Optimal Rapidly-exploring Random Trees. More...

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

Inheritance diagram for ompl::geometric::RRTstar:


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

Public Member Functions

 RRTstar (const base::SpaceInformationPtr &si)
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). 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.
void clear () override
 Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.
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.
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.
void setRewireFactor (double rewireFactor)
 Set the rewiring scale factor, s, such that r_rrg = s \times r_rrg* (or k_rrg = s \times k_rrg*)
double getRewireFactor () const
 Set the rewiring scale factor, s, such that r_rrg = s \times r_rrg* > r_rrg* (or k_rrg = s \times k_rrg* > k_rrg*)
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 collision 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 setTreePruning (bool prune)
 Controls whether the tree is pruned during the search. This pruning removes a vertex if and only if it and all its descendents passes the pruning condition. The pruning condition is whether the lower-bounding estimate of a solution constrained to pass the the vertex is greater than the current solution. Considering the descendents of a vertex prevents removing a descendent that may actually be capable of later providing a better solution once its incoming path passes through a different vertex (e.g., a change in homotopy class).
bool getTreePruning () const
 Get the state of the pruning option.
void setPruneThreshold (const double pp)
 Set the fractional change in solution cost necessary for pruning to occur, i.e., prune if the new solution is at least X% better than the old solution. (e.g., 0.0 will prune after every new solution, while 1.0 will never prune.)
double getPruneThreshold () const
 Get the current prune states percentage threshold parameter.
void setPrunedMeasure (bool informedMeasure)
 Use the measure of the pruned subproblem instead of the measure of the entire problem domain (if such an expression exists and a solution is present). Currently the only method to calculate this measure in closed-form is through a informed sampler, so this option also requires that.
bool getPrunedMeasure () const
 Get the state of using the pruned measure.
void setInformedSampling (bool informedSampling)
 Use direct sampling of the heuristic for the generation of random samples (e.g., x_rand). If a direct sampling method is not defined for the objective, rejection sampling will be used by default.
bool getInformedSampling () const
 Get the state direct heuristic sampling.
void setSampleRejection (bool reject)
 Controls whether heuristic rejection is used on samples (e.g., x_rand)
bool getSampleRejection () const
 Get the state of the sample rejection option.
void setNewStateRejection (const bool reject)
 Controls whether heuristic rejection is used on new states before connection (e.g., x_new = steer(x_nearest, x_rand))
bool getNewStateRejection () const
 Get the state of the new-state rejection option.
void setAdmissibleCostToCome (const bool admissible)
 Controls whether pruning and new-state rejection uses an admissible cost-to-come estimate or not.
bool getAdmissibleCostToCome () const
 Get the admissibility of the pruning and new-state rejection heuristic.
void setOrderedSampling (bool orderSamples)
 Controls whether samples are returned in ordered by the heuristic. This is accomplished by generating a batch at a time.
bool getOrderedSampling () const
 Get the state of sample ordering.
void setBatchSize (unsigned int batchSize)
 Set the batch size used for sample ordering.
unsigned int getBatchSize () const
 Get the batch size used for sample ordering.
void setFocusSearch (const bool focus)
 A meta parameter to focusing the search to improving the current solution. This is the parameter set by CFOREST. For RRT*, search focusing consists of pruning the existing search and limiting future search. Specifically, this is accomplished by turning on informed sampling, tree pruning and new-state rejection. This flag individually sets the options described above.
bool getFocusSearch () const
 Get the state of search focusing.
void setKNearest (bool useKNearest)
 Use a k-nearest search for rewiring instead of a r-disc search.
bool getKNearest () const
 Get the state of using a k-nearest search for rewiring.
void setNumSamplingAttempts (unsigned int numAttempts)
 Set the number of attempts to make while performing rejection or informed sampling.
unsigned int getNumSamplingAttempts () const
 Get the number of attempts to make while performing rejection or informed sampling.
unsigned int numIterations () const
ompl::base::Cost bestCost () const
- Public Member Functions inherited from ompl::base::Planner
 Planner (const Planner &)=delete
Planneroperator= (const Planner &)=delete
 Planner (SpaceInformationPtr si, std::string name)
virtual ~Planner ()=default
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.
