ompl::base::RejectionInfSampler Class Reference

A default rejection sampling scheme that samples uniformly from the entire planning domain. Samples are rejected until one is found that has a heuristic solution estimate that is less than the current solution. In general, direct sampling of the informed subset is much better, but this is a general default. More...

#include <ompl/base/samplers/informed/RejectionInfSampler.h>

Inheritance diagram for ompl::base::RejectionInfSampler:

Public Member Functions

 RejectionInfSampler (const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls)
 Construct a rejection sampler that only generates states with a heuristic solution estimate that is less than the cost of the current solution.
 
bool sampleUniform (State *statePtr, const Cost &maxCost) override
 Sample uniformly in the subset of the state space whose heuristic solution estimates are less than the provided cost, i.e. in the interval [0, maxCost). Returns false if such a state was not found in the specified number of iterations.
 
bool sampleUniform (State *statePtr, const Cost &minCost, const Cost &maxCost) override
 Sample uniformly in the subset of the state space whose heuristic solution estimates are between the provided costs, [minCost, maxCost). Returns false if such a state was not found in the specified number of iterations.
 
bool hasInformedMeasure () const override
 Whether the sampler can provide a measure of the informed subset.
 
double getInformedMeasure (const Cost &) const override
 The measure of the subset of the state space defined by the current solution cost that is being searched. As rejection sampling has no closed-form knowledge of the informed subset, the measure of the informed space is always the measure of the entire space.
 
double getInformedMeasure (const Cost &, const Cost &) const override
 The measure of the subset of the state space defined by the current solution cost that is being searched. As rejection sampling has no closed-form knowledge of the informed subset, the measure of the informed space is always the measure of the entire space.
 
- Public Member Functions inherited from ompl::base::InformedSampler
 InformedSampler (const InformedSampler &)=delete
 
InformedSampleroperator= (const InformedSampler &)=delete
 
 InformedSampler (const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls)
 Construct a sampler that only generates states with a heuristic solution estimate that is less than the cost of the current solution. Requires a function pointer to a method to query the cost of the current solution. If iteration is required, only maxNumberCalls are attempted, to assure that the function returns.
 
virtual Cost heuristicSolnCost (const State *statePtr) const
 A helper function to calculate the heuristic estimate of the solution cost for a given state using the optimization objective stored in the problem definition. More...
 
ProblemDefinitionPtr getProblemDefn () const
 
unsigned int getMaxNumberOfIters () const
 

Additional Inherited Members

- Protected Attributes inherited from ompl::base::InformedSampler
ProblemDefinitionPtr probDefn_
 A copy of the problem definition.
 
OptimizationObjectivePtr opt_
 A copy of the optimization objective.
 
StateSpacePtr space_
 A copy of the state space.
 
unsigned int numIters_
 The number of iterations I'm allowed to attempt.
 

Detailed Description

A default rejection sampling scheme that samples uniformly from the entire planning domain. Samples are rejected until one is found that has a heuristic solution estimate that is less than the current solution. In general, direct sampling of the informed subset is much better, but this is a general default.

Definition at line 53 of file RejectionInfSampler.h.


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