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Hsu Class Reference

The planner of Hsu, Latombe, Motwani (not really an RRT). More...

#include <rrt.h>

Inheritance diagram for Hsu:

RRT List of all members.

Public Methods

 Hsu (Problem *p)
virtual ~Hsu ()

Public Attributes

double NeighborFactor
double NeighborRadius
double StepSize

Protected Methods

virtual bool Extend (const vector &x, GRAPH<vector,vector> &g, node &nn, bool forward)
bool Connect (const vector &x1, const vector &x2)
node SelectNode (const GRAPH<vector,vector> &g)
vector ChooseNearbyState (const vector &x)
void UpdateWeights (const node &nn, const GRAPH<vector,vector> &g)

Protected Attributes

node_map<double> InverseWeights
double WeightScale

Detailed Description

The planner of Hsu, Latombe, Motwani (not really an RRT).

Grow a tree incrementally by simply selecting each vertex based probabilistically on the number of vertices within the neighborhood of each vertex. This method was proposed by Hsu, Latombe, and Motwani. Although derived from RRT, it is not really a Rapidly-exploring Random Tree since there is no random sampling over the state space to "pull" the tree.


Constructor & Destructor Documentation

Hsu::Hsu ( Problem * p )
 

Hsu::~Hsu ( ) [inline, virtual]
 


Member Function Documentation

vector Hsu::ChooseNearbyState ( const vector & x ) [protected]
 

bool Hsu::Connect ( const vector & x1,
const vector & x2 ) [protected]
 

bool Hsu::Extend ( const vector & x,
GRAPH< vector,vector >& g,
node & nn,
bool forward = true ) [protected, virtual]
 

Reimplemented from RRT.

node Hsu::SelectNode ( const GRAPH< vector,vector >& g ) [protected]
 

void Hsu::UpdateWeights ( const node & nn,
const GRAPH< vector,vector >& g ) [protected]
 


Member Data Documentation

node_map< double > Hsu::InverseWeights [protected]
 

double Hsu::NeighborFactor
 

double Hsu::NeighborRadius
 

double Hsu::StepSize
 

double Hsu::WeightScale [protected]
 


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


Web page maintained by Steve LaValle
Partial support provided by NSF CAREER Award IRI-970228 (LaValle), Honda Research, and Iowa State University.
Contributors: Peng Cheng, James Kuffner, Steve LaValle, and Libo Yang.