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

#include "ompl/geometric/planners/rrt/RRTstar.h"
#include <algorithm>
#include <boost/math/constants/constants.hpp>
#include <limits>
#include <vector>
#include "ompl/base/Goal.h"
#include "ompl/base/goals/GoalSampleableRegion.h"
#include "ompl/base/goals/GoalState.h"
#include "ompl/base/objectives/PathLengthOptimizationObjective.h"
#include "ompl/base/samplers/InformedStateSampler.h"
#include "ompl/base/samplers/informed/RejectionInfSampler.h"
#include "ompl/base/samplers/informed/OrderedInfSampler.h"
#include "ompl/tools/config/SelfConfig.h"
#include "ompl/util/GeometricEquations.h"

ompl::geometric::RRTstar::RRTstar(const base::SpaceInformationPtr &si)
  : base::Planner(si, "RRTstar")
  , goalBias_(0.05)
  , maxDistance_(0.0)
  , useKNearest_(true)
  , rewireFactor_(1.1)
  , k_rrt_(0u)
  , r_rrt_(0.0)
  , delayCC_(true)
  , lastGoalMotion_(nullptr)
  , useTreePruning_(false)
  , pruneThreshold_(0.05)
  , usePrunedMeasure_(false)
  , useInformedSampling_(false)
  , useRejectionSampling_(false)
  , useNewStateRejection_(false)
  , useAdmissibleCostToCome_(true)
  , numSampleAttempts_(100u)
  , useOrderedSampling_(false)
  , batchSize_(1u)
  , bestCost_(std::numeric_limits<double>::quiet_NaN())
  , prunedCost_(std::numeric_limits<double>::quiet_NaN())
  , prunedMeasure_(0.0)
  , iterations_(0u)
{
    specs_.approximateSolutions = true;
    specs_.optimizingPaths = true;
    specs_.canReportIntermediateSolutions = true;

    Planner::declareParam<double>("range", this, &RRTstar::setRange, &RRTstar::getRange, "0.:1.:10000.");
    Planner::declareParam<double>("goal_bias", this, &RRTstar::setGoalBias, &RRTstar::getGoalBias, "0.:.05:1.");
    Planner::declareParam<double>("rewire_factor", this, &RRTstar::setRewireFactor, &RRTstar::getRewireFactor,
                                  "1.0:0.01:2.0");
    Planner::declareParam<bool>("use_k_nearest", this, &RRTstar::setKNearest, &RRTstar::getKNearest, "0,1");
    Planner::declareParam<bool>("delay_collision_checking", this, &RRTstar::setDelayCC, &RRTstar::getDelayCC, "0,1");
    Planner::declareParam<bool>("tree_pruning", this, &RRTstar::setTreePruning, &RRTstar::getTreePruning, "0,1");
    Planner::declareParam<double>("prune_threshold", this, &RRTstar::setPruneThreshold, &RRTstar::getPruneThreshold,
                                  "0.:.01:1.");
    Planner::declareParam<bool>("pruned_measure", this, &RRTstar::setPrunedMeasure, &RRTstar::getPrunedMeasure, "0,1");
    Planner::declareParam<bool>("informed_sampling", this, &RRTstar::setInformedSampling, &RRTstar::getInformedSampling,
                                "0,1");
    Planner::declareParam<bool>("sample_rejection", this, &RRTstar::setSampleRejection, &RRTstar::getSampleRejection,
                                "0,1");
    Planner::declareParam<bool>("new_state_rejection", this, &RRTstar::setNewStateRejection,
                                &RRTstar::getNewStateRejection, "0,1");
    Planner::declareParam<bool>("use_admissible_heuristic", this, &RRTstar::setAdmissibleCostToCome,
                                &RRTstar::getAdmissibleCostToCome, "0,1");
    Planner::declareParam<bool>("ordered_sampling", this, &RRTstar::setOrderedSampling, &RRTstar::getOrderedSampling,
                                "0,1");
    Planner::declareParam<unsigned int>("ordering_batch_size", this, &RRTstar::setBatchSize, &RRTstar::getBatchSize,
                                        "1:100:1000000");
    Planner::declareParam<bool>("focus_search", this, &RRTstar::setFocusSearch, &RRTstar::getFocusSearch, "0,1");
    Planner::declareParam<unsigned int>("number_sampling_attempts", this, &RRTstar::setNumSamplingAttempts,
                                &RRTstar::getNumSamplingAttempts, "10:10:100000");

    addPlannerProgressProperty("iterations INTEGER", [this] { return numIterationsProperty(); });
    addPlannerProgressProperty("best cost REAL", [this] { return bestCostProperty(); });
}

ompl::geometric::RRTstar::~RRTstar()
{
    freeMemory();
}

void ompl::geometric::RRTstar::setup()
{
    Planner::setup();
    tools::SelfConfig sc(si_, getName());
    sc.configurePlannerRange(maxDistance_);
    if (!si_->getStateSpace()->hasSymmetricDistance() || !si_->getStateSpace()->hasSymmetricInterpolate())
    {
        OMPL_WARN("%s requires a state space with symmetric distance and symmetric interpolation.", getName().c_str());
    }

    if (!nn_)
        nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
    nn_->setDistanceFunction([this](const Motion *a, const Motion *b) { return distanceFunction(a, b); });

