Bidirectional Asymptotically Optimal Fast Marching Tree algorithm developed by J. Starek, J.V. Gomez, et al.
More...
|
|
| BFMT (const base::SpaceInformationPtr &si) |
| |
|
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.
|
| |
|
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 | 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).
|
| |
|
void | setNumSamples (const unsigned int numSamples) |
| | Set the number of states that the planner should sample. The planner will sample this number of states in addition to the initial states. If any of the goal states are not reachable from the randomly sampled states, those goal states will also be added. The default value is 1000.
|
| |
|
unsigned int | getNumSamples () const |
| | Get the number of states that the planner will sample.
|
| |
|
void | setNearestK (bool nearestK) |
| | If nearestK is true, FMT will be run using the Knearest strategy.
|
| |
|
bool | getNearestK () const |
| | Get the state of the nearestK strategy.
|
| |
|
void | setRadiusMultiplier (const double radiusMultiplier) |
| | The planner searches for neighbors of a node within a cost r, where r is the value described for BFMT* in Section 4 of [L. Janson, E. Schmerling, A. Clark, M. Pavone. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research, 34(7):883-921, 2015](https://arxiv.org/pdf/1306.3532.pdf). For guaranteed asymptotic convergence, the user should choose a constant multiplier for the search radius that is greater than one. The default value is 1.1. In general, a radius multiplier between 0.9 and 5 appears to perform the best.
|
| |
|
double | getRadiusMultiplier () const |
| | Get the multiplier used for the nearest neighbors search radius.
|
| |
|
void | setFreeSpaceVolume (const double freeSpaceVolume) |
| | Store the volume of the obstacle-free configuration space. If no value is specified, the default assumes an obstacle-free unit hypercube, freeSpaceVolume = (maximumExtent/sqrt(dimension))^(dimension)
|
| |
|
double | getFreeSpaceVolume () const |
| | Get the volume of the free configuration space that is being used by the planner.
|
| |
|
void | setCacheCC (bool ccc) |
| | Sets the collision check caching to save calls to the collision checker with slightly memory usage as a counterpart.
|
| |
|
bool | getCacheCC () const |
| | Get the state of the collision check caching.
|
| |
|
void | setHeuristics (bool h) |
| | Activates the cost to go heuristics when ordering the heap.
|
| |
|
bool | getHeuristics () const |
| | Returns true if the heap is ordered taking into account cost to go heuristics.
|
| |
|
void | setExtendedFMT (bool e) |
| | Activates the extended FMT*: adding new samples if planner does not finish successfully.
|
| |
|
bool | getExtendedFMT () const |
| | Returns true if the extended FMT* is activated.
|
| |
|
void | setExploration (bool balanced) |
| | Sets exploration strategy: balanced true expands one tree every iteration. False will select the tree with lowest maximum cost to go.
|
| |
|
bool | getExploration () const |
| | Returns the exploration strategy.
|
| |
|
void | setTermination (bool optimality) |
| | Sets the termination strategy: optimality true finishes when the best possible path is found. Otherwise, the algorithm will finish when the first feasible path is found.
|
| |
|
bool | getTermination () const |
| | Returns the termination strategy.
|
| |
|
void | setPrecomputeNN (bool p) |
| | Sets Nearest Neighbors precomputation. Currently, it precomputes once solve() has been called.
|
| |
|
bool | setPrecomputeNN () const |
| | Returns true if Nearest Neighbor precomputation is done.
|
| |
|
| 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.
|
| |
|
ProblemDefinitionPtr & | getProblemDefinition () |
| | 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() 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 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.
|
| |
|
|
void | swapTrees () |
| | Change the active tree.
|
| |
|
void | useFwdTree () |
| | Sets forward tree active.
|
| |
|
void | useRevTree () |
| | Sets reverse tree active.
|
| |
|
double | distanceFunction (const BiDirMotion *a, const BiDirMotion *b) const |
| | Compute the distance between two motions as the cost between their contained states. Note that for computationally intensive cost functions, the cost between motions should be stored to avoid duplicate calculations.
|
| |
|
double | calculateUnitBallVolume (unsigned int dimension) const |
| | Compute the volume of the unit ball in a given dimension.
|
| |
|
double | calculateRadius (unsigned int dimension, unsigned int n) const |
| | Calculate the radius to use for nearest neighbor searches, using the bound given in [L. Janson, E. Schmerling, A. Clark, M. Pavone. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research, 34(7):883-921, 2015](https://arxiv.org/pdf/1306.3532.pdf). The radius depends on the radiusMultiplier parameter, the volume of the free configuration space, the volume of the unit ball in the current dimension, and the number of nodes in the graph.
|
| |
|
void | freeMemory () |
| | Free the memory allocated by this planner.
|
| |
|
void | saveNeighborhood (BiDirMotion *m) |
| | Save the neighbors within a neighborhood of a given state. The strategy used (nearestK or nearestR depends on the planner configuration.
|
| |
|
void | sampleFree (const std::shared_ptr< NearestNeighbors< BiDirMotion * >> &nn, const base::PlannerTerminationCondition &ptc) |
| | Sample a state from the free configuration space and save it into the nearest neighbors data structure.
|
| |
|
void | initializeProblem (base::GoalSampleableRegion *&goal_s) |
| | Carries out some planner checks.
|
| |
|
void | expandTreeFromNode (BiDirMotion *&z, BiDirMotion *&connection_point) |
| | Complete one iteration of the main loop of the BFMT* algorithm: Find K nearest nodes in set Unvisited (or within a radius r) of the node z. Attempt to connect them to their optimal cost-to-come parent in set Open. Remove all newly connected nodes fromUnvisited and insert them into Open. Remove motion z from Open, and update z to be the current lowest cost-to-come node in Open.
