ompl::geometric::RRTstar Class Reference

Optimal Rapidly-exploring Random Trees. More...

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

Inheritance diagram for ompl::geometric::RRTstar:

Classes

struct  CostIndexCompare
 
class  Motion
 Representation of a motion. More...
 
struct  PruneScratchSpace
 

Public Member Functions

 RRTstar (const base::SpaceInformationPtr &si)
 
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. 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 setDelayCC (bool delayCC)
 Option that delays collision checking procedures. When it is enabled, all neighbors are sorted by cost. The planner then goes through this list, starting with the lowest cost, checking for collisions in order to find a parent. The planner stops iterating through the list when a collision free parent is found. This prevents the planner from collsion checking each neighbor, reducing computation time in scenarios where collision checking procedures are expensive.
 
bool getDelayCC () const
 Get the state of the delayed collision checking option.
 
void setPrune (const bool prune)
 Controls whether the tree is pruned during the search.
 
bool getPrune () const
 Get the state of the pruning option.
 
void setPruneStatesImprovementThreshold (const double pp)
 Set the percentage threshold (between 0 and 1) for pruning the tree. If the new tree has removed at least this percentage of states, the tree will be finally pruned.
 
double getPruneStatesImprovementThreshold () const
 Get the current prune states percentage threshold parameter.
 
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.
 
std::string getIterationCount () const
 
std::string getBestCost () const
 
- Public Member Functions inherited from ompl::base::Planner
 Planner (const SpaceInformationPtr &si, const std::string &name)
 Constructor.
 
virtual ~Planner ()
 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.
 
double distanceFunction (const Motion *a, const Motion *b) const
 Compute distance between motions (actually distance between contained states)
 
void removeFromParent (Motion *m)
 Removes the given motion from the parent's child list.
 
void updateChildCosts (Motion *m)
 Updates the cost of the children of this node if the cost up to this node has changed.
 
int pruneTree (const base::Cost pruneTreeCost)
 Prunes all those states which estimated total cost is higher than pruneTreeCost. Returns the number of motions pruned. Depends on the parameter set by setPruneStatesImprovementThreshold()
 
void deleteBranch (Motion *motion)
 Deletes (frees memory) the motion and its children motions.
 
base::Cost costToGo (const Motion *motion, const bool shortest=true) const
 Computes the Cost To Go heuristically as the cost to come from start to motion plus the cost to go from motion to goal. If shortest is true, the estimated cost to come start-motion is given. Otherwise, this cost to come is the current motion cost.
 
- 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.
 
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.
 
RNG rng_
 The random number generator.
 
bool delayCC_
 Option to delay and reduce collision checking within iterations.
 
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.
 
bool prune_
 If this value is set to true, tree pruning will be enabled.
 
double pruneStatesThreshold_
 The tree is only pruned is the percentage of states to prune is above this threshold (between 0 and 1).
 
struct ompl::geometric::RRTstar::PruneScratchSpace pruneScratchSpace_
 
MotionstartMotion_
 Stores the Motion containing the last added initial start state.
 
unsigned int iterations_
 Number of iterations the algorithm performed.
 
base::Cost bestCost_
 Best cost found so far by algorithm.
 
- 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.
 

Additional Inherited Members

- Public Types inherited from ompl::base::Planner
typedef boost::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

Optimal Rapidly-exploring Random Trees.

Short description
RRT* (optimal RRT) is an asymptotically-optimal incremental sampling-based motion planning algorithm. RRT* algorithm is guaranteed to converge to an optimal solution, while its running time is guaranteed to be a constant factor of the running time of the RRT. The notion of optimality is with respect to the distance function defined on the state space we are operating on. See ompl::base::Goal::setMaximumPathLength() for how to set the maximally allowed path length to reach the goal. If a solution path that is shorter than ompl::base::Goal::getMaximumPathLength() is found, the algorithm terminates before the elapsed time.
External documentation
S. Karaman and E. Frazzoli, Sampling-based Algorithms for Optimal Motion Planning, International Journal of Robotics Research, Vol 30, No 7, 2011. http://arxiv.org/abs/1105.1186

Definition at line 76 of file RRTstar.h.

Member Function Documentation

◆ setGoalBias()

void ompl::geometric::RRTstar::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 99 of file RRTstar.h.

◆ setRange()

void ompl::geometric::RRTstar::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 115 of file RRTstar.h.


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