    // Setup optimization objective
    //
    // If no optimization objective was specified, then default to
    // optimizing path length as computed by the distance() function
    // in the state space.
    if (pdef_)
    {
        if (pdef_->hasOptimizationObjective())
            opt_ = pdef_->getOptimizationObjective();
        else
        {
            OMPL_INFORM("%s: No optimization objective specified. Defaulting to optimizing path length for the allowed "
                        "planning time.",
                        getName().c_str());
            opt_ = std::make_shared<base::PathLengthOptimizationObjective>(si_);

            // Store the new objective in the problem def'n
            pdef_->setOptimizationObjective(opt_);
        }

        // Set the bestCost_ and prunedCost_ as infinite
        bestCost_ = opt_->infiniteCost();
        prunedCost_ = opt_->infiniteCost();
    }
    else
    {
        OMPL_INFORM("%s: problem definition is not set, deferring setup completion...", getName().c_str());
        setup_ = false;
    }

    // Get the measure of the entire space:
    prunedMeasure_ = si_->getSpaceMeasure();

    // Calculate some constants:
    calculateRewiringLowerBounds();
}

void ompl::geometric::RRTstar::clear()
{
    setup_ = false;
    Planner::clear();
    sampler_.reset();
    infSampler_.reset();
    freeMemory();
    if (nn_)
        nn_->clear();

    lastGoalMotion_ = nullptr;
    goalMotions_.clear();
    startMotions_.clear();

    iterations_ = 0;
    bestCost_ = base::Cost(std::numeric_limits<double>::quiet_NaN());
    prunedCost_ = base::Cost(std::numeric_limits<double>::quiet_NaN());
    prunedMeasure_ = 0.0;
}

ompl::base::PlannerStatus ompl::geometric::RRTstar::solve(const base::PlannerTerminationCondition &ptc)
{
    checkValidity();
    base::Goal *goal = pdef_->getGoal().get();
    base::GoalSampleableRegion *goal_s = dynamic_cast<base::GoalSampleableRegion *>(goal);

    bool symCost = opt_->isSymmetric();

    // Check if there are more starts
    if (pis_.haveMoreStartStates() == true)
    {
        // There are, add them
        while (const base::State *st = pis_.nextStart())
        {
            auto *motion = new Motion(si_);
            si_->copyState(motion->state, st);
            motion->cost = opt_->identityCost();
            nn_->add(motion);
            startMotions_.push_back(motion);
        }

        // And assure that, if we're using an informed sampler, it's reset
        infSampler_.reset();
    }
    // No else

    if (nn_->size() == 0)
    {
        OMPL_ERROR("%s: There are no valid initial states!", getName().c_str());
        return base::PlannerStatus::INVALID_START;
    }

    // Allocate a sampler if necessary
    if (!sampler_ && !infSampler_)
    {
        allocSampler();
    }

    OMPL_INFORM("%s: Starting planning with %u states already in datastructure", getName().c_str(), nn_->size());

    if ((useTreePruning_ || useRejectionSampling_ || useInformedSampling_ || useNewStateRejection_) &&
        !si_->getStateSpace()->isMetricSpace())
        OMPL_WARN("%s: The state space (%s) is not metric and as a result the optimization objective may not satisfy "
                  "the triangle inequality. "
                  "You may need to disable pruning or rejection.",
                  getName().c_str(), si_->getStateSpace()->getName().c_str());

    const base::ReportIntermediateSolutionFn intermediateSolutionCallback = pdef_->getIntermediateSolutionCallback();

    Motion *solution = lastGoalMotion_;

    Motion *approximation = nullptr;
    double approximatedist = std::numeric_limits<double>::infinity();
    bool sufficientlyShort = false;

    auto *rmotion = new Motion(si_);
    base::State *rstate = rmotion->state;
    base::State *xstate = si_->allocState();

    std::vector<Motion *> nbh;

    std::vector<base::Cost> costs;
    std::vector<base::Cost> incCosts;
    std::vector<std::size_t> sortedCostIndices;

    std::vector<int> valid;
    unsigned int rewireTest = 0;
    unsigned int statesGenerated = 0;

    if (solution)
        OMPL_INFORM("%s: Starting planning with existing solution of cost %.5f", getName().c_str(),
                    solution->cost.value());

    if (useKNearest_)
        OMPL_INFORM("%s: Initial k-nearest value of %u", getName().c_str(),
                    (unsigned int)std::ceil(k_rrt_ * log((double)(nn_->size() + 1u))));
    else
        OMPL_INFORM(
            "%s: Initial rewiring radius of %.2f", getName().c_str(),
            std::min(maxDistance_, r_rrt_ * std::pow(log((double)(nn_->size() + 1u)) / ((double)(nn_->size() + 1u)),
                                                     1 / (double)(si_->getStateDimension()))));

