ompl::geometric::LazyRRT Class Reference

Lazy RRT. More...

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

Inheritance diagram for ompl::geometric::LazyRRT:

## Classes

class  Motion
Representation of a motion. More...

## Public Member Functions

LazyRRT (const base::SpaceInformationPtr &si)
Constructor.

void getPlannerData (base::PlannerData &data) const override
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).

base::PlannerStatus solve (const base::PlannerTerminationCondition &ptc) override
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.

void clear () override
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 biasing. More...

double getGoalBias () const
Get the goal bias the planner is using.

void setRange (double distance)
Set the range the planner is supposed to use. More...

double getRange () const
Get the range the planner is using.

template<template< typename T > class NN>
void setNearestNeighbors ()
Set a different nearest neighbors datastructure.

void setup () override
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.

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

Planneroperator= (const Planner &)=delete

Planner (SpaceInformationPtr si, std::string name)
Constructor.

virtual ~Planner ()=default
Destructor.

template<class T >
T * as ()
Cast this instance to a desired type. More...

template<class T >
const T * as () const
Cast this instance to a desired type. More...

const SpaceInformationPtrgetSpaceInformation () const
Get the space information this planner is using.

const ProblemDefinitionPtrgetProblemDefinition () const
Get the problem definition the planner is trying to solve.

const PlannerInputStatesgetPlannerInputStates () const
Get the planner input states.

virtual void setProblemDefinition (const ProblemDefinitionPtr &pdef)
Set the problem definition for the planner. The problem needs to be set before calling solve(). Note: If this problem definition replaces a previous one, it may also be necessary to call clear().

PlannerStatus solve (const PlannerTerminationConditionFn &ptc, double checkInterval)
Same as above except the termination condition is only evaluated at a specified interval.

PlannerStatus solve (double solveTime)
Same as above except the termination condition is solely a time limit: the number of seconds the algorithm is allowed to spend planning.

const std::string & getName () const
Get the name of the planner.

void setName (const std::string &name)
Set the name of the planner.

const PlannerSpecsgetSpecs () const
Return the specifications (capabilities of this planner)

virtual void checkValidity ()
Check to see if the planner is in a working state (setup has been called, a goal was set, the input states seem to be in order). In case of error, this function throws an exception.

bool isSetup () const
Check if setup() was called for this planner.

ParamSetparams ()
Get the parameters for this planner.

const ParamSetparams () const
Get the parameters for this planner.

const PlannerProgressPropertiesgetPlannerProgressProperties () const
Retrieve a planner's planner progress property map.

virtual void printProperties (std::ostream &out) const
Print properties of the motion planner.

virtual void printSettings (std::ostream &out) const
Print information about the motion planner's settings.

## Protected Member Functions

void freeMemory ()
Free the memory allocated by this planner.

void removeMotion (Motion *motion)
Remove a motion from the tree datastructure.

double distanceFunction (const Motion *a, const Motion *b) const
Compute distance between motions (actually distance between contained states)

Protected Member Functions inherited from ompl::base::Planner
template<typename T , typename PlannerType , typename SetterType , typename GetterType >
void declareParam (const std::string &name, const PlannerType &planner, const SetterType &setter, const GetterType &getter, const std::string &rangeSuggestion="")
This function declares a parameter for this planner instance, and specifies the setter and getter functions.

template<typename T , typename PlannerType , typename SetterType >
void declareParam (const std::string &name, const PlannerType &planner, const SetterType &setter, const std::string &rangeSuggestion="")
This function declares a parameter for this planner instance, and specifies the setter function.

void addPlannerProgressProperty (const std::string &progressPropertyName, const PlannerProgressProperty &prop)
Add a planner progress property called progressPropertyName with a property querying function prop to this planner's progress property map.

## Protected Attributes

base::StateSamplerPtr sampler_
State sampler.

std::shared_ptr< NearestNeighbors< Motion * > > nn_
A nearest-neighbors datastructure containing the tree of motions.

double goalBias_ {.05}
The fraction of time the goal is picked as the state to expand towards (if such a state is available)

double maxDistance_ {0.}
The maximum length of a motion to be added to a tree.

RNG rng_
The random number generator.

MotionlastGoalMotion_ {nullptr}
The most recent goal motion. Used for PlannerData computation.

Protected Attributes inherited from ompl::base::Planner
SpaceInformationPtr si_
The space information for which planning is done.

ProblemDefinitionPtr pdef_
The user set problem definition.

PlannerInputStates pis_
Utility class to extract valid input states.

std::string name_
The name of this planner.

PlannerSpecs specs_
The specifications of the planner (its capabilities)

ParamSet params_
A map from parameter names to parameter instances for this planner. This field is populated by the declareParam() function.

PlannerProgressProperties plannerProgressProperties_
A mapping between this planner's progress property names and the functions used for querying those progress properties.

bool setup_
Flag indicating whether setup() has been called.

Public Types inherited from ompl::base::Planner
typedef std::function< std::string()> PlannerProgressProperty
Definition of a function which returns a property about the planner's progress that can be queried by a benchmarking routine.

typedef std::map< std::string, PlannerProgressPropertyPlannerProgressProperties
A dictionary which maps the name of a progress property to the function to be used for querying that property.

## Detailed Description

Lazy RRT.

Short description
RRT is a tree-based motion planner that uses the following idea: RRT samples a random state qr in the state space, then finds the state qc among the previously seen states that is closest to qr and expands from qc towards qr, until a state qm is reached. qm is then added to the exploration tree. The difference between RRT and LazyRRT is that when moving towards the new state qm, LazyRRT does not check to make sure the path is valid. Instead, it is optimistic and attempts to find a path as soon as possible. Once a path is found, it is checked for collision. If collisions are found, the invalid path segments are removed and the search process is continued.
External documentation
• J. Kuffner and S.M. LaValle, RRT-connect: An efficient approach to single-query path planning, in Proc. 2000 IEEE Intl. Conf. on Robotics and Automation, pp. 995–1001, Apr. 2000. DOI: 10.1109/ROBOT.2000.844730
[PDF] [more]
• R. Bohlin and L.E. Kavraki, A Randomized Algorithm for Robot Path Planning Based on Lazy Evaluation, in Handbook on Randomized Computing, pp. 221–249, 2001.
[PDF]
• R. Bohlin and L.E. Kavraki, Path planning using lazy PRM, in Proc. 2000 IEEE Intl. Conf. on Robotics and Automation, pp. 521–528, 2000. DOI: 10.1109/ROBOT.2000.844107
[PDF]

Definition at line 81 of file LazyRRT.h.

## ◆ setGoalBias()

 void ompl::geometric::LazyRRT::setGoalBias ( double goalBias )
inline

Set the goal biasing.

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 104 of file LazyRRT.h.

## ◆ setRange()

 void ompl::geometric::LazyRRT::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 120 of file LazyRRT.h.

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