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 | |
| InformedSampler & | operator= (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 117 of file RejectionInfSampler.h.
The documentation for this class was generated from the following files:
- ompl/base/samplers/informed/RejectionInfSampler.h
- ompl/base/samplers/informed/src/RejectionInfSampler.cpp