCost) == false)
            {
                throw ompl::Exception("You have passed the wrong edge cost to addEdge.");
            }
            */

            //If the vertex is currently in the tree, we need to rewire
            if (newEdge.second->hasParent() == true)
            {
                //Replace the edge
                this->replaceParent(newEdge, edgeCost, updateDescendants);
            }
            else
            {
                //If not, we just add the vertex, first connect:

                //Add a child to the parent, not updating costs:
                newEdge.first->addChild(newEdge.second, false);

                //Add a parent to the child, updating descendant costs if requested:
                newEdge.second->addParent(newEdge.first, edgeCost, updateDescendants);

                //Then add to the queues as necessary
                this->addVertex(newEdge.second, removeFromFree);
            }

            //If the path to the goal has changed, we may need to update the cached info about the solution cost or solution length:
            this->updateGoalVertex();
        }



        void BITstar::replaceParent(const VertexPtrPair& newEdge, const ompl::base::Cost& edgeCost, const bool& updateDescendants)
        {
            if (newEdge.second->getParent() == newEdge.first)
            {
                throw ompl::Exception("The new and old parents of the given rewiring are the same.");
            }

            //This would be a good debug-level-only assert
            if (opt_->isCostBetterThan(newEdge.second->getCost(), opt_->combineCosts(newEdge.first->getCost(), edgeCost)) == true)
            {
                throw ompl::Exception("The new edge will increase the cost-to-come of the vertex!");
            }

            //Increment our counter:
            ++numRewirings_;

            //Remove the child from the parent, not updating costs
            newEdge.second->getParent()->removeChild(newEdge.second, false);

            //Remove the parent from the child, not updating costs
            newEdge.second->removeParent(false);

            //Add the child to the parent, not updating costs
            newEdge.first->addChild(newEdge.second, false);

            //Add the parent to the child. This updates the cost of the child as well as all it's descendents (if requested).
            newEdge.second->addParent(newEdge.first, edgeCost, updateDescendants);

            //Mark the queues as unsorted below this child
            intQueue_->markVertexUnsorted(newEdge.second);
        }



        void BITstar::updateGoalVertex()
        {
            //Variable
            //Whether we've updated the goal, be pessimistic.
            bool goalUpdated = false;
            //The the new goal, start with the current goal
            VertexPtr newBestGoal = curGoalVertex_;
            //The new cost, start as the current bestCost_
            ompl::base::Cost newCost = bestCost_;

            //Iterate through the list of goals, and see if the solution has changed
            for (std::list<VertexPtr>::const_iterator gIter = goalVertices_.begin(); gIter != goalVertices_.end(); ++gIter)
            {
                //First, is this goal even in the tree?
                if ((*gIter)->isInTree() == true)
                {
                    //Next, is there currently a solution?
                    if (static_cast<bool>(newBestGoal) == true)
                    {
                        //There is already a solution, is it to to this goal?
                        if (*gIter == newBestGoal)
                        {
                            //Ah-ha, We meet again! Are we doing any better? We check the length as sometimes the path length changes with minimal change in cost.
                            if (opt_->isCostEquivalentTo((*gIter)->getCost(), newCost) == false || ((*gIter)->getDepth() + 1u) != bestLength_)
                            {
                                //The path to the current best goal has changed, so we need to update it.
                                goalUpdated = true;
                                newBestGoal = *gIter;
                                newCost = newBestGoal->getCost();
                            }
                            //No else, no change
                        }
                        else
                        {
                            //It is not to this goal, we have a second solution! What an easy problem... but is it better?
                            if (opt_->isCostBetterThan((*gIter)->getCost(), newCost) == true)
                            {
                                //It is! Save this as a better goal:
                                goalUpdated = true;
                                newBestGoal = *gIter;
                                newCost = newBestGoal->getCost();
                            }
                            //No else, not a better solution
                        }
                    }
                    else
                    {
                        //There isn't a preexisting solution, that means that any goal is an update:
                        goalUpdated = true;
                        newBestGoal = *gIter;
                        newCost = newBestGoal->getCost();
                    }
                }
                //No else, can't be a better solution if it's not in the spanning tree, can it?
            }