ProblemDefinitionPtrgetProblemDefinition ()
 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() or clearQuery().
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.
virtual void clearQuery ()
 Clears internal datastructures of any query-specific information from the previous query. Planner settings are not affected. The planner, if able, should retain all datastructures generated from previous queries that can be used to help solve the next query. Note that clear() should also clear all query-specific information along with all other datastructures in the planner. By default clearQuery() calls clear().
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 allocSampler ()
 Create the samplers.
bool sampleUniform (base::State *statePtr)
 Generate a sample.
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 getNeighbors (Motion *motion, std::vector< Motion * > &nbh) const
 Gets the neighbours of a given motion, using either k-nearest of radius as appropriate.
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()
base::Cost solutionHeuristic (const Motion *motion) const
 Computes the solution cost heuristically as the cost to come from start to the motion plus the cost to go from the motion to the goal. If the parameter use_admissible_heuristic (setAdmissibleCostToCome()) is true, a heuristic estimate of the cost to come is used; otherwise, the current cost to come to the motion is used (which may overestimate the cost through the motion).
void addChildrenToList (std::queue< Motion *, std::deque< Motion * >> *motionList, Motion *motion)
 Add the children of a vertex to the given list.
bool keepCondition (const Motion *motion, const base::Cost &threshold) const
 Check whether the given motion passes the specified cost threshold, meaning it will be kept during pruning.
void calculateRewiringLowerBounds ()
 Calculate the k_RRG* and r_RRG* terms.
std::string numIterationsProperty () const
std::string bestCostProperty () const
- 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.
base::InformedSamplerPtr infSampler_
 An informed 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.
bool useKNearest_ {true}
 Option to use k-nearest search for rewiring.
double rewireFactor_ {1.1}
 The rewiring factor, s, so that r_rrt = s \times r_rrt* > r_rrt* (or k_rrt = s \times k_rrt* > k_rrt*)
double k_rrt_ {0u}
 A constant for k-nearest rewiring calculations.
double r_rrt_ {0.}
 A constant for r-disc rewiring calculations.
bool delayCC_ {true}
 Option to delay and reduce collision checking within iterations.
base::OptimizationObjectivePtr opt_
 Objective we're optimizing.
MotionbestGoalMotion_ {nullptr}
 The best goal motion.
std::vector< Motion * > goalMotions_
 A list of states in the tree that satisfy the goal condition.
bool useTreePruning_ {false}
 The status of the tree pruning option.
double pruneThreshold_ {.05}
 The tree is pruned when the change in solution cost is greater than this fraction.
bool usePrunedMeasure_ {false}
 Option to use the informed measure.
bool useInformedSampling_ {false}
 Option to use informed sampling.
bool useRejectionSampling_ {false}
 The status of the sample rejection parameter.
bool useNewStateRejection_ {false}
 The status of the new-state rejection parameter.
bool useAdmissibleCostToCome_ {true}
 The admissibility of the new-state rejection heuristic.
unsigned int numSampleAttempts_ {100u}
 The number of attempts to make at informed sampling.
bool useOrderedSampling_ {false}
 Option to create batches of samples and order them.
unsigned int batchSize_ {1u}
 The size of the batches.
std::vector< Motion * > startMotions_
 Stores the start states as Motions.
base::Cost bestCost_ {std::numeric_limits<double>::quiet_NaN()}
 Best cost found so far by algorithm.
base::Cost prunedCost_ {std::numeric_limits<double>::quiet_NaN()}
 The cost at which the graph was last pruned.
double prunedMeasure_ {0.}
 The measure of the problem when we pruned it (if this isn't in use, it will be set to si_->getSpaceMeasure())
unsigned int iterations_ {0u}
 Number of iterations the algorithm performed.
- 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
using PlannerProgressProperty = std::function< std::string()>
 Definition of a function which returns a property about the planner's progress that can be queried by a benchmarking routine.
using PlannerProgressProperties = std::map< std::string, PlannerProgressProperty >
 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 a specified OptimizationObjective (set in the ProblemDefinition). If a solution path's cost is within a user-specified cost-threshold, 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. https://arxiv.org/abs/1105.1186

Definition at line 138 of file RRTstar.h.

Member Function Documentation

◆ setGoalBias()

void ompl::geometric::RRTstar::setGoalBias ( double  goalBias)

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 194 of file RRTstar.h.

◆ setRange()

void ompl::geometric::RRTstar::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.

Definition at line 210 of file RRTstar.h.

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