Exemple #1
0
 void pruneTree(KDNode<T> *node) {
     if(node == NULL) {
         return;
     } else {
         pruneTree(node->left);
         pruneTree(node->right);
         delete node;
     }
 }
TEST_F(TreeOperatorsTest, PruneLengths3) {
    vector<string> tokens;
    vector<string> leaves;

    tokens.resize(5);
    string toks[] = {"h", "7", "A", "8", "B"};
    copy (toks, toks+5, tokens.begin());

    leaves.push_back("A");

    pruneTree(tokens, leaves);

    ASSERT_EQ("B", detokenize(tokens));
}
// make sure branch lenghts are kept for nodes moved “up” in a tree as it is
// pruned
TEST_F(TreeOperatorsTest, PruneLengths2) {
    vector<string> tokens;
    vector<string> leaves;

    tokens.resize(13);
    string toks[] = {"h", "7", "A", "8", "h", "9", "h", "11", "B", "12", "C",
        "10", "D"};
    copy (toks, toks+13, tokens.begin());

    leaves.push_back("A");
    leaves.push_back("D");

    pruneTree(tokens, leaves);

    ASSERT_EQ("h\t11\tB\t12\tC", detokenize(tokens));
}
Exemple #4
0
 TreeNode* pruneTree(TreeNode* root) {
     if (!root) return NULL;
     root->left = pruneTree(root->left);
     root->right = pruneTree(root->right);
     return !root->left && !root->right && !root->val ? NULL : root;
 }
Exemple #5
0
void BTreeBase::pruneTree(BTreeNode *root, bool /*conditionalRoot*/)
{
	Traverser t(root);
	
	t.descendLeftwardToTerminal();
	bool done = false;
	while(!done)
	{
	//t.descendLeftwardToTerminal();
	if( t.current()->parent() )
	{
		if( t.oppositeNode()->hasChildren() ) pruneTree(t.oppositeNode());
	}
	
	t.moveToParent();
	if( !t.current()->hasChildren() )
	{
		//if(t.current() == t.root()) done = true;
		if(!t.current()->parent()) done = true;
		continue;
	}

	BTreeNode *l = t.current()->left();
	BTreeNode *r = t.current()->right();
	BTreeNode *n = 0;
	BTreeNode *z = 0;
	

	// Deal with situations where there are two constants so we want
	// to evaluate at compile time
	if( (l->type() == number && r->type() == number) ) // && !(t.current()==root&&conditionalRoot) )
	{
		if(t.current()->childOp() == Expression::division && r->value() == "0" ) 
		{
			t.current()->setChildOp(Expression::divbyzero);
			return;
		}
		QString value = QString::number(Parser::doArithmetic(l->value().toInt(),r->value().toInt(),t.current()->childOp()));
		t.current()->deleteChildren();
		t.current()->setChildOp(Expression::noop);
		t.current()->setType(number);
		t.current()->setValue(value);
	}
	
	// Addition and subtraction
	else if(t.current()->childOp() == Expression::addition || t.current()->childOp() == Expression::subtraction)
	{
	// See if one of the nodes is 0, and set n to the node that actually has data,
	// z to the one containing zero.
	bool zero = false;
	if( l->value() == "0" )
	{
		zero = true;
		n = r;
		z = l;
	}
	else if( r->value() == "0" )
	{
		zero = true;
		n = l;
		z = r;
	}
	// Now get rid of the useless nodes
	if(zero)
	{
		BTreeNode *p = t.current(); // save in order to delete after

		replaceNode(p,n);
		t.setCurrent(n);
		// Delete the old nodes
		delete p;
		delete z;
	}
	}
	
	// Multiplication and division
	else if(t.current()->childOp() == Expression::multiplication || t.current()->childOp() == Expression::division)
	{
	// See if one of the nodes is 0, and set n to the node that actually has data,
	// z to the one containing zero.
	bool zero = false;
	bool one = false;
	if( l->value() == "1" )
	{
		one = true;
		n = r;
		z = l;
	}
	else if( r->value() == "1" )
	{
		one = true;
		n = l;
		z = r;
	}
	if( l->value() == "0" )
	{
		zero = true;
		n = r;
		z = l;
	}
	else if( r->value() == "0" )
	{
		