            //Did we update the goal?
            if (goalUpdated == true)
            {
                //We have a better solution!
                if (hasSolution_ == false)
                {
                    approximateSoln_ = false;
                    approximateDiff_ = -1.0;
                }

                //Mark that we have a solution
                hasSolution_ = true;
                intQueue_->hasSolution();

                //Store the current goal
                curGoalVertex_ = newBestGoal;

                //Update the best cost:
                bestCost_ = newCost;

                //and best length
                bestLength_ = curGoalVertex_->getDepth() + 1u;

                //Update the queue threshold:
                intQueue_->setThreshold(bestCost_);

                //Stop the solution loop if enabled:
                stopLoop_ = stopOnSolnChange_ ;

                //Brag:
                this->goalMessage();

                //If enabled, pass the intermediate solution back through the call back:
                if (static_cast<bool>(Planner::pdef_->getIntermediateSolutionCallback()) == true)
                {
                    //The form of path passed to the intermediate solution callback is not well documented, but it *appears* that it's not supposed
                    //to include the start or goal; however, that makes no sense for multiple start/goal problems, so we're going to include it anyway (sorry).
                    //Similarly, it appears to be ordered as (goal, goal-1, goal-2,...start+1, start) which conveniently allows us to reuse code.
                    Planner::pdef_->getIntermediateSolutionCallback()(this, this->bestPathFromGoalToStart(), bestCost_);
                }
            }
            //No else, the goal didn't change
        }



        void BITstar::addSample(const VertexPtr& newSample)
        {
            //Mark as new
            newSample->markNew();

            //Add to the NN structure:
            freeStateNN_->add(newSample);
        }



        void BITstar::addVertex(const VertexPtr& newVertex, const bool& removeFromFree)
        {
            //Make sure it's connected first, so that the queue gets updated properly. This is a day of debugging I'll never get back
            if (newVertex->isInTree() == false)
            {
                throw ompl::Exception("Vertices must be connected to the graph before adding");
            }

            //Remove the vertex from the list of samples (if it even existed)
            if (removeFromFree == true)
            {
                freeStateNN_->remove(newVertex);
            }
            //No else

            //Add to the NN structure:
            vertexNN_->add(newVertex);

            //Add to the queue:
            intQueue_->insertVertex(newVertex);

            //Increment the number of vertices added:
            ++numVertices_;
        }



        void BITstar::nearestSamples(const VertexPtr& vertex, std::vector<VertexPtr>* neighbourSamples)
        {
            //Make sure sampling has happened first:
            this->updateSamples(vertex);

            //Increment our counter:
            ++numNearestNeighbours_;

            if (useKNearest_ == true)
            {
                freeStateNN_->nearestK(vertex, k_, *neighbourSamples);
            }
            else
            {
                freeStateNN_->nearestR(vertex, r_, *neighbourSamples);
            }
        }



        void BITstar::nearestVertices(const VertexPtr& vertex, std::vector<VertexPtr>* neighbourVertices)
        {
            //Increment our counter:
            ++numNearestNeighbours_;

            if (useKNearest_ == true)
            {
                vertexNN_->nearestK(vertex, k_, *neighbourVertices);
            }
            else
            {
                vertexNN_->nearestR(vertex, r_, *neighbourVertices);
            }
        }



        double BITstar::nnDistance(const VertexPtr& a, const VertexPtr& b) const
        {
            //Using RRTstar as an example, this order gives us the distance FROM the queried state TO the other neighbours in the structure.
            //The distance function between two states
            if (!a->state())
            {
                throw ompl::Exception("a->state is unallocated");
            }
            if (!b->state())
            {
                throw ompl::Exception("b->state is unallocated");
            }
            return Planner::si_->distance(b->state(), a->state());
        }



        ompl::base::Cost BITstar::lowerBoundHeuristicVertex(const VertexPtr& vertex) const
        {
            return opt_->combineCosts( this->costToComeHeuristic(vertex), this->costToGoHeuristic(vertex) );
        }



        ompl::base::Cost BITstar::currentHeuristicVertex(const VertexPtr& vertex) const
        {
            return opt_->combineCosts( vertex->getCost(), this->costToGoHeuristic(vertex) );
        }


