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). 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.

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](http://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.

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

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.

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().

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.

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.

## Protected Types

typedef ompl::BinaryHeap< Motion *, MotionCompareMotionBinHeap
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](http://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.

MotiongetBestParent (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.

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](http://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.

MotionlastGoalMotion_
The most recent goal motion. Used for PlannerData computation.

base::StategoalState_
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.

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

typedef std::map< std::string, PlannerProgressPropertyPlannerProgressProperties
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 90 of file FMT.h.

## ◆ 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). 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.

The documentation for this class was generated from the following files:
• ompl/geometric/planners/fmt/FMT.h
• ompl/geometric/planners/fmt/src/FMT.cpp