Informed RRT*. More...

#include <ompl/geometric/planners/rrt/InformedRRTstar.h>

Inheritance diagram for ompl::geometric::InformedRRTstar:

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Public Member Functions
	InformedRRTstar (const base::SpaceInformationPtr &si)
	Constructor.

Public Member Functions inherited from ompl::geometric::RRTstar
	RRTstar (const base::SpaceInformationPtr &si)

void	getPlannerData (base::PlannerData &data) const override
	Get information about the current run of the motion planner. Repeated calls to this function will update data (only additions are made). This is useful to see what changed in the exploration datastructure, between calls to solve(), for example (without calling clear() in between).

base::PlannerStatus	solve (const base::PlannerTerminationCondition &ptc) override
	Function that can solve the motion planning problem. This function can be called multiple times on the same problem, without calling clear() in between. This allows the planner to continue work for more time on an unsolved problem, for example. If this option is used, it is assumed the problem definition is not changed (unpredictable results otherwise). The only change in the problem definition that is accounted for is the addition of starting or goal states (but not changing previously added start/goal states). If clearQuery() is called, the planner may retain prior datastructures generated from a previous query on a new problem definition. The function terminates if the call to ptc returns true.

void	clear () override
	Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.

void	setup () override
	Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceInformation::setup() if needed. This must be called before solving.

void	setGoalBias (double goalBias)
	Set the goal bias. More...

double	getGoalBias () const
	Get the goal bias the planner is using.

void	setRange (double distance)
	Set the range the planner is supposed to use. More...

double	getRange () const
	Get the range the planner is using.

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

double	getRewireFactor () const
	Set the rewiring scale factor, s, such that r_rrg = s \times r_rrg* > r_rrg* (or k_rrg = s \times k_rrg* > k_rrg*)

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

void	setDelayCC (bool delayCC)
	Option that delays collision checking procedures. When it is enabled, all neighbors are sorted by cost. The planner then goes through this list, starting with the lowest cost, checking for collisions in order to find a parent. The planner stops iterating through the list when a collision free parent is found. This prevents the planner from collision checking each neighbor, reducing computation time in scenarios where collision checking procedures are expensive.

bool	getDelayCC () const
	Get the state of the delayed collision checking option.

void	setTreePruning (bool prune)
	Controls whether the tree is pruned during the search. This pruning removes a vertex if and only if it and all its descendents passes the pruning condition. The pruning condition is whether the lower-bounding estimate of a solution constrained to pass the the vertex is greater than the current solution. Considering the descendents of a vertex prevents removing a descendent that may actually be capable of later providing a better solution once its incoming path passes through a different vertex (e.g., a change in homotopy class).

bool	getTreePruning () const
	Get the state of the pruning option.

void	setPruneThreshold (const double pp)
	Set the fractional change in solution cost necessary for pruning to occur, i.e., prune if the new solution is at least X% better than the old solution. (e.g., 0.0 will prune after every new solution, while 1.0 will never prune.)

double	getPruneThreshold () const
	Get the current prune states percentage threshold parameter.

void	setPrunedMeasure (bool informedMeasure)
	Use the measure of the pruned subproblem instead of the measure of the entire problem domain (if such an expression exists and a solution is present). Currently the only method to calculate this measure in closed-form is through a informed sampler, so this option also requires that.

bool	getPrunedMeasure () const
	Get the state of using the pruned measure.

void	setInformedSampling (bool informedSampling)
	Use direct sampling of the heuristic for the generation of random samples (e.g., x_rand). If a direct sampling method is not defined for the objective, rejection sampling will be used by default.

bool	getInformedSampling () const
	Get the state direct heuristic sampling.

void	setSampleRejection (bool reject)
	Controls whether heuristic rejection is used on samples (e.g., x_rand)

bool	getSampleRejection () const
	Get the state of the sample rejection option.

void	setNewStateRejection (const bool reject)
	Controls whether heuristic rejection is used on new states before connection (e.g., x_new = steer(x_nearest, x_rand))

bool	getNewStateRejection () const
	Get the state of the new-state rejection option.

void	setAdmissibleCostToCome (const bool admissible)
	Controls whether pruning and new-state rejection uses an admissible cost-to-come estimate or not.

bool	getAdmissibleCostToCome () const
	Get the admissibility of the pruning and new-state rejection heuristic.

void	setOrderedSampling (bool orderSamples)
	Controls whether samples are returned in ordered by the heuristic. This is accomplished by generating a batch at a time.

bool	getOrderedSampling () const
	Get the state of sample ordering.

void	setBatchSize (unsigned int batchSize)
	Set the batch size used for sample ordering.

unsigned int	getBatchSize () const
	Get the batch size used for sample ordering.

void	setFocusSearch (const bool focus)
	A meta parameter to focusing the search to improving the current solution. This is the parameter set by CFOREST. For RRT*, search focusing consists of pruning the existing search and limiting future search. Specifically, this is accomplished by turning on informed sampling, tree pruning and new-state rejection. This flag individually sets the options described above.

bool	getFocusSearch () const
	Get the state of search focusing.

void	setKNearest (bool useKNearest)
	Use a k-nearest search for rewiring instead of a r-disc search.

bool	getKNearest () const
	Get the state of using a k-nearest search for rewiring.

void	setNumSamplingAttempts (unsigned int numAttempts)
	Set the number of attempts to make while performing rejection or informed sampling.

unsigned int	getNumSamplingAttempts () const
	Get the number of attempts to make while performing rejection or informed sampling.