        ompl::base::Cost BITstar::lowerBoundHeuristicEdge(const VertexPtrPair& edgePair) const
        {
            return this->combineCosts(this->costToComeHeuristic(edgePair.first), this->edgeCostHeuristic(edgePair), this->costToGoHeuristic(edgePair.second));
        }



        ompl::base::Cost BITstar::currentHeuristicEdge(const VertexPtrPair& edgePair) const
        {
            return opt_->combineCosts(this->currentHeuristicEdgeTarget(edgePair), this->costToGoHeuristic(edgePair.second));
        }



        ompl::base::Cost BITstar::currentHeuristicEdgeTarget(const VertexPtrPair& edgePair) const
        {
            return opt_->combineCosts(edgePair.first->getCost(), this->edgeCostHeuristic(edgePair));
        }



        ompl::base::Cost BITstar::costToComeHeuristic(const VertexPtr& vertex) const
        {
            //Variable
            //The current best cost to the state, initialize to infinity
            ompl::base::Cost curBest = opt_->infiniteCost();

            //Iterate over the list of starts, finding the minimum estimated cost-to-come to the state
            for (std::list<VertexPtr>::const_iterator startIter = startVertices_.begin(); startIter != startVertices_.end(); ++startIter)
            {
                //Update the cost-to-come as the better of the best so far and the new one
                curBest = opt_->betterCost(curBest, opt_->motionCostHeuristic((*startIter)->state(), vertex->state()));
            }

            //Return
            return curBest;
        }



        ompl::base::Cost BITstar::edgeCostHeuristic(const VertexPtrPair& edgePair) const
        {
            return opt_->motionCostHeuristic(edgePair.first->state(), edgePair.second->state());
        }



        ompl::base::Cost BITstar::costToGoHeuristic(const VertexPtr& vertex) const
        {
            //Variable
            //The current best cost to a goal from the state, initialize to infinity
            ompl::base::Cost curBest = opt_->infiniteCost();

            //Iterate over the list of goals, finding the minimum estimated cost-to-go from the state
            for (std::list<VertexPtr>::const_iterator goalIter = goalVertices_.begin(); goalIter != goalVertices_.end(); ++goalIter)
            {
                //Update the cost-to-go as the better of the best so far and the new one
                curBest = opt_->betterCost(curBest, opt_->motionCostHeuristic(vertex->state(), (*goalIter)->state()));
            }

            //Return
            return curBest;
        }


        ompl::base::Cost BITstar::trueEdgeCost(const VertexPtrPair& edgePair) const
        {
            return opt_->motionCost(edgePair.first->state(), edgePair.second->state());
        }



        ompl::base::Cost BITstar::neighbourhoodCost(const VertexPtr& vertex) const
        {
            if (useJustInTimeSampling_ == true)
            {
                return opt_->betterCost(prunedCost_, opt_->combineCosts(this->lowerBoundHeuristicVertex(vertex), ompl::base::Cost(2.0 * r_)));
            }
            else
            {
                return prunedCost_;
            }
        }



        bool BITstar::isCostWorseThan(const ompl::base::Cost& a, const ompl::base::Cost& b) const
        {
            //If b is better than a, then a is worse than b
            return opt_->isCostBetterThan(b, a);
        }



        bool BITstar::isCostNotEquivalentTo(const ompl::base::Cost& a, const ompl::base::Cost& b) const
        {
            //If a is better than b, or b is better than a, then they are not equal
            return opt_->isCostBetterThan(a,b) || opt_->isCostBetterThan(b,a);
        }



        bool BITstar::isCostBetterThanOrEquivalentTo(const ompl::base::Cost& a, const ompl::base::Cost& b) const
        {
            //If b is not better than a, then a is better than, or equal to, b
            return !opt_->isCostBetterThan(b, a);
        }



        bool BITstar::isCostWorseThanOrEquivalentTo(const ompl::base::Cost& a, const ompl::base::Cost& b) const
        {
            //If a is not better than b, than a is worse than, or equal to, b
            return !opt_->isCostBetterThan(a,b);
        }



        ompl::base::Cost BITstar::combineCosts(const ompl::base::Cost& a, const ompl::base::Cost& b, const ompl::base::Cost& c) const
        {
            return opt_->combineCosts(a, opt_->combineCosts(b, c));
        }



