Class LazyRRT

Nested Relationships

Nested Types

Inheritance Relationships

Base Type

Class Documentation

class LazyRRT : public ompl::base::Planner

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]

Public Functions

LazyRRT(const base::SpaceInformationPtr &si)

Constructor.

~LazyRRT() override
virtual 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).

virtual 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). If clearQuery() is called, the planner may retain prior datastructures generated from a previous query on a new problem definition. The function terminates if the call to ptc returns true.

virtual void clear() override

Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.

inline void setGoalBias(double goalBias)

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.

inline double getGoalBias() const

Get the goal bias the planner is using.

inline void setRange(double distance)

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.

inline double getRange() const

Get the range the planner is using.

template<template<typename T> class NN>
inline void setNearestNeighbors()

Set a different nearest neighbors datastructure.

virtual 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.

Protected Functions

void freeMemory()

Free the memory allocated by this planner.

void removeMotion(Motion *motion)

Remove a motion from the tree datastructure.

inline double distanceFunction(const Motion *a, const Motion *b) const

Compute distance between motions (actually distance between contained states)

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.

Motion *lastGoalMotion_ = {nullptr}

The most recent goal motion. Used for PlannerData computation.

class Motion

Representation of a motion.

Public Functions

Motion() = default
inline Motion(const base::SpaceInformationPtr &si)

Constructor that allocates memory for the state.

~Motion() = default

Public Members

base::State *state = {nullptr}

The state contained by the motion.

Motion *parent = {nullptr}

The parent motion in the exploration tree.

bool valid = {false}

Flag indicating whether this motion has been validated.

std::vector<Motion*> children

The set of motions that descend from this one.