		// since we can't call compileError from in this class, we have a special way of handling it:
		// Leave the children as they are, and set childOp to divbyzero
		if( t.current()->childOp() == Expression::division )
		{
			t.current()->setChildOp(Expression::divbyzero);
			return; // no point doing any more since we are going to raise a compileError later anyway.
		}
		zero = true;
		n = l;
		z = r;
	}
	// Now get rid of the useless nodes
	if(one)
	{
		BTreeNode *p = t.current(); // save in order to delete after
		replaceNode(p,n);
		t.setCurrent(n);
		// Delete the old nodes
		delete p;
		delete z;
	}
	if(zero)
	{
		BTreeNode *p = t.current();
		p->deleteChildren();
		p->setChildOp(Expression::noop);
		p->setType(number);
		p->setValue("0");
		
	}
	}
	else if( t.current()->childOp() == Expression::bwand || t.current()->childOp() == Expression::bwor || t.current()->childOp() == Expression::bwxor )
	{
	bool zero = false;
	if( l->value() == "0" )
	{
		zero = true;
		n = r;
		z = l;
	}
	else if( r->value() == "0" )
	{
		zero = true;
		n = l;
		z = r;
	}
	// Now get rid of the useless nodes
	if(zero)
	{
		BTreeNode *p = t.current();
		QString value;
		if( p->childOp() == Expression::bwand )
		{
			value = "0";
			p->deleteChildren();
			p->setChildOp(Expression::noop);
			p->setType(number);
		}
		if( p->childOp() == Expression::bwor || p->childOp() == Expression::bwxor )
		{
			value = n->value();
			BTreeNode *p = t.current(); // save in order to delete after
			replaceNode(p,n);
			t.setCurrent(n);
			// Delete the old nodes
			delete p;
			delete z;
		}
		p->setValue(value);
	}
	}
	
	if(!t.current()->parent() || t.current() == root) done = true;
	else
	{

	}
	}
}
Exemple #6
0
ompl::base::PlannerStatus ompl::geometric::RRTsharp::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();

    while (const base::State *st = pis_.nextStart())
    {
        Motion *motion = new Motion(si_);
        si_->copyState(motion->state, st);
        motion->cost = opt_->identityCost();
        nn_->add(motion);
        startMotion_ = motion;
    }

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

    if (!sampler_)
        sampler_ = si_->allocStateSampler();

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

    if (prune_ && !si_->getStateSpace()->isMetricSpace())
        OMPL_WARN("%s: tree pruning was activated but since the state space %s is not a metric space, "
                  "the optimization objective might not satisfy the triangle inequality. You may need to disable pruning."
                  , getName().c_str(), si_->getStateSpace()->getName().c_str());

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

    Motion *solution       = lastGoalMotion_;

    // \todo Make this variable unnecessary, or at least have it
    // persist across solve runs
    base::Cost bestCost    = opt_->infiniteCost();

    bestCost_ = opt_->infiniteCost();

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

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

    // e+e/d.  K-nearest RRT*
    double k_rrg           = boost::math::constants::e<double>() +
                             (boost::math::constants::e<double>() / (double)si_->getStateSpace()->getDimension());

    std::vector<Motion*>       nbh;

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

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

    if (solution)
        OMPL_INFORM("%s: Starting planning with existing solution of cost %.5f", getName().c_str(), solution->cost.value());
    OMPL_INFORM("%s: Initial k-nearest value of %u", getName().c_str(), (unsigned int)std::ceil(k_rrg * log((double)(nn_->size() + 1))));

    // 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
        {
            sampler_->sampleUniform(rstate);

            if (prune_ && opt_->isCostBetterThan(bestCost_, costToGo(rmotion)))
                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
            Motion *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 - k-nearest RRT*
            unsigned int k = std::ceil(k_rrg * log((double)(nn_->size() + 1)));
            nn_->nearestK(motion, k, 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 || 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 (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 (prune_)
            {
                if (opt_->isCostBetterThan(costToGo(motion, false), bestCost_))
                {
                    nn_->add(motion);
                    motion->parent->children.push_back(motion);
                }
                else // If the new motion does not improve the best cost it is ignored.
                {
                    --statesGenerated;
                    si_->freeState(motion->state);
                    delete motion;
                    continue;
                }
            }
            else
            {
                // add motion to the tree
                nn_->add(motion);
                motion->parent->children.push_back(motion);
            }