        ompl::base::Cost BITstar::combineCosts(const ompl::base::Cost& a, const ompl::base::Cost& b, const ompl::base::Cost& c, const ompl::base::Cost& d) const
        {
            return opt_->combineCosts(a, this->combineCosts(b, c, d));
        }



        double BITstar::fractionalChange(const ompl::base::Cost& newCost, const ompl::base::Cost& oldCost) const
        {
            return this->fractionalChange(newCost, oldCost, oldCost);
        }



        double BITstar::fractionalChange(const ompl::base::Cost& newCost, const ompl::base::Cost& oldCost, const ompl::base::Cost& refCost) const
        {
            //If the old cost is not finite, than we call that infinite percent improvement
            if (opt_->isFinite(oldCost) == false)
            {
                //Return infinity (but not beyond)
                return std::numeric_limits<double>::infinity();
            }
            else
            {
                //Calculate and return
                return ( newCost.value() - oldCost.value() )/refCost.value();
            }
        }



        void BITstar::initializeNearestTerms()
        {
            //Calculate the k-nearest constant
            k_rgg_ = this->minimumRggK();

            //Update the actual terms
            this->updateNearestTerms();
        }



        void BITstar::updateNearestTerms()
        {
            //Variables:
            //The number of uniformly distributed states:
            unsigned int N;

            //Calculate the number of N, are we dropping samples?
            if (dropSamplesOnPrune_ == true)
            {
                //We arre, so we've been tracking the number of uniform states, just us that
                N = numUniformStates_;
            }
            else
            {
                //We are not, so the all vertices and samples are uniform, less the starts and goals.
                N = vertexNN_->size() + freeStateNN_->size() - startVertices_.size() - goalVertices_.size();
            }

            //In general, we calculate the terms considering the future samples. This is only not the case when it's the initial call (i.e., the 0 batch):
            if (numBatches_ != 0u)
            {
                N = N + samplesPerBatch_;
            }
            //No else


            //If we only have starts and goals, be lazy
            if (N == 0u)
            {
                k_ = startVertices_.size() + goalVertices_.size();
                r_ = std::numeric_limits<double>::infinity();
            }
            else
            {
                if (useKNearest_ == true)
                {
                    k_ = this->calculateK(N);
                }
                else
                {
                    r_ = this->calculateR(N);
                }
            }
        }



        double BITstar::calculateR(unsigned int N) const
        {
            //Variables
            //The dimension cast as a double for readibility;
            double dimDbl = static_cast<double>(Planner::si_->getStateDimension());
            //The size of the graph
            double cardDbl = static_cast<double>(N);

            //Calculate the term and return
            return this->minimumRggR()*std::pow( std::log(cardDbl)/cardDbl, 1/dimDbl );
        }



        unsigned int BITstar::calculateK(unsigned int N) const
        {
            //Calculate the term and return
            return std::ceil( k_rgg_ * std::log(static_cast<double>(N)) );
        }



        double BITstar::minimumRggR() const
        {
            //Variables
            //The dimension cast as a double for readibility;
            double dimDbl = static_cast<double>(Planner::si_->getStateDimension());

            //Calculate the term and return
            return rewireFactor_*2.0*std::pow( (1.0 + 1.0/dimDbl)*( prunedMeasure_/unitNBallMeasure(Planner::si_->getStateDimension()) ), 1.0/dimDbl ); //RRG radius (biggest for unit-volume problem)
            //return rewireFactor_*std::pow( 2.0*(1.0 + 1.0/dimDbl)*( prunedMeasure_/unitNBallMeasure(Planner::si_->getStateDimension()) ), 1.0/dimDbl ); //RRT* radius (smaller for unit-volume problem)
            //return rewireFactor_*2.0*std::pow( (1.0/dimDbl)*( prunedMeasure_/unitNBallMeasure(Planner::si_->getStateDimension()) ), 1.0/dimDbl ); //FMT* radius (smallest for R2, equiv to RRT* for R3 and then middle for higher d. All unit-volume)
        }



        double BITstar::minimumRggK() const
        {
            //Variables
            //The dimension cast as a double for readibility;
            double dimDbl = static_cast<double>(Planner::si_->getStateDimension());

