Adaptively Informed Trees (AIT*) More...

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

Inheritance diagram for ompl::geometric::AITstar:

Public Member Functions
	AITstar (const ompl::base::SpaceInformationPtr &spaceInformation)
	Constructs a AIT*.

	~AITstar ()=default
	Destructs a AIT*.

void	setup () override
	Additional setup that can only be done once a problem definition is set.

ompl::base::PlannerStatus::StatusType	ensureSetup ()
	Checks whether the planner is successfully setup.

ompl::base::PlannerStatus::StatusType	ensureStartAndGoalStates (const ompl::base::PlannerTerminationCondition &terminationCondition)
	Checks whether the problem is successfully setup.

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

ompl::base::PlannerStatus	solve (const ompl::base::PlannerTerminationCondition &terminationCondition) override
	Solves a motion planning problem.

ompl::base::Cost	bestCost () const
	Get the cost of the incumbent solution.

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

void	setBatchSize (std::size_t batchSize)
	Set the batch size.

std::size_t	getBatchSize () const
	Get the batch size.

void	setRewireFactor (double rewireFactor)
	Set the rewire factor of the RGG graph.

double	getRewireFactor () const
	Get the rewire factor of the RGG graph.

void	trackApproximateSolutions (bool track)
	Set whether to track approximate solutions.

bool	areApproximateSolutionsTracked () const
	Get whether approximate solutions are tracked.

void	enablePruning (bool prune)
	Set whether pruning is enabled or not.

bool	isPruningEnabled () const
	Get whether pruning is enabled or not.

void	setUseKNearest (bool useKNearest)
	Set whether to use a k-nearest RGG connection model. If false, AIT* uses an r-disc model.

bool	getUseKNearest () const
	Get whether to use a k-nearest RGG connection model. If false, AIT* uses an r-disc model.

void	setMaxNumberOfGoals (unsigned int numberOfGoals)
	Set the maximum number of goals AIT* will sample from sampleable goal regions.

unsigned int	getMaxNumberOfGoals () const
	Get the maximum number of goals AIT* will sample from sampleable goal regions.

std::vector< aitstar::Edge >	getEdgesInQueue () const
	Get the edge queue.

std::vector< std::shared_ptr< aitstar::Vertex > >	getVerticesInQueue () const
	Get the vertex queue.

aitstar::Edge	getNextEdgeInQueue () const
	Get the next edge in the queue.

std::shared_ptr< aitstar::Vertex >	getNextVertexInQueue () const
	Get the next vertex in the queue.

std::vector< std::shared_ptr< aitstar::Vertex > >	getVerticesInReverseSearchTree () const
	Get the vertices in the reverse search tree.

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::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

Adaptively Informed Trees (AIT*)

AIT* (Adaptively Informed Trees) is an almost-surely asymptotically optimal path planner. It aims to find an initial solution quickly and asymptotically converge to the globally optimal solution.

AIT* views the planning problem as the two subproblems of approximation and search, and approximates the free state space with an increasingly dense, edge-implicit random geometric graph (RGG), similar to BIT* and ABIT*.

AIT* uses an asymmetric bidirectional search that simultaneously estimates and exploits an accurate, adaptive heuristic that is specific to each problem instance. The reverse search (goal to start) does not perform collision detection on the edges and estimates cost-to-go values for all states in the RGG that are processed with the forward search (start to goal) which does perform collision detection. Because AIT* uses LPA* as the reverse search algorithm, the heuristic is admissible in the context of the current RGG and can efficiently be updated when the forward search detects a collision on an edge that is in the reverse search tree (as this indicates a flawed heuristic).

This implementation of AIT* can solve problems with multiple start and goal states and supports adding start and goal states while the planner is running. One can also turn off repairing the reverse search tree upon collision detection, which might be beneficial for problems in which collision detection is computationally inexpensive.

Associated publication:

M. P. Strub, J. D. Gammell. “Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics” 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 151 of file AITstar.h.

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

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

Public Member Functions

Additional Inherited Members

Detailed Description