    // our functor for sorting nearest neighbors
    CostIndexCompare compareFn(costs, *opt_);

    while (ptc == false)
    {
        iterations_++;

        // sample random state (with goal biasing)
        // Goal samples are only sampled until maxSampleCount() goals are in the tree, to prohibit duplicate goal
        // states.
        if (goal_s && goalMotions_.size() < goal_s->maxSampleCount() && rng_.uniform01() < goalBias_ &&
            goal_s->canSample())
            goal_s->sampleGoal(rstate);
        else
        {
            // Attempt to generate a sample, if we fail (e.g., too many rejection attempts), skip the remainder of this
            // loop and return to try again
            if (!sampleUniform(rstate))
                continue;
        }

        // find closest state in the tree
        Motion *nmotion = nn_->nearest(rmotion);

        if (intermediateSolutionCallback && si_->equalStates(nmotion->state, rstate))
            continue;

        base::State *dstate = rstate;

        // find state to add to the tree
        double d = si_->distance(nmotion->state, rstate);
        if (d > maxDistance_)
        {
            si_->getStateSpace()->interpolate(nmotion->state, rstate, maxDistance_ / d, xstate);
            dstate = xstate;
        }

        // Check if the motion between the nearest state and the state to add is valid
        if (si_->checkMotion(nmotion->state, dstate))
        {
            // create a motion
            auto *motion = new Motion(si_);
            si_->copyState(motion->state, dstate);
            motion->parent = nmotion;
            motion->incCost = opt_->motionCost(nmotion->state, motion->state);
            motion->cost = opt_->combineCosts(nmotion->cost, motion->incCost);

            // Find nearby neighbors of the new motion
            getNeighbors(motion, nbh);

            rewireTest += nbh.size();
            ++statesGenerated;

            // cache for distance computations
            //
            // Our cost caches only increase in size, so they're only
            // resized if they can't fit the current neighborhood
            if (costs.size() < nbh.size())
            {
                costs.resize(nbh.size());
                incCosts.resize(nbh.size());
                sortedCostIndices.resize(nbh.size());
            }

            // cache for motion validity (only useful in a symmetric space)
            //
            // Our validity caches only increase in size, so they're
            // only resized if they can't fit the current neighborhood
            if (valid.size() < nbh.size())
                valid.resize(nbh.size());
            std::fill(valid.begin(), valid.begin() + nbh.size(), 0);

            // Finding the nearest neighbor to connect to
            // By default, neighborhood states are sorted by cost, and collision checking
            // is performed in increasing order of cost
            if (delayCC_)
            {
                // calculate all costs and distances
                for (std::size_t i = 0; i < nbh.size(); ++i)
                {
                    incCosts[i] = opt_->motionCost(nbh[i]->state, motion->state);
                    costs[i] = opt_->combineCosts(nbh[i]->cost, incCosts[i]);
                }

                // sort the nodes
                //
                // we're using index-value pairs so that we can get at
                // original, unsorted indices
                for (std::size_t i = 0; i < nbh.size(); ++i)
                    sortedCostIndices[i] = i;
                std::sort(sortedCostIndices.begin(), sortedCostIndices.begin() + nbh.size(), compareFn);

                // collision check until a valid motion is found
                //
                // ASYMMETRIC CASE: it's possible that none of these
                // neighbors are valid. This is fine, because motion
                // already has a connection to the tree through
                // nmotion (with populated cost fields!).
                for (std::vector<std::size_t>::const_iterator i = sortedCostIndices.begin();
                     i != sortedCostIndices.begin() + nbh.size(); ++i)
                {
                    if (nbh[*i] == nmotion ||
                        ((!useKNearest_ || si_->distance(nbh[*i]->state, motion->state) < maxDistance_) &&
                         si_->checkMotion(nbh[*i]->state, motion->state)))
                    {
                        motion->incCost = incCosts[*i];
                        motion->cost = costs[*i];
                        motion->parent = nbh[*i];
                        valid[*i] = 1;
                        break;
                    }
                    else
                        valid[*i] = -1;
                }
            }
            else  // if not delayCC
            {
                motion->incCost = opt_->motionCost(nmotion->state, motion->state);
                motion->cost = opt_->combineCosts(nmotion->cost, motion->incCost);
                // find which one we connect the new state to
                for (std::size_t i = 0; i < nbh.size(); ++i)
                {
                    if (nbh[i] != nmotion)
                    {
                        incCosts[i] = opt_->motionCost(nbh[i]->state, motion->state);
                        costs[i] = opt_->combineCosts(nbh[i]->cost, incCosts[i]);
                        if (opt_->isCostBetterThan(costs[i], motion->cost))
                        {
                            if ((!useKNearest_ || si_->distance(nbh[i]->state, motion->state) < maxDistance_) &&
                                si_->checkMotion(nbh[i]->state, motion->state))
                            {
                                motion->incCost = incCosts[i];
                                motion->cost = costs[i];
                                motion->parent = nbh[i];
                                valid[i] = 1;
                            }
                            else
                                valid[i] = -1;
                        }
                    }
                    else
                    {
                        incCosts[i] = motion->incCost;
                        costs[i] = motion->cost;
                        valid[i] = 1;
                    }
                }
            }

            if (useNewStateRejection_)
            {
                if (opt_->isCostBetterThan(solutionHeuristic(motion), bestCost_))
                {
                    nn_->add(motion);
                    motion->parent->children.push_back(motion);
                }
                else  // If the new motion does not improve the best cost it is ignored.
                {
                    si_->freeState(motion->state);
                    delete motion;
                    continue;
                }
            }
            else
            {
                // add motion to the tree
                nn_->add(motion);
                motion->parent->children.push_back(motion);
            }

            bool checkForSolution = false;
            for (std::size_t i = 0; i < nbh.size(); ++i)
            {
                if (nbh[i] != motion->parent)
                {
                    base::Cost nbhIncCost;
                    if (symCost)
                        nbhIncCost = incCosts[i];
                    else
                        nbhIncCost = opt_->motionCost(motion->state, nbh[i]->state);
                    base::Cost nbhNewCost = opt_->combineCosts(motion->cost, nbhIncCost);
                    if (opt_->isCostBetterThan(nbhNewCost, nbh[i]->cost))
                    {
                        bool motionValid;
                        if (valid[i] == 0)
                        {
                            motionValid =
                                (!useKNearest_ || si_->distance(nbh[i]->state, motion->state) < maxDistance_) &&
                                si_->checkMotion(motion->state, nbh[i]->state);
                        }
                        else
                        {
                            motionValid = (valid[i] == 1);
                        }

                        if (motionValid)
                        {
                            // Remove this node from its parent list
                            removeFromParent(nbh[i]);

                            // Add this node to the new parent
                            nbh[i]->parent = motion;
                            nbh[i]->incCost = nbhIncCost;
                            nbh[i]->cost = nbhNewCost;
                            nbh[i]->parent->children.push_back(nbh[i]);

                            // Update the costs of the node's children
                            updateChildCosts(nbh[i]);

                            checkForSolution = true;
                        }
                    }
                }
            }

            // Add the new motion to the goalMotion_ list, if it satisfies the goal
            double distanceFromGoal;
            if (goal->isSatisfied(motion->state, &distanceFromGoal))
            {
                motion->inGoal = true;
                goalMotions_.push_back(motion);
                checkForSolution = true;
            }

            // Checking for solution or iterative improvement
            if (checkForSolution)
            {
                bool updatedSolution = false;
                for (auto &goalMotion : goalMotions_)
                {
                    if (opt_->isCostBetterThan(goalMotion->cost, bestCost_))
                    {
                        if (opt_->isFinite(bestCost_) == false)
                        {
                            OMPL_INFORM("%s: Found an initial solution with a cost of %.2f in %u iterations (%u "
                                        "vertices in the graph)",
                                        getName().c_str(), goalMotion->cost.value(), iterations_, nn_->size());
                        }
                        bestCost_ = goalMotion->cost;
                        updatedSolution = true;
                    }

                    sufficientlyShort = opt_->isSatisfied(goalMotion->cost);
                    if (sufficientlyShort)
                    {
                        solution = goalMotion;
                        break;
                    }
                    else if (!solution || opt_->isCostBetterThan(goalMotion->cost, solution->cost))
                    {
                        solution = goalMotion;
                        updatedSolution = true;
                    }
                }

                if (updatedSolution)
                {
                    if (useTreePruning_)
                    {
                        pruneTree(bestCost_);
                    }

                    if (intermediateSolutionCallback)
                    {
                        std::vector<const base::State *> spath;
                        Motion *intermediate_solution =
                            solution->parent;  // Do not include goal state to simplify code.

                        // Push back until we find the start, but not the start itself
                        while (intermediate_solution->parent != nullptr)
                        {
                            spath.push_back(intermediate_solution->state);
                            intermediate_solution = intermediate_solution->parent;
                        }

                        intermediateSolutionCallback(this, spath, bestCost_);
                    }
                }
            }

            // Checking for approximate solution (closest state found to the goal)
            if (goalMotions_.size() == 0 && distanceFromGoal < approximatedist)
            {
                approximation = motion;
                approximatedist = distanceFromGoal;
            }
        }

        // terminate if a sufficient solution is found
        if (solution && sufficientlyShort)
            break;
    }

    bool approximate = (solution == nullptr);
    bool addedSolution = false;
    if (approximate)
        solution = approximation;
    else
        lastGoalMotion_ = solution;

    if (solution != nullptr)
    {
        ptc.terminate();
        // construct the solution path
        std::vector<Motion *> mpath;
        while (solution != nullptr)
        {
            mpath.push_back(solution);
            solution = solution->parent;
        }

        // set the solution path
        auto path(std::make_shared<PathGeometric>(si_));
        for (int i = mpath.size() - 1; i >= 0; --i)
            path->append(mpath[i]->state);

        // Add the solution path.
        base::PlannerSolution psol(path);
        psol.setPlannerName(getName());
        if (approximate)
            psol.setApproximate(approximatedist);
        // Does the solution satisfy the optimization objective?
        psol.setOptimized(opt_, bestCost_, sufficientlyShort);
        pdef_->addSolutionPath(psol);

        addedSolution = true;
    }

    si_->freeState(xstate);
    if (rmotion->state)
        si_->freeState(rmotion->state);
    delete rmotion;

    OMPL_INFORM("%s: Created %u new states. Checked %u rewire options. %u goal states in tree. Final solution cost "
                "%.3f",
                getName().c_str(), statesGenerated, rewireTest, goalMotions_.size(), bestCost_.value());

    return base::PlannerStatus(addedSolution, approximate);
}

void ompl::geometric::RRTstar::getNeighbors(Motion *motion, std::vector<Motion *> &nbh) const
{
    double cardDbl = static_cast<double>(nn_->size() + 1u);
    if (useKNearest_)
    {
        //- k-nearest RRT*
        unsigned int k = std::ceil(k_rrt_ * log(cardDbl));
        nn_->nearestK(motion, k, nbh);
    }
    else
    {
        double r = std::min(
            maxDistance_, r_rrt_ * std::pow(log(cardDbl) / cardDbl, 1 / static_cast<double>(si_->getStateDimension())));
        nn_->nearestR(motion, r, nbh);
    }
}

void ompl::geometric::RRTstar::removeFromParent(Motion *m)
{
    for (auto it = m->parent->children.begin(); it != m->parent->children.end(); ++it)
    {
        if (*it == m)
        {
            m->parent->children.erase(it);
            break;
        }
    }
}

void ompl::geometric::RRTstar::updateChildCosts(Motion *m)
{
    for (std::size_t i = 0; i < m->children.size(); ++i)
    {
        m->children[i]->cost = opt_->combineCosts(m->cost, m->children[i]->incCost);
        updateChildCosts(m->children[i]);
    }
}

void ompl::geometric::RRTstar::freeMemory()
{
    if (nn_)
    {
        std::vector<Motion *> motions;
        nn_->list(motions);
        for (auto &motion : motions)
        {
            if (motion->state)
                si_->freeState(motion->state);
            delete motion;
        }
    }
}

void ompl::geometric::RRTstar::getPlannerData(base::PlannerData &data) const
{
    Planner::getPlannerData(data);

    std::vector<Motion *> motions;
    if (nn_)
        nn_->list(motions);

    if (lastGoalMotion_)
        data.addGoalVertex(base::PlannerDataVertex(lastGoalMotion_->state));

    for (auto &motion : motions)
    {
        if (motion->parent == nullptr)
            data.addStartVertex(base::PlannerDataVertex(motion->state));
        else
            data.addEdge(base::PlannerDataVertex(motion->parent->state), base::PlannerDataVertex(motion->state));
    }
}

int ompl::geometric::RRTstar::pruneTree(const base::Cost &pruneTreeCost)
{
    // Variable
    // The percent improvement (expressed as a [0,1] fraction) in cost
    double fracBetter;
    // The number pruned
    int numPruned = 0;

    if (opt_->isFinite(prunedCost_))
    {
        fracBetter = std::abs((pruneTreeCost.value() - prunedCost_.value()) / prunedCost_.value());
    }
    else
    {
        fracBetter = 1.0;
    }

    if (fracBetter > pruneThreshold_)
    {
        // We are only pruning motions if they, AND all descendents, have a estimated cost greater than pruneTreeCost
        // The easiest way to do this is to find leaves that should be pruned and ascend up their ancestry until a
        // motion is found that is kept.
        // To avoid making an intermediate copy of the NN structure, we process the tree by descending down from the
        // start(s).
        // In the first pass, all Motions with a cost below pruneTreeCost, or Motion's with children with costs below
        // pruneTreeCost are added to the replacement NN structure,
        // while all other Motions are stored as either a 'leaf' or 'chain' Motion. After all the leaves are
        // disconnected and deleted, we check
        // if any of the the chain Motions are now leaves, and repeat that process until done.
        // This avoids (1) copying the NN structure into an intermediate variable and (2) the use of the expensive
        // NN::remove() method.

