Advanced Batch Informed Trees (ABIT*) More...

#include <ompl/geometric/planners/informedtrees/ABITstar.h>

Inheritance diagram for ompl::geometric::ABITstar:

Public Member Functions
	ABITstar (const base::SpaceInformationPtr &spaceInfo, const std::string &name="ABITstar")

void	setInitialInflationFactor (double factor)
	Set the inflation factor for the initial search.

void	setInflationScalingParameter (double parameter)
	Set the parameter for the inflation factor update policy.

void	setTruncationScalingParameter (double parameter)
	Set the parameter for the truncation factor update policy.

double	getInitialInflationFactor () const
	Get the inflation factor for the initial search.

double	getInflationScalingParameter () const
	Get the inflation scaling parameter.

double	getTruncationScalingParameter () const
	Get the truncation scaling parameter.

double	getCurrentInflationFactor () const
	Get the inflation factor for the current search.

double	getCurrentTruncationFactor () const
	Get the truncation factor for the current search.

Public Member Functions inherited from ompl::geometric::BITstar
	BITstar (const base::SpaceInformationPtr &spaceInfo, const std::string &name="kBITstar")
	Construct with a pointer to the space information and an optional name.

virtual	~BITstar () override=default
	Destruct using the default destructor.

void	setup () override
	Setup the algorithm.

void	clear () override
	Clear the algorithm's internal state.

base::PlannerStatus	solve (const base::PlannerTerminationCondition &terminationCondition) override
	Solve the problem given a termination condition.

void	getPlannerData (base::PlannerData &data) const override
	Get results.

std::pair< const ompl::base::State , const ompl::base::State >	getNextEdgeInQueue ()
	Get the next edge to be processed. Causes vertices in the queue to be expanded (if necessary) and therefore effects the run timings of the algorithm, but helpful for some videos and debugging.

ompl::base::Cost	getNextEdgeValueInQueue ()
	Get the value of the next edge to be processed. Causes vertices in the queue to be expanded (if necessary) and therefore effects the run timings of the algorithm, but helpful for some videos and debugging.

void	getEdgeQueue (VertexConstPtrPairVector *edgesInQueue)
	Get the whole messy set of edges in the queue. Expensive but helpful for some videos.

unsigned int	numIterations () const
	Get the number of iterations completed.

ompl::base::Cost	bestCost () const
	Retrieve the best exact-solution cost found.

unsigned int	numBatches () const
	Retrieve the number of batches processed as the raw data.

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	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.

void	setSamplesPerBatch (unsigned int n)
	Set the number of samplers per batch.

unsigned int	getSamplesPerBatch () const
	Get the number of samplers per batch.

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	setStrictQueueOrdering (bool beStrict)
	Enable "strict sorting" of the edge queue. Rewirings can change the position in the queue of an edge. When strict sorting is enabled, the effected edges are resorted immediately, while disabling strict sorting delays this resorting until the end of the batch.

bool	getStrictQueueOrdering () const
	Get whether strict queue ordering is in use.

void	setPruning (bool prune)
	Enable pruning of vertices/samples that CANNOT improve the current solution. When a vertex in the graph is pruned, it's descendents are also pruned (if they also cannot improve the solution) or placed back in the set of free samples (if they could improve the solution). This assures that a uniform density is maintained.

bool	getPruning () const
	Get whether graph and sample pruning is in use.

void	setPruneThresholdFraction (double fractionalChange)
	Set the fractional change in the solution cost AND problem measure necessary for pruning to occur.

double	getPruneThresholdFraction () const
	Get the fractional change in the solution cost AND problem measure necessary for pruning to occur.

void	setDelayRewiringUntilInitialSolution (bool delayRewiring)
	Delay the consideration of rewiring edges until an initial solution is found. When multiple batches are required to find an initial solution, this can improve the time required to do so, by delaying improvements in the cost-to-come to a connected vertex. As the rewiring edges are considered once an initial solution is found, this has no effect on the theoretical asymptotic optimality of the planner. More...

bool	getDelayRewiringUntilInitialSolution () const
	Get whether BIT* is delaying rewiring until a solution is found.

void	setJustInTimeSampling (bool useJit)
	Delay the generation of samples until they are necessary. This only works when using an r-disc connection scheme, and is currently only implemented for problems seeking to minimize path length. This helps reduce the complexity of nearest-neighbour look ups, and can be particularly beneficial in unbounded planning problems where selecting an appropriate bounding box is difficult. With JIT sampling enabled, BIT* can solve planning problems whose state space has infinite boundaries. When enumerating outgoing edges from a vertex, BIT* uses JIT sampling to assure that the area within r of the vertex has been sampled during this batch. This is done in a way that maintains uniform sample distribution and has no effect on the theoretical asymptotic optimality of the planner. More...

