ompl::geometric::FMT Class Reference

Asymptotically Optimal Fast Marching Tree algorithm developed by L. Janson and M. Pavone. More...

#include <ompl/geometric/planners/fmt/FMT.h>

Inheritance diagram for ompl::geometric::FMT:

Classes
struct	CostIndexCompare
class	Motion
	Representation of a motion. More...
struct	MotionCompare
	Comparator used to order motions in a binary heap. More...

Public Member Functions
	FMT (const base::SpaceInformationPtr &si)
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.
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. More...
void	clear () override
	Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.
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).
void	setNumSamples (const unsigned int numSamples)
	Set the number of states that the planner should sample. The planner will sample this number of states in addition to the initial states. If any of the goal states are not reachable from the randomly sampled states, those goal states will also be added. The default value is 1000.
unsigned int	getNumSamples () const
	Get the number of states that the planner will sample.
void	setNearestK (bool nearestK)
	If nearestK is true, FMT will be run using the Knearest strategy.
bool	getNearestK () const
	Get the state of the nearestK strategy.
void	setRadiusMultiplier (const double radiusMultiplier)
	The planner searches for neighbors of a node within a cost r, where r is the value described for FMT* in Section 4 of [L. Janson, E. Schmerling, A. Clark, M. Pavone. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research, 34(7):883-921, 2015](https://arxiv.org/pdf/1306.3532.pdf). For guaranteed asymptotic convergence, the user should choose a constant multiplier for the search radius that is greater than one. The default value is 1.1. In general, a radius multiplier between 0.9 and 5 appears to perform the best.
double	getRadiusMultiplier () const
	Get the multiplier used for the nearest neighbors search radius.
void	setFreeSpaceVolume (const double freeSpaceVolume)
	Store the volume of the obstacle-free configuration space. If no value is specified, the default assumes an obstacle-free unit hypercube, freeSpaceVolume = (maximumExtent/sqrt(dimension))^(dimension)
double	getFreeSpaceVolume () const
	Get the volume of the free configuration space that is being used by the planner.
void	setCacheCC (bool ccc)
	Sets the collision check caching to save calls to the collision checker with slightly memory usage as a counterpart.
bool	getCacheCC () const
	Get the state of the collision check caching.
void	setHeuristics (bool h)
	Activates the cost to go heuristics when ordering the heap.
bool	getHeuristics () const
	Returns true if the heap is ordered taking into account cost to go heuristics.
void	setExtendedFMT (bool e)
	Activates the extended FMT*: adding new samples if planner does not finish successfully.
bool	getExtendedFMT () const
	Returns true if the extended FMT* is activated.
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.

Protected Types
using	MotionBinHeap = ompl::BinaryHeap< Motion *, MotionCompare >
	A binary heap for storing explored motions in cost-to-come sorted order.