        // Variable
        // The queue of Motions to process:
        std::queue<Motion *, std::deque<Motion *>> motionQueue;
        // The list of leaves to prune
        std::queue<Motion *, std::deque<Motion *>> leavesToPrune;
        // The list of chain vertices to recheck after pruning
        std::list<Motion *> chainsToRecheck;

        // Clear the NN structure:
        nn_->clear();

        // Put all the starts into the NN structure and their children into the queue:
        // We do this so that start states are never pruned.
        for (auto &startMotion : startMotions_)
        {
            // Add to the NN
            nn_->add(startMotion);

            // Add their children to the queue:
            addChildrenToList(&motionQueue, startMotion);
        }

        while (motionQueue.empty() == false)
        {
            // Test, can the current motion ever provide a better solution?
            if (keepCondition(motionQueue.front(), pruneTreeCost))
            {
                // Yes it can, so it definitely won't be pruned
                // Add it back into the NN structure
                nn_->add(motionQueue.front());

                // Add it's children to the queue
                addChildrenToList(&motionQueue, motionQueue.front());
            }
            else
            {
                // No it can't, but does it have children?
                if (motionQueue.front()->children.empty() == false)
                {
                    // Yes it does.
                    // We can minimize the number of intermediate chain motions if we check their children
                    // If any of them won't be pruned, then this motion won't either. This intuitively seems
                    // like a nice balance between following the descendents forever.

                    // Variable
                    // Whether the children are definitely to be kept.
                    bool keepAChild = false;

                    // Find if any child is definitely not being pruned.
                    for (unsigned int i = 0u; keepAChild == false && i < motionQueue.front()->children.size(); ++i)
                    {
                        // Test if the child can ever provide a better solution
                        keepAChild = keepCondition(motionQueue.front()->children.at(i), pruneTreeCost);
                    }

                    // Are we *definitely* keeping any of the children?
                    if (keepAChild)
                    {
                        // Yes, we are, so we are not pruning this motion
                        // Add it back into the NN structure.
                        nn_->add(motionQueue.front());
                    }
                    else
                    {
                        // No, we aren't. This doesn't mean we won't though
                        // Move this Motion to the temporary list
                        chainsToRecheck.push_back(motionQueue.front());
                    }

                    // Either way. add it's children to the queue
                    addChildrenToList(&motionQueue, motionQueue.front());
                }
                else
                {
                    // No, so we will be pruning this motion:
                    leavesToPrune.push(motionQueue.front());
                }
            }

            // Pop the iterator, std::list::erase returns the next iterator
            motionQueue.pop();
        }

        // We now have a list of Motions to definitely remove, and a list of Motions to recheck
        // Iteratively check the two lists until there is nothing to to remove
        while (leavesToPrune.empty() == false)
        {
            // First empty the leave-to-prune
            while (leavesToPrune.empty() == false)
            {
                // If this leaf is a goal, remove it from the goal set
                if (leavesToPrune.front()->inGoal == true)
                {
                    // Remove it
                    goalMotions_.erase(std::remove(goalMotions_.begin(), goalMotions_.end(), leavesToPrune.front()),
                                       goalMotions_.end());
                }

                // Remove the leaf from its parent
                removeFromParent(leavesToPrune.front());

                // Erase the actual motion
                // First free the state
                si_->freeState(leavesToPrune.front()->state);

                // then delete the pointer
                delete leavesToPrune.front();

                // And finally remove it from the list, erase returns the next iterator
                leavesToPrune.pop();

                // Update our counter
                ++numPruned;
            }

            // Now, we need to go through the list of chain vertices and see if any are now leaves
            auto mIter = chainsToRecheck.begin();
            while (mIter != chainsToRecheck.end())
            {
                // Is the Motion a leaf?
                if ((*mIter)->children.empty() == true)
                {
                    // It is, add to the removal queue
                    leavesToPrune.push(*mIter);

                    // Remove from this queue, getting the next
                    mIter = chainsToRecheck.erase(mIter);
                }
                else
                {
                    // Is isn't, skip to the next
                    ++mIter;
                }
            }
        }

        // Now finally add back any vertices left in chainsToReheck.
        // These are chain vertices that have descendents that we want to keep
        for (std::list<Motion *>::const_iterator mIter = chainsToRecheck.begin(); mIter != chainsToRecheck.end();
             ++mIter)
        {
            // Add the motion back to the NN struct:
            nn_->add(*mIter);
        }

        // All done pruning.
        // Update the cost at which we've pruned:
        prunedCost_ = pruneTreeCost;