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

void	setDropSamplesOnPrune (bool dropSamples)
	Drop all unconnected samples when pruning, regardless of their heuristic value. This provides a method for BIT* to remove samples that have not been connected to the graph and may be beneficial in problems where portions of the free space are unreachable (i.e., disconnected). BIT* calculates the connection radius for each batch from the underlying uniform distribution of states. The resulting larger connection radius may be detrimental in areas where the graph is dense, but maintains the theoretical asymptotic optimality of the planner. More...

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

void	setStopOnSolnImprovement (bool stopOnChange)
	Stop the planner each time a solution improvement is found. Useful for examining the intermediate solutions found by BIT*.

bool	getStopOnSolnImprovement () const
	Get whether BIT* stops each time a solution is found.

void	setConsiderApproximateSolutions (bool findApproximate)
	Set BIT* to consider approximate solutions during its initial search.

bool	getConsiderApproximateSolutions () const
	Get whether BIT* is considering approximate solutions.

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

Public Member Functions inherited from ompl::base::Planner
	Planner (const Planner &)=delete

Planner &	operator= (const Planner &)=delete

	Planner (SpaceInformationPtr si, std::string name)
	Constructor.

virtual	~Planner ()=default
	Destructor.

template<class T >
T *	as ()
	Cast this instance to a desired type. More...

template<class T >
const T *	as () const
	Cast this instance to a desired type. More...

const SpaceInformationPtr &	getSpaceInformation () const
	Get the space information this planner is using.

const ProblemDefinitionPtr &	getProblemDefinition () const
	Get the problem definition the planner is trying to solve.

ProblemDefinitionPtr &	getProblemDefinition ()
	Get the problem definition the planner is trying to solve.

const PlannerInputStates &	getPlannerInputStates () const
	Get the planner input states.

virtual void	setProblemDefinition (const ProblemDefinitionPtr &pdef)
	Set the problem definition for the planner. The problem needs to be set before calling solve(). Note: If this problem definition replaces a previous one, it may also be necessary to call clear() or clearQuery().

PlannerStatus	solve (const PlannerTerminationConditionFn &ptc, double checkInterval)
	Same as above except the termination condition is only evaluated at a specified interval.

PlannerStatus	solve (double solveTime)
	Same as above except the termination condition is solely a time limit: the number of seconds the algorithm is allowed to spend planning.

virtual void	clearQuery ()
	Clears internal datastructures of any query-specific information from the previous query. Planner settings are not affected. The planner, if able, should retain all datastructures generated from previous queries that can be used to help solve the next query. Note that clear() should also clear all query-specific information along with all other datastructures in the planner. By default clearQuery() calls clear().

const std::string &	getName () const
	Get the name of the planner.

void	setName (const std::string &name)
	Set the name of the planner.

const PlannerSpecs &	getSpecs () const
	Return the specifications (capabilities of this planner)

virtual void	checkValidity ()
	Check to see if the planner is in a working state (setup has been called, a goal was set, the input states seem to be in order). In case of error, this function throws an exception.

bool	isSetup () const
	Check if setup() was called for this planner.

ParamSet &	params ()
	Get the parameters for this planner.

const ParamSet &	params () const
	Get the parameters for this planner.

const PlannerProgressProperties &	getPlannerProgressProperties () const
	Retrieve a planner's planner progress property map.

virtual void	printProperties (std::ostream &out) const
	Print properties of the motion planner.

virtual void	printSettings (std::ostream &out) const
	Print information about the motion planner's settings.

Additional Inherited Members
Public Types inherited from ompl::geometric::BITstar
using	VertexPtr = std::shared_ptr< Vertex >
	A shared pointer to a vertex.

using	VertexConstPtr = std::shared_ptr< const Vertex >
	A shared pointer to a const vertex.

using	VertexWeakPtr = std::weak_ptr< Vertex >
	A weak pointer to a vertex.

using	VertexPtrVector = std::vector< VertexPtr >
	A vector of shared pointers to vertices.

using	VertexConstPtrVector = std::vector< VertexConstPtr >
	A vector of shared pointers to const vertices.

using	VertexId = unsigned int
	The vertex id type.

using	VertexPtrPair = std::pair< VertexPtr, VertexPtr >
	A pair of vertices, i.e., an edge.

using	VertexConstPtrPair = std::pair< VertexConstPtr, VertexConstPtr >
	A pair of const vertices, i.e., an edge.

using	VertexPtrPairVector = std::vector< VertexPtrPair >
	A vector of pairs of vertices, i.e., a vector of edges.

using	VertexConstPtrPairVector = std::vector< VertexConstPtrPair >
	A vector of pairs of const vertices, i.e., a vector of edges.

using	VertexPtrNNPtr = std::shared_ptr< NearestNeighbors< VertexPtr > >
	The OMPL::NearestNeighbors structure.

using	NameFunc = std::function< std::string()>
	A utility functor for ImplicitGraph and SearchQueue.

Public Types inherited from ompl::base::Planner
using	PlannerProgressProperty = std::function< std::string()>
	Definition of a function which returns a property about the planner's progress that can be queried by a benchmarking routine.

using	PlannerProgressProperties = std::map< std::string, PlannerProgressProperty >
	A dictionary which maps the name of a progress property to the function to be used for querying that property.

