ompl::geometric::LBTRRT Class Reference

Lower Bound Tree Rapidly-exploring Random Trees. More...

#include <ompl/geometric/planners/rrt/LBTRRT.h>

Inheritance diagram for ompl::geometric::LBTRRT:

List of all members.

Classes

struct  CostCompare
class  Motion
 Representation of a motion. More...

Public Member Functions

 LBTRRT (const base::SpaceInformationPtr &si)
 Constructor.
virtual void getPlannerData (base::PlannerData &data) const
 Get information about the current run of the motion planner. Repeated calls to this function will update data (only additions are made). This is useful to see what changed in the exploration datastructure, between calls to solve(), for example (without calling clear() in between).
virtual base::PlannerStatus solve (const base::PlannerTerminationCondition &ptc)
 Function that can solve the motion planning problem. This function can be called multiple times on the same problem, without calling clear() in between. This allows the planner to continue work for more time on an unsolved problem, for example. If this option is used, it is assumed the problem definition is not changed (unpredictable results otherwise). The only change in the problem definition that is accounted for is the addition of starting or goal states (but not changing previously added start/goal states). The function terminates if the call to ptc returns true.
virtual void clear ()
 Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.
void setGoalBias (double goalBias)
 Set the goal bias.
double getGoalBias () const
 Get the goal bias the planner is using.
void setRange (double distance)
 Set the range the planner is supposed to use.
double getRange () const
 Get the range the planner is using.
template<template< typename T > class NN>
void setNearestNeighbors ()
 Set a different nearest neighbors datastructure.
virtual void setup ()
 Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceInformation::setup() if needed. This must be called before solving.
void setApproximationFactor (double epsilon)
 Set the apprimation factor.
double getApproximationFactor () const
 Get the apprimation factor.
std::string getIterationCount () const
std::string getBestCost () const

Protected Member Functions

bool attemptNodeUpdate (Motion *potentialParent, Motion *child)
 attempt to rewire the trees
void updateChildCostsLb (Motion *m)
 update the child cost of the lower bound tree
void updateChildCostsApx (Motion *m)
 update the child cost of the approximation tree
void removeFromParentLb (Motion *m)
 remove motion from its parent in the lower bound tree
void removeFromParentApx (Motion *m)
 remove motion from its parent in the approximation tree
void removeFromParent (const Motion *m, std::vector< Motion * > &vec)
 remove motion from a vector
void freeMemory ()
 Free the memory allocated by this planner.
double distanceFunction (const Motion *a, const Motion *b) const
 Compute distance between motions (actually distance between contained states)
base::Cost costFunction (const Motion *a, const Motion *b) const

Protected Attributes

base::StateSamplerPtr sampler_
 State sampler.
boost::shared_ptr
< NearestNeighbors< Motion * > > 
nn_
 A nearest-neighbors datastructure containing the tree of motions.
double goalBias_
 The fraction of time the goal is picked as the state to expand towards (if such a state is available)
double maxDistance_
 The maximum length of a motion to be added to a tree.
double epsilon_
 approximation factor
RNG rng_
 The random number generator.
base::OptimizationObjectivePtr opt_
 Objective we're optimizing.
MotionlastGoalMotion_
 The most recent goal motion. Used for PlannerData computation.
std::vector< Motion * > goalMotions_
 A list of states in the tree that satisfy the goal condition.
unsigned int iterations_
 Number of iterations the algorithm performed.
base::Cost bestCost_
 Best cost found so far by algorithm.

Static Protected Attributes

static const double kRRG = 5.5
 kRRG = 2e~5.5 is a valid choice for all problem instances

Detailed Description

Lower Bound Tree Rapidly-exploring Random Trees.

Short description
LBTRRT (Lower Bound Tree RRT) is a near asymptotically-optimal incremental sampling-based motion planning algorithm. LBTRRT algorithm is guaranteed to converge to a solution that is within a constant factor of the optimal solution. The notion of optimality is with respect to the distance function defined on the state space we are operating on.
External documentation
O. Salzman and D. Halperin, Sampling-based Asymptotically near-optimal RRT for fast, high-quality, motion planning, 2013. [[PDF]](http://arxiv.org/abs/1308.0189)

Definition at line 68 of file LBTRRT.h.


Member Function Documentation

void ompl::geometric::LBTRRT::setGoalBias ( double  goalBias) [inline]

Set the goal bias.

In the process of randomly selecting states in the state space to attempt to go towards, the algorithm may in fact choose the actual goal state, if it knows it, with some probability. This probability is a real number between 0.0 and 1.0; its value should usually be around 0.05 and should not be too large. It is probably a good idea to use the default value.

Definition at line 92 of file LBTRRT.h.

void ompl::geometric::LBTRRT::setRange ( double  distance) [inline]

Set the range the planner is supposed to use.

This parameter greatly influences the runtime of the algorithm. It represents the maximum length of a motion to be added in the tree of motions.

Definition at line 108 of file LBTRRT.h.


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