        // And if we're using the pruned measure, the measure to which we've pruned
        if (usePrunedMeasure_)
        {
            prunedMeasure_ = infSampler_->getInformedMeasure(prunedCost_);

            if (useKNearest_ == false)
            {
                calculateRewiringLowerBounds();
            }
        }
        // No else, prunedMeasure_ is the si_ measure by default.
    }

    return numPruned;
}

void ompl::geometric::RRTstar::addChildrenToList(std::queue<Motion *, std::deque<Motion *>> *motionList, Motion *motion)
{
    for (auto &child : motion->children)
    {
        motionList->push(child);
    }
}

bool ompl::geometric::RRTstar::keepCondition(const Motion *motion, const base::Cost &threshold) const
{
    // We keep if the cost-to-come-heuristic of motion is <= threshold, by checking
    // if (!threshold < heuristic), as if b is not better than a, then a is better than, or equal to, b
    return !opt_->isCostBetterThan(threshold, solutionHeuristic(motion));
}

ompl::base::Cost ompl::geometric::RRTstar::solutionHeuristic(const Motion *motion) const
{
    base::Cost costToCome;
    if (useAdmissibleCostToCome_)
    {
        // Start with infinite cost
        costToCome = opt_->infiniteCost();

        // Find the min from each start
        for (auto &startMotion : startMotions_)
        {
            costToCome = opt_->betterCost(
                costToCome, opt_->motionCost(startMotion->state,
                                             motion->state));  // lower-bounding cost from the start to the state
        }
    }
    else
    {
        costToCome = motion->cost;  // current cost from the state to the goal
    }

    const base::Cost costToGo =
        opt_->costToGo(motion->state, pdef_->getGoal().get());  // lower-bounding cost from the state to the goal
    return opt_->combineCosts(costToCome, costToGo);            // add the two costs
}

void ompl::geometric::RRTstar::setTreePruning(const bool prune)
{
    if (static_cast<bool>(opt_) == true)
    {
        if (opt_->hasCostToGoHeuristic() == false)
        {
            OMPL_INFORM("%s: No cost-to-go heuristic set. Informed techniques will not work well.", getName().c_str());
        }
    }

    // If we just disabled tree pruning, but we wee using prunedMeasure, we need to disable that as it required myself
    if (prune == false && getPrunedMeasure() == true)
    {
        setPrunedMeasure(false);
    }

    // Store
    useTreePruning_ = prune;
}

void ompl::geometric::RRTstar::setPrunedMeasure(bool informedMeasure)
{
    if (static_cast<bool>(opt_) == true)
    {
        if (opt_->hasCostToGoHeuristic() == false)
        {
            OMPL_INFORM("%s: No cost-to-go heuristic set. Informed techniques will not work well.", getName().c_str());
        }
    }

    // This option only works with informed sampling
    if (informedMeasure == true && (useInformedSampling_ == false || useTreePruning_ == false))
    {
        OMPL_ERROR("%s: InformedMeasure requires InformedSampling and TreePruning.", getName().c_str());
    }

    // Check if we're changed and update parameters if we have:
    if (informedMeasure != usePrunedMeasure_)
    {
        // Store the setting
        usePrunedMeasure_ = informedMeasure;

        // Update the prunedMeasure_ appropriately, if it has been configured.
        if (setup_ == true)
        {
            if (usePrunedMeasure_)
            {
                prunedMeasure_ = infSampler_->getInformedMeasure(prunedCost_);
            }
            else
            {
                prunedMeasure_ = si_->getSpaceMeasure();
            }
        }

        // And either way, update the rewiring radius if necessary
        if (useKNearest_ == false)
        {
            calculateRewiringLowerBounds();
        }
    }
}

void ompl::geometric::RRTstar::setInformedSampling(bool informedSampling)
{
    if (static_cast<bool>(opt_) == true)
    {
        if (opt_->hasCostToGoHeuristic() == false)
        {
            OMPL_INFORM("%s: No cost-to-go heuristic set. Informed techniques will not work well.", getName().c_str());
        }
    }

    // This option is mutually exclusive with setSampleRejection, assert that:
    if (informedSampling == true && useRejectionSampling_ == true)
    {
        OMPL_ERROR("%s: InformedSampling and SampleRejection are mutually exclusive options.", getName().c_str());
    }

    // If we just disabled tree pruning, but we are using prunedMeasure, we need to disable that as it required myself
    if (informedSampling == false && getPrunedMeasure() == true)
    {
        setPrunedMeasure(false);
    }

    // Check if we're changing the setting of informed sampling. If we are, we will need to create a new sampler, which
    // we only want to do if one is already allocated.
    if (informedSampling != useInformedSampling_)
    {
        // If we're disabled informedSampling, and prunedMeasure is enabled, we need to disable that
        if (informedSampling == false && usePrunedMeasure_ == true)
        {
            setPrunedMeasure(false);
        }

