ompl::base::DeterministicStateSampler Class Reference

An abstract class for the concept of using deterministic sampling sequences to decrease the dispersion of the samples. More...

#include <ompl/base/samplers/DeterministicStateSampler.h>

Inheritance diagram for ompl::base::DeterministicStateSampler:

## Public Types

enum  DeterministicSamplerType { HALTON }

## Public Member Functions

DeterministicStateSampler (const StateSpace *space, DeterministicSamplerType type=DeterministicSamplerType::HALTON)
Constructor, which creates the sequence internally based on the specified sequence type. Uses the default constructor for the sequence.

DeterministicStateSampler (const StateSpace *space, std::shared_ptr< DeterministicSequence > sequence_ptr)
Constructor that takes a pointer to a DeterministicSequence and uses that object instead of its own. This can be used to apply non default options to the deterministic sequence.

virtual void sampleUniform (State *state)
Sample a state.

virtual void sampleUniformNear (State *, const State *, double)
Sample a state near another, within a neighborhood controlled by a distance parameter. More...

virtual void sampleGaussian (State *, const State *, double)
Sample a state using a Gaussian distribution with given mean and standard deviation (stdDev). More...

Public Member Functions inherited from ompl::base::StateSampler
StateSampler (const StateSampler &)=delete

StateSampleroperator= (const StateSampler &)=delete

StateSampler (const StateSpace *space)
Constructor.

## Protected Attributes

std::shared_ptr< DeterministicSequencesequence_ptr_

Protected Attributes inherited from ompl::base::StateSampler
const StateSpacespace_
The state space this sampler samples.

RNG rng_
An instance of a random number generator.

## Detailed Description

An abstract class for the concept of using deterministic sampling sequences to decrease the dispersion of the samples.

Short description
DeterministicStateSampler Implementation of a deterministic state sampler. The implementation allows to load and draw samples from a precomputed sequence or from the Halton Sequence,
External documentation
Dispertio: Optimal Sampling For Safe Deterministic Motion Planning L Palmieri, L Bruns, M Meurer, KO Arras - IEEE Robotics and Automation Letters, 2019 DOI: 10.1109/LRA.2019.2958525
[PDF]

Definition at line 64 of file DeterministicStateSampler.h.

## ◆ sampleGaussian()

 virtual void ompl::base::DeterministicStateSampler::sampleGaussian ( State * state, const State * mean, double stdDev )
inlinevirtual

Sample a state using a Gaussian distribution with given mean and standard deviation (stdDev).

As with sampleUniform, the implementation of sampleGaussian is specific to the derived class and few assumptions can be made about the distance between state and mean.

Implements ompl::base::StateSampler.

Definition at line 93 of file DeterministicStateSampler.h.

## ◆ sampleUniformNear()

 virtual void ompl::base::DeterministicStateSampler::sampleUniformNear ( State * state, const State * near, double distance )
inlinevirtual

Sample a state near another, within a neighborhood controlled by a distance parameter.

Typically, StateSampler-derived classes will return in state a state that is uniformly distributed within a ball with radius distance defined by the distance function from the corresponding state space. However, this is not guaranteed. For example, the default state sampler for the RealVectorStateSpace returns samples uniformly distributed using L_inf distance, while the default distance function is L_2 distance.

Implements ompl::base::StateSampler.

Definition at line 89 of file DeterministicStateSampler.h.

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