            //Calculate the term and return
            return rewireFactor_*(boost::math::constants::e<double>() + (boost::math::constants::e<double>() / dimDbl)); //RRG k-nearest
        }



        void BITstar::goalMessage() const
        {
            OMPL_INFORM("%s (%u iters): Found a solution of cost %.4f (%u vertices) from %u samples by processing %u edges (%u collision checked) to create %u vertices and perform %u rewirings. The graph currently has %u vertices.", Planner::getName().c_str(), numIterations_, bestCost_.value(), bestLength_, numSamples_, numEdgesProcessed_, numEdgeCollisionChecks_, numVertices_, numRewirings_, vertexNN_->size());
        }



        void BITstar::endSuccessMessage() const
        {
            OMPL_INFORM("%s: Finished with a solution of cost %.4f (%u vertices) found from %u samples by processing %u edges (%u collision checked) to create %u vertices and perform %u rewirings. The final graph has %u vertices.", Planner::getName().c_str(), bestCost_.value(), bestLength_, numSamples_, numEdgesProcessed_, numEdgeCollisionChecks_, numVertices_, numRewirings_, vertexNN_->size());
        }



        void BITstar::endFailureMessage() const
        {
            OMPL_INFORM("%s (%u iters): Did not find a solution from %u samples after processing %u edges (%u collision checked) to create %u vertices and perform %u rewirings. The final graph has %u vertices.", Planner::getName().c_str(), numIterations_, numSamples_, numEdgesProcessed_, numEdgeCollisionChecks_, numVertices_, numRewirings_, vertexNN_->size());
        }



        void BITstar::statusMessage(const ompl::msg::LogLevel& msgLevel, const std::string& status) const
        {
            //Check if we need to create the message
            if (msgLevel >= ompl::msg::getLogLevel())
            {
                //Variable
                //The message as a stream:
                std::stringstream outputStream;

                //Create the stream:
                //The name of the planner
                outputStream << Planner::getName();
                outputStream << " (";
                //The current path cost:
                outputStream << "l: " << std::setw(6) << std::setfill(' ') << std::setprecision(5) << bestCost_.value();
                //The number of batches:
                outputStream << ", b: " << std::setw(5) << std::setfill(' ') << numBatches_;
                //The number of iterations
                outputStream << ", i: " << std::setw(5) << std::setfill(' ') << numIterations_;
                //The number of states current in the graph
                outputStream << ", g: " << std::setw(5) << std::setfill(' ') << vertexNN_->size();
                //The number of free states
                outputStream << ", f: " << std::setw(5) << std::setfill(' ') << freeStateNN_->size();
                //The number edges in the queue:
                outputStream << ", q: " << std::setw(5) << std::setfill(' ') << intQueue_->numEdges();
                //The total number of edges taken out of the queue:
                outputStream << ", t: " << std::setw(5) << std::setfill(' ') << numEdgesProcessed_;
                //The number of samples generated
                outputStream << ", s: " << std::setw(5) << std::setfill(' ') << numSamples_;
                //The number of vertices ever added to the graph:
                outputStream << ", v: " << std::setw(5) << std::setfill(' ') << numVertices_;
                //The number of prunings:
                outputStream << ", p: " << std::setw(5) << std::setfill(' ') << numPrunings_;
                //The number of rewirings:
                outputStream << ", r: " << std::setw(5) << std::setfill(' ') << numRewirings_;
                //The number of nearest-neighbour calls
                outputStream << ", n: " << std::setw(5) << std::setfill(' ') << numNearestNeighbours_;
                //The number of state collision checks:
                outputStream << ", c(s): " << std::setw(5) << std::setfill(' ') << numStateCollisionChecks_;
                //The number of edge collision checks:
                outputStream << ", c(e): " << std::setw(5) << std::setfill(' ') << numEdgeCollisionChecks_;
                outputStream << "):    ";
                //The message:
                outputStream << status;


                if (msgLevel == ompl::msg::LOG_DEBUG)
                {
                    OMPL_DEBUG("%s", outputStream.str().c_str());
                }
                else if (msgLevel == ompl::msg::LOG_INFO)
                {
                    OMPL_INFORM("%s", outputStream.str().c_str());
                }
                else if (msgLevel == ompl::msg::LOG_WARN)
                {
                    OMPL_WARN("%s", outputStream.str().c_str());
                }
                else if (msgLevel == ompl::msg::LOG_ERROR)
                {
                    OMPL_ERROR("%s", outputStream.str().c_str());
                }
                else
                {
                    throw ompl::Exception("Log level not recognized");
                }
            }
            //No else, this message is below the log level
        }
        /////////////////////////////////////////////////////////////////////////////////////////////