Protected Member Functions
double	distanceFunction (const Motion *a, const Motion *b) const
	Compute the distance between two motions as the cost between their contained states. Note that for computationally intensive cost functions, the cost between motions should be stored to avoid duplicate calculations.
void	freeMemory ()
	Free the memory allocated by this planner.
void	sampleFree (const ompl::base::PlannerTerminationCondition &ptc)
	Sample a state from the free configuration space and save it into the nearest neighbors data structure.
void	assureGoalIsSampled (const ompl::base::GoalSampleableRegion *goal)
	For each goal region, check to see if any of the sampled states fall within that region. If not, add a goal state from that region directly into the set of vertices. In this way, FMT is able to find a solution, if one exists. If no sampled nodes are within a goal region, there would be no way for the algorithm to successfully find a path to that region.
double	calculateUnitBallVolume (unsigned int dimension) const
	Compute the volume of the unit ball in a given dimension.
double	calculateRadius (unsigned int dimension, unsigned int n) const
	Calculate the radius to use for nearest neighbor searches, using the bound given in [L. Janson, E. Schmerling, A. Clark, M. Pavone. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research, 34(7):883-921, 2015](https://arxiv.org/pdf/1306.3532.pdf). The radius depends on the radiusMultiplier parameter, the volume of the free configuration space, the volume of the unit ball in the current dimension, and the number of nodes in the graph.
void	saveNeighborhood (Motion *m)
	Save the neighbors within a neighborhood of a given state. The strategy used (nearestK or nearestR depends on the planner configuration.
void	traceSolutionPathThroughTree (Motion *goalMotion)
	Trace the path from a goal state back to the start state and save the result as a solution in the Problem Definiton.
bool	expandTreeFromNode (Motion **z)
	Complete one iteration of the main loop of the FMT* algorithm: Find K nearest nodes in set Unvisited (or within a radius r) of the node z. Attempt to connect them to their optimal cost-to-come parent in set Open. Remove all newly connected nodes fromUnvisited and insert them into Open. Remove motion z from Open, and update z to be the current lowest cost-to-come node in Open.
void	updateNeighborhood (Motion *m, std::vector< Motion * > nbh)
	For a motion m, updates the stored neighborhoods of all its neighbors by by inserting m (maintaining the cost-based sorting). Computes the nearest neighbors if there is no stored neighborhood.
Motion *	getBestParent (Motion *m, std::vector< Motion * > &neighbors, base::Cost &cMin)
	Returns the best parent and the connection cost in the neighborhood of a motion m.
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
MotionBinHeap	Open_
	A binary heap for storing explored motions in cost-to-come sorted order. The motions in Open have been explored, yet are still close enough to the frontier of the explored set Open to be connected to nodes in the unexplored set Unvisited.
std::map< Motion *, std::vector< Motion * > >	neighborhoods_
	A map linking a motion to all of the motions within a distance r of that motion.
unsigned int	numSamples_ {1000u}
	The number of samples to use when planning.
unsigned int	collisionChecks_ {0u}
	Number of collision checks performed by the algorithm.
bool	nearestK_ {true}
	Flag to activate the K nearest neighbors strategy.
bool	cacheCC_ {true}
	Flag to activate the collision check caching.
bool	heuristics_ {false}
	Flag to activate the cost to go heuristics.
double	NNr_
	Radius employed in the nearestR strategy.
unsigned int	NNk_
	K used in the nearestK strategy.
double	freeSpaceVolume_
	The volume of the free configuration space, computed as an upper bound with 95% confidence.
double	radiusMultiplier_ {1.1}
	This planner uses a nearest neighbor search radius proportional to the lower bound for optimality derived for FMT* in Section 4 of [L. Janson, E. Schmerling, A. Clark, M. Pavone. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research, 34(7):883-921, 2015](https://arxiv.org/pdf/1306.3532.pdf). The radius multiplier is the multiplier for the lower bound. For guaranteed asymptotic convergence, the user should choose a multiplier for the search radius that is greater than one. The default value is 1.1. In general, a radius between 0.9 and 5 appears to perform the best.
std::shared_ptr< NearestNeighbors< Motion * > >	nn_
	A nearest-neighbor datastructure containing the set of all motions.
base::StateSamplerPtr	sampler_
	State sampler.
base::OptimizationObjectivePtr	opt_
	The cost objective function.
Motion *	lastGoalMotion_
	The most recent goal motion. Used for PlannerData computation.
base::State *	goalState_
	Goal state caching to accelerate cost to go heuristic computation.
bool	extendedFMT_ {true}
	Add new samples if the tree was not able to find a solution.
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.

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.

Detailed Description

Asymptotically Optimal Fast Marching Tree algorithm developed by L. Janson and M. Pavone.

Short description

FMT* is an asymptotically-optimal sampling-based motion planning algorithm, which is guaranteed to converge to a shortest path solution. The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. The FMT* algorithm essentially performs a lazy dynamic programming recursion on a set of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-come space.

Deviation from the paper

The implementation includes a cache in the collision checking since the original algorithm could check the same collision more than once. It increases the memory requirements to O(n logn), but as samples tend to infinity this bound tend to O(n).

It also implements the resampling strategy (extended FMT) included in the BiDirectional FMT* paper.

External documentation

L. Janson, E. Schmerling, A. Clark, M. Pavone. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research, 34(7):883-921, 2015. DOI: 10.1177/0278364915577958
[PDF]

J. A. Starek, J. V. Gomez, E. Schmerling, L. Janson, L. Moreno, and M. Pavone, An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning, in IEEE/RSJ International Conference on Intelligent Robots Systems, 2015. [PDF]

Definition at line 154 of file FMT.h.

Member Function Documentation

solve()

ompl::base::PlannerStatus ompl::geometric::FMT::solve ( const base::PlannerTerminationCondition &  ptc )

overridevirtual

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.

Todo:

Create a PRM-like connection strategy

Implements ompl::base::Planner.

Definition at line 276 of file FMT.cpp.

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

• ompl/geometric/planners/fmt/FMT.h
• ompl/geometric/planners/fmt/src/FMT.cpp