Protected Member Functions inherited from ompl::geometric::BITstar
void	setInitialInflationFactor (double factor)
	Set the inflation for the initial search of RGG approximation. See ABIT*'s class description for more details about the inflation factor update policy.

void	setInflationScalingParameter (double parameter)
	The parameter that scales the inflation factor on the second search of each RGG approximation. See ABIT*'s class description for more details about the inflation factor update policy.

void	setTruncationScalingParameter (double parameter)
	Sets the parameter that scales the truncation factor for the searches of each RGG approximation. See ABIT*'s class description for more details about the truncation factor update policy.

void	enableCascadingRewirings (bool enable)
	Enable the cascading of rewirings.

double	getInitialInflationFactor () const
	Get the inflation factor for the initial search.

double	getInflationScalingParameter () const
	Get the inflation scaling parameter.

double	getTruncationScalingParameter () const
	Get the truncation factor parameter.

double	getCurrentInflationFactor () const
	Get the inflation factor for the current search.

double	getCurrentTruncationFactor () const
	Get the truncation factor for the current search.

Protected Member Functions inherited from ompl::base::Planner
template<typename T , typename PlannerType , typename SetterType , typename GetterType >
void	declareParam (const std::string &name, const PlannerType &planner, const SetterType &setter, const GetterType &getter, const std::string &rangeSuggestion="")
	This function declares a parameter for this planner instance, and specifies the setter and getter functions.

template<typename T , typename PlannerType , typename SetterType >
void	declareParam (const std::string &name, const PlannerType &planner, const SetterType &setter, const std::string &rangeSuggestion="")
	This function declares a parameter for this planner instance, and specifies the setter function.

void	addPlannerProgressProperty (const std::string &progressPropertyName, const PlannerProgressProperty &prop)
	Add a planner progress property called progressPropertyName with a property querying function prop to this planner's progress property map.

Protected Attributes inherited from ompl::base::Planner
SpaceInformationPtr	si_
	The space information for which planning is done.

ProblemDefinitionPtr	pdef_
	The user set problem definition.

PlannerInputStates	pis_
	Utility class to extract valid input states

std::string	name_
	The name of this planner.

PlannerSpecs	specs_
	The specifications of the planner (its capabilities)

ParamSet	params_
	A map from parameter names to parameter instances for this planner. This field is populated by the declareParam() function.

PlannerProgressProperties	plannerProgressProperties_
	A mapping between this planner's progress property names and the functions used for querying those progress properties.

bool	setup_
	Flag indicating whether setup() has been called.

Detailed Description

Advanced Batch Informed Trees (ABIT*)

ABIT* (Advanced Batch Informed Trees) is an almost-surely asymptotically optimal path planner. It views the planning problem as the two subproblems of approximation and search. This perspective allows it use advanced graph-search techniques, such as inflation and truncation.

This implementation of ABIT* derives from BIT* which means it benefits from a couple of features not mentioned in the ABIT* publication. It can handle multiple starts, multiple goals, a variety of optimization objectives (e.g., path length), and with just-in-time sampling, infinite problem domains. Note that for some of optimization objectives, the user must specify a suitable heuristic and that when this heuristic is not specified, it will use the conservative/always admissible zero-heuristic.

This implementation also includes some new advancements, including the ability to prioritize exploration until an initial solution is found (Delayed rewiring), the ability to generate samples only when necessary (Just-in-time sampling), and the ability to periodically remove samples that have yet to be connected to the graph (Sample dropping). With just-in-time sampling, ABIT* can even solve planning problems with infinite state space boundaries, i.e., (-inf, inf).

The inflation and truncation factor update policies of this implementation reflect the policies used to create the experimental results in ABIT*'s publication. Each increasingly dense RGG is searched twice, once with a very high inflation factor (gABITstarSetInitialInflationFactor, 1'000'000 by default), and once with an inflation factor that scales as 1 + inflation_parameter / q, where inflation_parameter is a parameter that scales the inflation factor (gABITstarSetInflationScalingParameter, 10 by default) is and q is the number of samples. Each search is truncated with a truncation factor of 1 + truncation_parameter / q, where truncation_parameter is a parameter that influences the truncation factor (gABITstarSetTruncationScalingParameter, 5 by default) and q is the number of samples again. For more information see ABIT*'s publication.

Associated publication:

M. P. Strub, J. D. Gammell. “Advanced BIT* (ABIT*): Sampling-based planning with advanced graph-search techniques.” in Proceedings of the IEEE international conference on robotics and automation (ICRA), Paris, France, 31 May – 4 Jun. 2020. DOI: arXiv:2002.06589. Video 1: ICRA submission video. Video 2: ICRA presentation video

Definition at line 150 of file ABITstar.h.

The documentation for this class was generated from the following files:

ompl/geometric/planners/informedtrees/ABITstar.h
ompl/geometric/planners/informedtrees/src/ABITstar.cpp

Public Member Functions

Additional Inherited Members

Detailed Description