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