            this->nodesToAnalyzeForRewiring = std::priority_queue< Motion* ,std::vector<Motion*>, std::greater<Motion*>>();
            assert(nodesToAnalyzeForRewiring.empty());
            this->visitedMotions.clear();
            this->toVisitMotions.clear();
            bool checkForSolution = false;

            toVisitMotions.insert(motion);
            nodesToAnalyzeForRewiring.push(motion);

            while (!nodesToAnalyzeForRewiring.empty())
            {
//                if (((int)nn_->size())>7000)
//                    usleep(200000);
                Motion* mc = nodesToAnalyzeForRewiring.top();
                nodesToAnalyzeForRewiring.pop();
                visitedMotions.insert(mc);
                toVisitMotions.erase(mc);

                nn_->nearestK(mc, k, nbh);
//                Cost minNbhCost = mc->cost;

                bool updatedWiring = false;
                if (mc!=motion){
                    for (std::size_t i = 0; i < nbh.size(); ++i){
                        rewireTest++;
                        // TODO: add if(symCost) option
                        base::Cost temp_incCost = opt_->motionCost(nbh[i]->state, mc->state);
                        base::Cost temp_Cost = opt_->combineCosts(nbh[i]->cost, temp_incCost);

                        if (opt_->isCostBetterThan(temp_Cost,mc->cost)){
                            if (si_->checkMotion(nbh[i]->state, mc->state))
                            {
                                removeFromParent (mc);

                                mc->parent = nbh[i];
                                mc->cost = temp_Cost;
                                mc->incCost = temp_incCost;
                                mc->parent->children.push_back(mc);
                                updatedWiring = true;

                                checkForSolution = true;
                            }
                        }
                    }
                } else {
                    updatedWiring = true;
                }

                if (updatedWiring){
                    // add children to update list
                    for (std::size_t j = 0; j < mc->children.size(); ++j){
                        if (toVisitMotions.count(mc->children[j])==0 && visitedMotions.count(mc->children[j])==0){
                            nodesToAnalyzeForRewiring.push(mc->children[j]);
                            toVisitMotions.insert(mc->children[j]);
                        }
                    }

                    for (std::size_t i = 0; i < nbh.size(); ++i){
                        // TODO: avoid repeated calculation of same value
                        if (opt_->isCostBetterThan(opt_->combineCosts(mc->cost, opt_->motionCost(mc->state, nbh[i]->state)),nbh[i]->cost) && toVisitMotions.count(nbh[i])==0 && visitedMotions.count(nbh[i])==0){
                            nodesToAnalyzeForRewiring.push(nbh[i]);
                            toVisitMotions.insert(nbh[i]);
                        }
                    }
                }
            }

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

            // Checking for solution or iterative improvement
            if (checkForSolution)
            {
                bool updatedSolution = false;
                for (size_t i = 0; i < goalMotions_.size(); ++i)
                {
                    if (opt_->isCostBetterThan(goalMotions_[i]->cost, bestCost))
                    {
                        bestCost = goalMotions_[i]->cost;
                        bestCost_ = bestCost;
                        updatedSolution = true;
                    }

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

                if (updatedSolution)
                {
                    if (prune_)
                    {
                        int n = pruneTree(bestCost_);
                        statesGenerated -= n;
                    }

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

                        do
                        {
                            spath.push_back(intermediate_solution->state);
                            intermediate_solution = intermediate_solution->parent;
                        } while (intermediate_solution->parent != 0); // Do not include the start state.

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

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

        // set the solution path
        PathGeometric *geoPath = new PathGeometric(si_);
        for (int i = mpath.size() - 1 ; i >= 0 ; --i)
            geoPath->append(mpath[i]->state);

        base::PathPtr path(geoPath);
        // 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.", getName().c_str(), statesGenerated, rewireTest, goalMotions_.size());

    return base::PlannerStatus(addedSolution, approximate);
}
Exemple #7
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_rrg_ * log((double)(nn_->size() + 1u))));
    else
        OMPL_INFORM("%s: Initial rewiring radius of %.2f", getName().c_str(), std::min(maxDistance_, r_rrg_*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 || 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 (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 = 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))
            {
                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 *geoPath = new PathGeometric(si_);
        for (int i = mpath.size() - 1 ; i >= 0 ; --i)
            geoPath->append(mpath[i]->state);

        base::PathPtr path(geoPath);
        // 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);
}