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ompl::geometric::RRTstar Class Reference
Optimal Rapidlyexploring 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...  
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). 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 r_rrg* (or k_rrg = s k_rrg*)  
double  getRewireFactor () const 
Set the rewiring scale factor, s, such that r_rrg = s r_rrg* > r_rrg* (or k_rrg = s 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 lowerbounding 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 closedform 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 newstate rejection option.  
void  setAdmissibleCostToCome (const bool admissible) 
Controls whether pruning and newstate rejection uses an admissible costtocome estimate or not.  
bool  getAdmissibleCostToCome () const 
Get the admissibility of the pruning and newstate 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 newstate rejection. This flag individually sets the options described above.  
bool  getFocusSearch () const 
Get the state of search focusing.  
void  setKNearest (bool useKNearest) 
Use a knearest search for rewiring instead of a rdisc search.  
bool  getKNearest () const 
Get the state of using a knearest 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  
Planner &  operator= (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 SpaceInformationPtr &  getSpaceInformation () const 
Get the space information this planner is using.  
const ProblemDefinitionPtr &  getProblemDefinition () const 
Get the problem definition the planner is trying to solve.  
const PlannerInputStates &  getPlannerInputStates () 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 PlannerSpecs &  getSpecs () 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.  
ParamSet &  params () 
Get the parameters for this planner.  
const ParamSet &  params () const 
Get the parameters for this planner.  
const PlannerProgressProperties &  getPlannerProgressProperties () 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 knearest 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 nearestneighbors 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 knearest search for rewiring.  
double  rewireFactor_ {1.1} 
The rewiring factor, s, so that r_rrt = s r_rrt* > r_rrt* (or k_rrt = s k_rrt* > k_rrt*)  
double  k_rrt_ {0u} 
A constant for knearest rewiring calculations.  
double  r_rrt_ {0.} 
A constant for rdisc rewiring calculations.  
bool  delayCC_ {true} 
Option to delay and reduce collision checking within iterations.  
base::OptimizationObjectivePtr  opt_ 
Objective we're optimizing.  
Motion *  bestGoalMotion_ {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 newstate rejection parameter.  
bool  useAdmissibleCostToCome_ {true} 
The admissibility of the newstate 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  
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, PlannerProgressProperty >  PlannerProgressProperties 
A dictionary which maps the name of a progress property to the function to be used for querying that property.  
Detailed Description
Optimal Rapidlyexploring Random Trees.
 Short description
 RRT* (optimal RRT) is an asymptoticallyoptimal incremental samplingbased 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, Samplingbased Algorithms for Optimal Motion Planning, International Journal of Robotics Research, Vol 30, No 7, 2011. http://arxiv.org/abs/1105.1186
Member Function Documentation
◆ setGoalBias()

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.
◆ setRange()

inline 
The documentation for this class was generated from the following files:
 ompl/geometric/planners/rrt/RRTstar.h
 ompl/geometric/planners/rrt/src/RRTstar.cpp