unsigned int	numIterations () const

ompl::base::Cost	bestCost () const

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::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::RRTstar
void	allocSampler ()
	Create the samplers.

bool	sampleUniform (base::State *statePtr)
	Generate a sample.

void	freeMemory ()
	Free the memory allocated by this planner.

double	distanceFunction (const Motion a, const Motion b) const
	Compute distance between motions (actually distance between contained states)

void	getNeighbors (Motion motion, std::vector< Motion > &nbh) const
	Gets the neighbours of a given motion, using either k-nearest of radius as appropriate.

void	removeFromParent (Motion *m)
	Removes the given motion from the parent's child list.

void	updateChildCosts (Motion *m)
	Updates the cost of the children of this node if the cost up to this node has changed.

int	pruneTree (const base::Cost &pruneTreeCost)
	Prunes all those states which estimated total cost is higher than pruneTreeCost. Returns the number of motions pruned. Depends on the parameter set by setPruneStatesImprovementThreshold()

base::Cost	solutionHeuristic (const Motion *motion) const
	Computes the solution cost heuristically as the cost to come from start to the motion plus the cost to go from the motion to the goal. If the parameter use_admissible_heuristic (setAdmissibleCostToCome()) is true, a heuristic estimate of the cost to come is used; otherwise, the current cost to come to the motion is used (which may overestimate the cost through the motion).

void	addChildrenToList (std::queue< Motion , std::deque< Motion >> motionList, Motion motion)
	Add the children of a vertex to the given list.

bool	keepCondition (const Motion *motion, const base::Cost &threshold) const
	Check whether the given motion passes the specified cost threshold, meaning it will be kept during pruning.

void	calculateRewiringLowerBounds ()
	Calculate the k_RRG* and r_RRG* terms.

std::string	numIterationsProperty () const

std::string	bestCostProperty () const

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::geometric::RRTstar
base::StateSamplerPtr	sampler_
	State sampler.

base::InformedSamplerPtr	infSampler_
	An informed sampler.

std::shared_ptr< NearestNeighbors< Motion * > >	nn_
	A nearest-neighbors datastructure containing the tree of motions.

double	goalBias_ {.05}
	The fraction of time the goal is picked as the state to expand towards (if such a state is available)

double	maxDistance_ {0.}
	The maximum length of a motion to be added to a tree.

RNG	rng_
	The random number generator.

bool	useKNearest_ {true}
	Option to use k-nearest search for rewiring.

double	rewireFactor_ {1.1}
	The rewiring factor, s, so that r_rrt = s \times r_rrt* > r_rrt* (or k_rrt = s \times k_rrt* > k_rrt*)

double	k_rrt_ {0u}
	A constant for k-nearest rewiring calculations.

double	r_rrt_ {0.}
	A constant for r-disc rewiring calculations.

bool	delayCC_ {true}
	Option to delay and reduce collision checking within iterations.

base::OptimizationObjectivePtr	opt_
	Objective we're optimizing.

Motion *	bestGoalMotion_ {nullptr}
	The best goal motion.

std::vector< Motion * >	goalMotions_
	A list of states in the tree that satisfy the goal condition.

bool	useTreePruning_ {false}
	The status of the tree pruning option.

double	pruneThreshold_ {.05}
	The tree is pruned when the change in solution cost is greater than this fraction.

bool	usePrunedMeasure_ {false}
	Option to use the informed measure.

bool	useInformedSampling_ {false}
	Option to use informed sampling.

bool	useRejectionSampling_ {false}
	The status of the sample rejection parameter.

bool	useNewStateRejection_ {false}
	The status of the new-state rejection parameter.

bool	useAdmissibleCostToCome_ {true}
	The admissibility of the new-state rejection heuristic.

unsigned int	numSampleAttempts_ {100u}
	The number of attempts to make at informed sampling.

bool	useOrderedSampling_ {false}
	Option to create batches of samples and order them.

unsigned int	batchSize_ {1u}
	The size of the batches.

std::vector< Motion * >	startMotions_
	Stores the start states as Motions.

base::Cost	bestCost_ {std::numeric_limits<double>::quiet_NaN()}
	Best cost found so far by algorithm.

base::Cost	prunedCost_ {std::numeric_limits<double>::quiet_NaN()}
	The cost at which the graph was last pruned.

double	prunedMeasure_ {0.}
	The measure of the problem when we pruned it (if this isn't in use, it will be set to si_->getSpaceMeasure())

unsigned int	iterations_ {0u}
	Number of iterations the algorithm performed.

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

Informed RRT*.

Run RRT* with an informed search strategy that uses heuristics to only consider subproblem that could provide a better solution. The search is limited to this subproblem by pruning the graph, generating samples only in this subproblem (directly if available, e.g., path length) and, when available, using the measure of this subproblem to calculate the connection terms (e.g., path length)

Associated publication:

J. D. Gammell, T. D. Barfoot, S. S. Srinivasa, "Informed sampling for asymptotically optimal path planning." IEEE Transactions on Robotics (T-RO), 34(4): 966-984, Aug. 2018. DOI: TRO.2018.2830331. arXiv: 1706.06454 [cs.RO]. Illustration video. Short description video.

Definition at line 131 of file InformedRRTstar.h.

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

ompl/geometric/planners/rrt/InformedRRTstar.h
ompl/geometric/planners/rrt/src/InformedRRTstar.cpp

Public Member Functions

Additional Inherited Members

Detailed Description