        /////////////////////////////////////////////////////////////////////////////////////////////
        //Boring sets/gets (Public) and progress properties (Protected):
        void BITstar::setRewireFactor(double rewireFactor)
        {
            rewireFactor_ = rewireFactor;

            //Check if there's things to update
            if (this->isSetup() == true)
            {
                //Reinitialize the terms:
                this->initializeNearestTerms();
            }
        }



        double BITstar::getRewireFactor() const
        {
            return rewireFactor_;
        }



        void BITstar::setSamplesPerBatch(unsigned int n)
        {
            samplesPerBatch_ = n;
        }



        unsigned int BITstar::getSamplesPerBatch() const
        {
            return samplesPerBatch_;
        }



        void BITstar::setKNearest(bool useKNearest)
        {
            //Check if the flag has changed
            if (useKNearest != useKNearest_)
            {
                //Set the k-nearest flag
                useKNearest_ = useKNearest;

                if (useKNearest_ == true)
                {
                    //Warn that this isn't exactly implemented
                    OMPL_WARN("%s: The implementation of k-nearest is overly conservative, as it considers the k-nearest samples and the k-nearest vertices, instead of just the combined k-nearest.", Planner::getName().c_str()); //This is because we have a separate nearestNeighbours structure for samples and vertices and you don't know what fraction of K to ask for from each...

                    //Check that we're not doing JIT
                    if (useJustInTimeSampling_ == true)
                    {
                        throw ompl::Exception("JIT sampling does not work with the k-nearest variant of BIT*.");
                    }
                }

                //Check if there's things to update
                if (this->isSetup() == true)
                {
                    //Reinitialize the terms:
                    this->initializeNearestTerms();
                }
            }
            //No else, it didn't change.
        }



        bool BITstar::getKNearest() const
        {
            return useKNearest_;
        }


        void BITstar::setStrictQueueOrdering(bool beStrict)
        {
            useStrictQueueOrdering_ = beStrict;
        }



        bool BITstar::getStrictQueueOrdering() const
        {
            return useStrictQueueOrdering_;
        }



        void BITstar::setPruning(bool prune)
        {
            if (prune == false)
            {
                OMPL_WARN("%s: Turning pruning off does not turn a fake pruning on, as it should.", Planner::getName().c_str());
            }

            usePruning_ = prune;
        }



        bool BITstar::getPruning() const
        {
            return usePruning_;
        }



        void BITstar::setPruneThresholdFraction(double fractionalChange)
        {
            if (fractionalChange < 0.0 || fractionalChange > 1.0)
            {
                throw ompl::Exception("Prune threshold must be specified as a fraction between [0, 1].");
            }

            pruneFraction_ = fractionalChange;
        }



        double BITstar::getPruneThresholdFraction() const
        {
            return pruneFraction_;
        }



        void BITstar::setDelayRewiringUntilInitialSolution(bool delayRewiring)
        {
            delayRewiring_ = delayRewiring;

            //Configure queue if constructed:
            if (intQueue_)
            {
                intQueue_->setDelayedRewiring(delayRewiring_);
            }
        }



        bool BITstar::getDelayRewiringUntilInitialSolution() const
        {
            return delayRewiring_;
        }



        void BITstar::setJustInTimeSampling(bool useJit)
        {
            if (useJit == true)
            {
                OMPL_WARN("%s: Just-in-time sampling is experimental and currently only implemented for problems seeking to minimize path-length.", Planner::getName().c_str());

                //Assert that this the r-disc connection scheme
                if (useKNearest_ == true)
                {
                    throw ompl::Exception("JIT sampling does not work with the k-nearest variant of BIT*.");
                }
            }

