Optimal Rapidly-exploring Random Trees Maintaining A Pseudo Optimal Tree. More...

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

Inheritance diagram for ompl::geometric::RRTXstatic:


class  Motion
 Representation of a motion (node of the tree) More...
struct  MotionCompare
 Defines the operator to compare motions. More...

Public Member Functions

 RRTXstatic (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 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 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.
virtual void setEpsilon (double epsilon)
 Set the threshold epsilon. More...
double getEpsilon () const
 Get the threshold epsilon 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 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 setUpdateChildren (bool val)
 Set whether or not to always propagate cost updates to children.
bool getUpdateChildren () const
 True if the cost is always propagate to children.
void setVariant (const int variant)
 Set variant used for rejection sampling.
int getVariant () const
 Get variant used for rejection sampling.
void setAlpha (const double a)
 Set the value alpha used for rejection sampling.
double getAlpha () const
 Get the value alpha used for rejection 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 updateQueue (Motion *x)
 Update (or add) a motion in the queue.
void removeFromParent (Motion *m)
 Removes the given motion from the parent's child list.
void getNeighbors (Motion *motion) const
 Gets the neighbours of a given motion, using either k-nearest of radius as appropriate.
void calculateRewiringLowerBounds ()
 Calculate the k_RRG* and r_RRG* terms.
void calculateRRG ()
 Calculate the rrg_r_ and rrg_k_ terms.
bool includeVertex (const Motion *x) const
 Test if the vertex should be included according to the variant in use.
std::string numIterationsProperty () const
std::string bestCostProperty () const
std::string numMotionsProperty () 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_rrg = s \times r_rrg* > r_rrg* (or k_rrg = s \times k_rrg* > k_rrg*)
double k_rrt_ {0u}
 A constant for k-nearest rewiring calculations.
double r_rrt_ {0.}
 A constant for r-disc rewiring calculations.
base::OptimizationObjectivePtr opt_
 Objective we're optimizing.
MotionlastGoalMotion_ {nullptr}
 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.
base::Cost bestCost_ {std::numeric_limits<double>::quiet_NaN()}
 Best cost found so far by algorithm.
unsigned int iterations_ {0u}
 Number of iterations the algorithm performed.
MotionCompare mc_
 Comparator of motions, used to order the queue.
BinaryHeap< Motion *, MotionCompareq_
 Queue to order the nodes to update.
base::Cost epsilonCost_ {0.}
 Threshold for the propagation of information.
bool updateChildren_ {true}
 Whether or not to propagate the cost to children if the update is less than epsilon.
double rrg_r_
 Current value of the radius used for the neighbors.
unsigned int rrg_k_
 Current value of the number of neighbors used.
int variant_ {0}
 Variant used for rejection sampling.
double alpha_ {1.}
 Alpha parameter, scaling the rejection sampling tests.
bool useInformedSampling_ {false}
 Option to use informed sampling.
bool useRejectionSampling_ {false}
 The status of the sample rejection parameter.
unsigned int numSampleAttempts_ {100u}
 The number of attempts to make at informed sampling.
- 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 Maintaining A Pseudo Optimal Tree.

Short description
RRTXstatic is an asymptotically-optimal incremental sampling-based motion planning algorithm. It differs from the RRT* algorithm by maintaining a pseudo-optimal tree.
When adding a new motion, any rewiring improving the cost by more than epsilon is done. While each iteration is more costly than one of RRT*, the convergence is faster thanks to the pseudo-optimal tree.
Parameters are:
  • range: maximum size allowed for an edge
  • goal_bias: probability of sampling the goal region
  • rewire_factor: multiplicative factor for the size of the neighbor ball (or the number of neighbors)
  • use_k_nearest: use kNN instead of rNN
  • informed_sampling: direct sampling in the relevant region defined by the heuristic
  • sample_rejection: rejects sampled out of the relevant region defined by the heuristic
  • number_sampling_attempts: number of sampling attempts when using rejection sampling or informed sampling
  • epsilon: the minimum threshold for cost improvement required to rewire the tree
  • update_children: Force propagation of cost to children even if it is less than the threshold epsilon. It improves convergence with minimal extra computation.
  • rejection_variant: Variants of the rejection of samples, 0: no rejection, 1-3: variants from reference (3)
  • rejection_variant_alpha: Parameter alpha used for the rejection sampling, it allows to scale the non-admissible heuristic, see reference (3)

The queue is implemented only using a scalar (cost + heuristic) as a key for ordering. With random samples, the set {cost + heuristic = constant} should be of measure 0, so a more complex key is not needed.

Only the static part of the RRTX algorithm is implemented. Dynamical obstacles and updates of the robot position are not available in this implementation.
External documentation
  1. M. Otte & E. Frazzoli - RRTX : Real-Time Motion Planning/Replanning for Environments with Unpredictable Obstacles, Algorithmic Foundations of Robotics XI, Volume 107 of the series Springer Tracts in Advanced Robotics pp 461-478
  2. O. Arslan, P. Tsiotras - The role of vertex consistency in sampling-based algorithms for optimal motion planning, https://arxiv.org/pdf/1204.6453
  3. O. Arslan, P. Tsiotras - Dynamic programming guided exploration for sampling-based motion planning algorithms, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp 4819-4826

Definition at line 167 of file RRTXstatic.h.

Member Function Documentation

◆ setEpsilon()

virtual void ompl::geometric::RRTXstatic::setEpsilon ( double  epsilon)

Set the threshold epsilon.

While propagating information, the propagation is done only if the cost enhancement is at least epsilon

Reimplemented in ompl::geometric::RRTsharp.

Definition at line 269 of file RRTXstatic.h.

◆ setGoalBias()

void ompl::geometric::RRTXstatic::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 223 of file RRTXstatic.h.

◆ setRange()

void ompl::geometric::RRTXstatic::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 285 of file RRTXstatic.h.

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