|
| |
| bool | plan (BiDirMotion *x_init, BiDirMotion *x_goal, BiDirMotion *&connection_point, const base::PlannerTerminationCondition &ptc) |
| | Executes the actual planning algorithm, swapping and expanding the trees. More...
|
| |
|
bool | termination (BiDirMotion *&z, BiDirMotion *&connection_point, const base::PlannerTerminationCondition &ptc) |
| | Checks if the termination condition is met.
|
| |
|
void | chooseTreeAndExpansionNode (BiDirMotion *&z) |
| | Chooses and expand a tree according to the exploration strategy.
|
| |
|
void | tracePath (BiDirMotion *z, BiDirMotionPtrs &path) |
| | Trace the path along a tree towards the root (forward or reverse)
|
| |
|
void | updateNeighborhood (BiDirMotion *m, std::vector< BiDirMotion * > nbh) |
| | For a motion m, updates the stored neighborhoods of all its neighbors by by inserting m (maintaining the cost-based sorting)
|
| |
|
void | insertNewSampleInOpen (const base::PlannerTerminationCondition &ptc) |
| | Extended FMT strategy: inserts a new motion in open if the heap is empty.
|
| |
|
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.
|
| |
|
|
unsigned int | numSamples_ {1000u} |
| | The number of samples to use when planning.
|
| |
|
double | radiusMultiplier_ {1.} |
| | This planner uses a nearest neighbor search radius proportional to the lower bound for optimality derived for FMT* in Section 4 of [L. Janson, E. Schmerling, A. Clark, M. Pavone. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research, 34(7):883-921, 2015](https://arxiv.org/pdf/1306.3532.pdf). The radius multiplier is the multiplier for the lower bound. For guaranteed asymptotic convergence, the user should choose a multiplier for the search radius that is greater than one. The default value is 1.1. In general, a radius between 0.9 and 5 appears to perform the best.
|
| |
|
double | freeSpaceVolume_ |
| | The volume of numSathe free configuration space, computed as an upper bound with 95% confidence.
|
| |
|
unsigned int | collisionChecks_ {0u} |
| | Number of collision checks performed by the algorithm.
|
| |
|
bool | nearestK_ {true} |
| | Flag to activate the K nearest neighbors strategy.
|
| |
|
double | NNr_ {0.} |
| | Radius employed in the nearestR strategy.
|
| |
|
unsigned int | NNk_ {0} |
| | K used in the nearestK strategy.
|
| |
|
TreeType | tree_ {FWD} |
| | Active tree.
|
| |
|
ExploreType | exploration_ {SWAP_EVERY_TIME} |
| | Exploration strategy used.
|
| |
|
TerminateType | termination_ {OPTIMALITY} |
| | Termination strategy used.
|
| |
|
bool | precomputeNN_ {false} |
| | If true all the nearest neighbors maps are precomputed before solving.
|
| |
|
std::shared_ptr< NearestNeighbors< BiDirMotion * > > | nn_ |
| | A nearest-neighbor datastructure containing the set of all motions.
|
| |
|
std::map< BiDirMotion *, BiDirMotionPtrs > | neighborhoods_ |
| | A map linking a motion to all of the motions within a distance r of that motion.
|
| |
|
BiDirMotionBinHeap | Open_ [2] |
| | A binary heap for storing explored motions in cost-to-come sorted order. The motions in Open have been explored, yet are still close enough to the frontier of the explored set Open to be connected to nodes in the unexplored set Unvisited.
|
| |
|
std::map< BiDirMotion *, BiDirMotionBinHeap::Element * > | Open_elements [2] |
| | Map to know the corresponding heap element from the given motion.
|
| |
|
base::StateSamplerPtr | sampler_ |
| | State sampler.
|
| |
|
base::OptimizationObjectivePtr | opt_ |
| | The cost objective function.
|
| |
|
bool | heuristics_ {true} |
| | Flag to activate the cost to go heuristics.
|
| |
|
base::State * | heurGoalState_ [2] |
| | Goal state caching to accelerate cost to go heuristic computation.
|
| |
|
bool | cacheCC_ {true} |
| | Flag to activate the collision check caching.
|
| |
|
bool | extendedFMT_ {true} |
| | Add new samples if the tree was not able to find a solution.
|
| |
|
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.
|
| |
Bidirectional Asymptotically Optimal Fast Marching Tree algorithm developed by J. Starek, J.V. Gomez, et al.
- Short description
- BFMT* is an asymptotically-optimal, bidirectional sampling-based motion planning algorithm, which is guaranteed to converge to a shortest path solution. The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. The BFMT* algorithm essentially performs a lazy dynamic programming recursion on a set of probabilistically-drawn samples to grow two trees of paths, which moves steadily outward in cost-to-come space, one from the start state and the other one from the goal state.
- Deviation from the paper
- The implementation includes a cache in the collision checking since the original algorithm could check the same collision more than once. It increases the memory requirements to O(n logn), but as samples tend to infinity this bound tend to O(n).
- External documentation
- J. A. Starek, J. V. Gomez, E. Schmerling, L. Janson, L. Moreno, and M. Pavone, An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning, inIEEE/RSJ International Conference on Intelligent Robots Systems, 2015. [PDF]
Definition at line 146 of file BFMT.h.
Public Types inherited from ompl::base::Planner