            //Store
            useJustInTimeSampling_ = useJit;
        }



        bool BITstar::getJustInTimeSampling() const
        {
            return useJustInTimeSampling_;
        }



        void BITstar::setDropSamplesOnPrune(bool dropSamples)
        {
            //If we're turning the function on, make sure the number of uniform states is up to date.
            if (dropSamples == true && dropSamplesOnPrune_ == false)
            {
                //Start at 0
                numUniformStates_ = 0u;

                //Add vertices and samples, if they exist
                if (static_cast<bool>(vertexNN_) == true)
                {
                    numUniformStates_  = numUniformStates_ + vertexNN_->size();
                }
                if (static_cast<bool>(freeStateNN_) == true)
                {
                    numUniformStates_  = numUniformStates_ + freeStateNN_->size();
                }

                //Remove starts and goals if this won't make us negative. This protects against the case where the problem is setup with starts/goals but not started yet
                if (numUniformStates_ >= (startVertices_.size() + goalVertices_.size()))
                {
                    numUniformStates_ = numUniformStates_ - startVertices_.size() - goalVertices_.size();
                }
            }

            dropSamplesOnPrune_ = dropSamples;
        }



        bool BITstar::getDropSamplesOnPrune() const
        {
            return dropSamplesOnPrune_;
        }



        void BITstar::setUseFailureTracking(bool trackFailures)
        {
            //Store
            useFailureTracking_ = trackFailures;

            //Configure queue if constructed:
            if (intQueue_)
            {
                intQueue_->setUseFailureTracking(useFailureTracking_);
            }
        }



        bool BITstar::getUseFailureTracking() const
        {
            return useFailureTracking_;
        }



        void BITstar::setStopOnSolnImprovement(bool stopOnChange)
        {
            stopOnSolnChange_ = stopOnChange;
        }



        bool BITstar::getStopOnSolnImprovement() const
        {
            return stopOnSolnChange_;
        }



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



        std::string BITstar::bestCostProgressProperty() const
        {
            return boost::lexical_cast<std::string>(this->bestCost().value());
        }



        std::string BITstar::bestLengthProgressProperty() const
        {
            return boost::lexical_cast<std::string>(bestLength_);
        }



        std::string BITstar::currentFreeProgressProperty() const
        {
            return boost::lexical_cast<std::string>(freeStateNN_->size());
        }



        std::string BITstar::currentVertexProgressProperty() const
        {
            return boost::lexical_cast<std::string>(vertexNN_->size());
        }



        std::string BITstar::vertexQueueSizeProgressProperty() const
        {
            return boost::lexical_cast<std::string>(intQueue_->numVertices());
        }



        std::string BITstar::edgeQueueSizeProgressProperty() const
        {
            return boost::lexical_cast<std::string>(intQueue_->numEdges());
        }



        unsigned int BITstar::numIterations() const
        {
            return numIterations_;
        }



        std::string BITstar::iterationProgressProperty() const
        {
            return boost::lexical_cast<std::string>(this->numIterations());
        }



        unsigned int BITstar::numBatches() const
        {
            return numBatches_;
        }



        std::string BITstar::batchesProgressProperty() const
        {
            return boost::lexical_cast<std::string>(this->numBatches());
        }



        std::string BITstar::pruningProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numPrunings_);
        }



        std::string BITstar::totalStatesCreatedProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numSamples_);
        }



        std::string BITstar::verticesConstructedProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numVertices_);
        }



        std::string BITstar::statesPrunedProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numFreeStatesPruned_);
        }



        std::string BITstar::verticesDisconnectedProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numVerticesDisconnected_);
        }



        std::string BITstar::rewiringProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numRewirings_);
        }



        std::string BITstar::stateCollisionCheckProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numStateCollisionChecks_);
        }



        std::string BITstar::edgeCollisionCheckProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numEdgeCollisionChecks_);
        }



        std::string BITstar::nearestNeighbourProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numNearestNeighbours_);
        }



        std::string BITstar::edgesProcessedProgressProperty() const
        {
            return boost::lexical_cast<std::string>(numEdgesProcessed_);
        }
        /////////////////////////////////////////////////////////////////////////////////////////////
    }//geometric
}//ompl