        // Store the value
        useInformedSampling_ = informedSampling;

        // If we currently have a sampler, we need to make a new one
        if (sampler_ || infSampler_)
        {
            // Reset the samplers
            sampler_.reset();
            infSampler_.reset();

            // Create the sampler
            allocSampler();
        }
    }
}

void ompl::geometric::RRTstar::setSampleRejection(const bool reject)
{
    if (static_cast<bool>(opt_) == true)
    {
        if (opt_->hasCostToGoHeuristic() == false)
        {
            OMPL_INFORM("%s: No cost-to-go heuristic set. Informed techniques will not work well.", getName().c_str());
        }
    }

    // This option is mutually exclusive with setInformedSampling, assert that:
    if (reject == true && useInformedSampling_ == true)
    {
        OMPL_ERROR("%s: InformedSampling and SampleRejection are mutually exclusive options.", getName().c_str());
    }

    // Check if we're changing the setting of rejection sampling. If we are, we will need to create a new sampler, which
    // we only want to do if one is already allocated.
    if (reject != useRejectionSampling_)
    {
        // Store the setting
        useRejectionSampling_ = reject;

        // If we currently have a sampler, we need to make a new one
        if (sampler_ || infSampler_)
        {
            // Reset the samplers
            sampler_.reset();
            infSampler_.reset();

            // Create the sampler
            allocSampler();
        }
    }
}

void ompl::geometric::RRTstar::setOrderedSampling(bool orderSamples)
{
    // Make sure we're using some type of informed sampling
    if (useInformedSampling_ == false && useRejectionSampling_ == false)
    {
        OMPL_ERROR("%s: OrderedSampling requires either informed sampling or rejection sampling.", getName().c_str());
    }

    // Check if we're changing the setting. If we are, we will need to create a new sampler, which we only want to do if
    // one is already allocated.
    if (orderSamples != useOrderedSampling_)
    {
        // Store the setting
        useOrderedSampling_ = orderSamples;

        // If we currently have a sampler, we need to make a new one
        if (sampler_ || infSampler_)
        {
            // Reset the samplers
            sampler_.reset();
            infSampler_.reset();

            // Create the sampler
            allocSampler();
        }
    }
}

void ompl::geometric::RRTstar::allocSampler()
{
    // Allocate the appropriate type of sampler.
    if (useInformedSampling_)
    {
        // We are using informed sampling, this can end-up reverting to rejection sampling in some cases
        OMPL_INFORM("%s: Using informed sampling.", getName().c_str());
        infSampler_ = opt_->allocInformedStateSampler(pdef_, numSampleAttempts_);
    }
    else if (useRejectionSampling_)
    {
        // We are explicitly using rejection sampling.
        OMPL_INFORM("%s: Using rejection sampling.", getName().c_str());
        infSampler_ = std::make_shared<base::RejectionInfSampler>(pdef_, numSampleAttempts_);
    }
    else
    {
        // We are using a regular sampler
        sampler_ = si_->allocStateSampler();
    }

    // Wrap into a sorted sampler
    if (useOrderedSampling_ == true)
    {
        infSampler_ = std::make_shared<base::OrderedInfSampler>(infSampler_, batchSize_);
    }
    // No else
}

bool ompl::geometric::RRTstar::sampleUniform(base::State *statePtr)
{
    // Use the appropriate sampler
    if (useInformedSampling_ || useRejectionSampling_)
    {
        // Attempt the focused sampler and return the result.
        // If bestCost is changing a lot by small amounts, this could
        // be prunedCost_ to reduce the number of times the informed sampling
        // transforms are recalculated.
        return infSampler_->sampleUniform(statePtr, bestCost_);
    }
    else
    {
        // Simply return a state from the regular sampler
        sampler_->sampleUniform(statePtr);

        // Always true
        return true;
    }
}

void ompl::geometric::RRTstar::calculateRewiringLowerBounds()
{
    const double dimDbl = static_cast<double>(si_->getStateDimension());

    // k_rrt > 2^(d + 1) * e * (1 + 1 / d).  K-nearest RRT*
    k_rrt_ = rewireFactor_ * (std::pow(2, dimDbl + 1) * boost::math::constants::e<double>() * (1.0 + 1.0 / dimDbl));

    // r_rrt > (2*(1+1/d))^(1/d)*(measure/ballvolume)^(1/d)
    // If we're not using the informed measure, prunedMeasure_ will be set to si_->getSpaceMeasure();
    r_rrt_ =
        rewireFactor_ * 
        std::pow(2 * (1.0 + 1.0 / dimDbl) * (prunedMeasure_ / unitNBallMeasure(si_->getStateDimension())), 1.0 / dimDbl);
}
