Class BITstar::ImplicitGraph

Defined in File ImplicitGraph.h

Nested Relationships

This class is a nested type of Class BITstar.

Class Documentation

class ImplicitGraph

A conceptual representation of samples as an edge-implicit random geometric graph.

Short Description: An edge-implicit representation of a random geometric graph. TODO(Marlin): Separating the search tree from the RGG seems conceptually cleaner. Think about its implications.

Public Functions

ImplicitGraph(NameFunc nameFunc): Construct an implicit graph.

virtual ~ImplicitGraph() = default: Destruct the graph using default destruction.

void setup(const ompl::base::SpaceInformationPtr &spaceInformation, const ompl::base::ProblemDefinitionPtr &problemDefinition, CostHelper *costHelper, SearchQueue *searchQueue, const ompl::base::Planner *plannerPtr, ompl::base::PlannerInputStates &inputStates): Setup the ImplicitGraph, must be called before use. Does not take a copy of the PlannerInputStates, but checks it for starts/goals.

void reset(): Reset the graph to the state of construction.

bool hasAStart() const: Gets whether the graph contains a start or not.

bool hasAGoal() const: Gets whether the graph contains a goal or not.

VertexPtrVector::const_iterator startVerticesBeginConst() const: Returns a const-iterator to the front of the start-vertex vector.

VertexPtrVector::const_iterator startVerticesEndConst() const: Returns a const-iterator to the end of the start-vertex vector.

VertexPtrVector::const_iterator goalVerticesBeginConst() const: Returns a const-iterator to the front of the goal-vertex vector.

VertexPtrVector::const_iterator goalVerticesEndConst() const: Returns a const-iterator to the end of the goal-vertex vector.

ompl::base::Cost minCost() const: Get the minimum cost solution possible for this problem.

bool hasInformedMeasure() const: Query whether the underlying state sampler can provide an informed measure.

double getInformedMeasure(const ompl::base::Cost &cost) const: Query the underlying state sampler for the informed measure of the problem.

double distance(const VertexConstPtr &a, const VertexConstPtr &b) const: Computes the distance between two states.

double distance(const VertexConstPtrPair &vertices) const: Computes the distance between two states.

void nearestSamples(const VertexPtr &vertex, VertexPtrVector *neighbourSamples): Get the nearest unconnected samples using the appropriate “near” definition (i.e., k or r).

void getGraphAsPlannerData(ompl::base::PlannerData &data) const: Adds the graph to the given PlannerData struct.

VertexConstPtr closestVertexToGoal() const: IF BEING TRACKED, returns the closest vertex in the tree to the goal.

double smallestDistanceToGoal() const: IF BEING TRACKED, returns the how close vertices in the tree are to the goal.

unsigned int getConnectivityK() const: Get the k of this k-nearest RGG.

double getConnectivityR() const: Get the radius of this r-disc RGG.

VertexPtrVector getCopyOfSamples() const: Get a copy of all samples.

void registerSolutionCost(const ompl::base::Cost &solutionCost): Mark that a solution has been found and that the graph should be limited to the given heuristic value.

void updateStartAndGoalStates(ompl::base::PlannerInputStates &inputStates, const base::PlannerTerminationCondition &terminationCondition): Adds any new goals or starts that have appeared in the problem definition to the vector of vertices and the queue. Creates a new informed sampler if necessary.

void addNewSamples(const unsigned int &numSamples): Increase the resolution of the graph-based approximation of the continuous search domain by adding a batch of new samples.

std::pair<unsigned int, unsigned int> prune(double prunedMeasure): Prune the samples to the subproblem of the given measure. Returns the number of vertices disconnected and the number of samples removed.

void addToSamples(const VertexPtr &sample): Add an unconnected sample.

void addToSamples(const VertexPtrVector &samples): Add a vector of unconnected samples.

void removeFromSamples(const VertexPtr &sample): Remove a sample from the sample set.

void pruneSample(const VertexPtr &sample): Remove an unconnected sample.

void recycleSample(const VertexPtr &sample): Insert a sample into the set for recycled samples.

void registerAsVertex(const VertexPtr &vertex): Add a vertex to the tree, optionally moving it from the set of unconnected samples.

unsigned int removeFromVertices(const VertexPtr &sample, bool moveToFree): Remove a vertex from the tree, can optionally be allowed to move it to the set of unconnected samples if may still be useful.

std::pair<unsigned int, unsigned int> pruneVertex(const VertexPtr &vertex): Remove a vertex and mark as pruned.

void removeEdgeBetweenVertexAndParent(const VertexPtr &child, bool cascadeCostUpdates): Disconnect a vertex from its parent by removing the edges stored in itself, and its parents. Cascades cost updates if requested.

void setRewireFactor(double rewireFactor): Set the rewiring scale factor, s, such that r_rrg = s \times r_rrg*.

double getRewireFactor() const: Get the rewiring scale factor.

void setUseKNearest(bool useKNearest): Enable a k-nearest search for instead of an r-disc search.

bool getUseKNearest() const: Get whether a k-nearest search is being used.

void setJustInTimeSampling(bool useJit): Enable sampling “just-in-time”, i.e., only when necessary for a nearest-neighbour search.

bool getJustInTimeSampling() const: Get whether we’re using just-in-time sampling.

void setDropSamplesOnPrune(bool dropSamples): Set whether unconnected samples are dropped on pruning.

void setPruning(bool usePruning): Set whether samples that are provably not beneficial should be kept around.

bool getDropSamplesOnPrune() const: Get whether unconnected samples are dropped on pruning.

void setTrackApproximateSolutions(bool findApproximate): Set whether to track approximate solutions during the search.

bool getTrackApproximateSolutions() const: Get whether approximate solutions are tracked during the search.

void setAverageNumOfAllowedFailedAttemptsWhenSampling(std::size_t number): Set the average number of allowed failed attempts when sampling.

std::size_t getAverageNumOfAllowedFailedAttemptsWhenSampling() const: Get the average number of allowed failed attempts when sampling.

template<template<typename T> class NN> void setNearestNeighbors(): Set a different nearest neighbours datastructure.

unsigned int numSamples() const: The number of samples.

unsigned int numVertices() const: The number of vertices in the search tree.

unsigned int numStatesGenerated() const: The total number of states generated.

unsigned int numVerticesConnected() const: The total number of vertices added to the graph.

unsigned int numFreeStatesPruned() const: The number of states pruned.

unsigned int numVerticesDisconnected() const: The number of tree vertices disconnected.

unsigned int numNearestLookups() const: The number of nearest neighbour calls.

unsigned int numStateCollisionChecks() const: The number of state collision checks.

bool canVertexBeDisconnected(const VertexPtr &vertex) const: Returns whether the vertex can be pruned, i.e., whether it could provide a better solution given. the current graph. The check should always be g_t(v) + h^(v) >= g_t(x_g).

bool canSampleBePruned(const VertexPtr &sample) const: Returns whether the sample can be pruned, i.e., whether it could ever provide a better solution. The check should always be g^(v) + h^(v) >= g_t(x_g).