ompl::geometric::SST Class Reference

#include <ompl/geometric/planners/sst/SST.h>

Inheritance diagram for ompl::geometric::SST:

## Classes

class  Motion
Representation of a motion. More...

class  Witness

## Public Member Functions

SST (const base::SpaceInformationPtr &si)
Constructor.

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
Continue solving for some amount of time. Return true if solution was found.

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 clear () override
Clear datastructures. Call this function if the input data to the planner has changed and you do not want to continue planning.

void setGoalBias (double goalBias)

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.

Set the radius for selecting nodes relative to random sample. More...

double getSelectionRadius () const
Get the selection radius the planner is using.

Set the radius for pruning nodes. More...

double getPruningRadius () const
Get the pruning radius the planner is using.

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

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 Member Functions

MotionselectNode (Motion *sample)
Finds the best node in the tree withing the selection radius around a random sample.

WitnessfindClosestWitness (Motion *node)
Find the closest witness node to a newly generated potential node.

base::StatemonteCarloProp (Motion *m)
Randomly propagate a new edge.

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)

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

base::StateSamplerPtr sampler_
State sampler.

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

std::shared_ptr< NearestNeighbors< Motion * > > witnesses_
A nearest-neighbors datastructure containing the tree of witness 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_ {5.}
The maximum length of a motion to be added to a tree.

The radius for determining the node selected for extension.

The radius for determining the size of the pruning region.

RNG rng_
The random number generator.

std::vector< base::State * > prevSolution_
The best solution we found so far.

base::Cost prevSolutionCost_
The best solution cost we found so far.

base::OptimizationObjectivePtr opt_
The optimization objective.

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
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

Short description
SST (Stable Sparse RRT) is an asymptotically near-optimal incremental sampling-based motion planning algorithm. It is recommended for geometric problems to use an alternative method that makes use of a steering function. Using SST for geometric problems does not take advantage of this function.
External documentation
Yanbo Li, Zakary Littlefield, Kostas E. Bekris, Sampling-based Asymptotically Optimal Sampling-based Kinodynamic Planning. [PDF]

Definition at line 59 of file SST.h.

## ◆ setGoalBias()

 void ompl::geometric::SST::setGoalBias ( double goalBias )
inline

In the process of randomly selecting states in the state space to attempt to go towards, the algorithm may in fact choose the actual goal state, if it knows it, with some probability. This probability is a real number between 0.0 and 1.0; its value should usually be around 0.05 and should not be too large. It is probably a good idea to use the default value.

Definition at line 86 of file SST.h.

inline

Set the radius for pruning nodes.

This is the radius used to surround nodes in the witness set. Within this radius around a state in the witness set, only one active tree node can exist. This limits the size of the tree and forces computation to focus on low path costs nodes. If this value is too large, narrow passages will be impossible to traverse. In addition, children nodes may be removed if they are not at least this distance away from their parent nodes.

Definition at line 142 of file SST.h.

## ◆ setRange()

 void ompl::geometric::SST::setRange ( double distance )
inline

Set the range the planner is supposed to use.

This parameter greatly influences the runtime of the algorithm. It represents the maximum length of a motion to be added in the tree of motions.

Definition at line 102 of file SST.h.

inline

Set the radius for selecting nodes relative to random sample.

This radius is used to mimic behavior of RRT* in that it promotes extending from nodes with good path cost from the root of the tree. Making this radius larger will provide higher quality paths, but has two major drawbacks; exploration will occur much more slowly and exploration around the boundary of the state space may become impossible.

Definition at line 121 of file SST.h.

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