ompl::geometric::ABITstar Class Reference

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="BITstar")
 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
 
Planneroperator= (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 SpaceInformationPtrgetSpaceInformation () const
 Get the space information this planner is using.
 
const ProblemDefinitionPtrgetProblemDefinition () const
 Get the problem definition the planner is trying to solve.
 
ProblemDefinitionPtrgetProblemDefinition ()
 Get the problem definition the planner is trying to solve.
 
const PlannerInputStatesgetPlannerInputStates () 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 PlannerSpecsgetSpecs () 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.
 
ParamSetparams ()
 Get the parameters for this planner.
 
const ParamSetparams () const
 Get the parameters for this planner.
 
const PlannerProgressPropertiesgetPlannerProgressProperties () 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: