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| PDST (const SpaceInformationPtr &si) |
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.
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void | clear () override |
| Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.
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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.
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void | getPlannerData (base::PlannerData &data) const override |
| Extracts the planner data from the priority queue into data.
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void | setProjectionEvaluator (const base::ProjectionEvaluatorPtr &projectionEvaluator) |
| Set the projection evaluator. This class is able to compute the projection of a given state.
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void | setProjectionEvaluator (const std::string &name) |
| Set the projection evaluator (select one from the ones registered with the state space).
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const base::ProjectionEvaluatorPtr & | getProjectionEvaluator () const |
| Get the projection evaluator.
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void | setGoalBias (double goalBias) |
| 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. */.
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double | getGoalBias () const |
| Get the goal bias the planner is using */.
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| Planner (const Planner &)=delete |
Planner & | operator= (const Planner &)=delete |
| Planner (SpaceInformationPtr si, std::string name) |
| Constructor.
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virtual | ~Planner ()=default |
| Destructor.
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template<class T> |
T * | as () |
| Cast this instance to a desired type.
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template<class T> |
const T * | as () const |
| Cast this instance to a desired type.
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const SpaceInformationPtr & | getSpaceInformation () const |
| Get the space information this planner is using.
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const ProblemDefinitionPtr & | getProblemDefinition () const |
| Get the problem definition the planner is trying to solve.
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ProblemDefinitionPtr & | getProblemDefinition () |
| Get the problem definition the planner is trying to solve.
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const PlannerInputStates & | getPlannerInputStates () const |
| Get the planner input states.
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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().
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PlannerStatus | solve (const PlannerTerminationConditionFn &ptc, double checkInterval) |
| Same as above except the termination condition is only evaluated at a specified interval.
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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.
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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().
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const std::string & | getName () const |
| Get the name of the planner.
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void | setName (const std::string &name) |
| Set the name of the planner.
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const PlannerSpecs & | getSpecs () const |
| Return the specifications (capabilities of this planner)
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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.
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bool | isSetup () const |
| Check if setup() was called for this planner.
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ParamSet & | params () |
| Get the parameters for this planner.
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const ParamSet & | params () const |
| Get the parameters for this planner.
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const PlannerProgressProperties & | getPlannerProgressProperties () const |
| Retrieve a planner's planner progress property map.
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virtual void | printProperties (std::ostream &out) const |
| Print properties of the motion planner.
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virtual void | printSettings (std::ostream &out) const |
| Print information about the motion planner's settings.
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void | addMotion (Motion *motion, Cell *cell, base::State *, base::State *, Eigen::Ref< Eigen::VectorXd >, Eigen::Ref< Eigen::VectorXd >) |
| Inserts the motion into the appropriate cells, splitting the motion as necessary. motion is assumed to be fully contained within cell.
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void | updateHeapElement (Motion *motion) |
| Either update heap after motion's priority has changed or insert motion into heap.
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Motion * | propagateFrom (Motion *motion, base::State *, base::State *) |
| Select a state along motion and propagate a new motion from there. Return nullptr if no valid motion could be generated starting at the selected state.
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unsigned int | findDurationAndAncestor (Motion *motion, base::State *state, base::State *scratch, Motion *&ancestor) const |
| Find the max. duration that the control_ in motion can be applied s.t. the trajectory passes through state. This means that "ancestor" motions with the same control_ are also considered. A pointer to the oldest ancestor with the same control_ is returned. Upon return applying the control ancestor->control_ for duration steps starting from the state ancestor->startState_ should result in the state state.
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void | freeMemory () |
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.
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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.
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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.
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Path-Directed Subdivision Tree.
- Short description
- PDST is a tree-based motion planner that attempts to detect the less explored area of the space through the use of a binary space partition of a projection of the state space. Exploration is biased towards large cells with few path segments. Unlike most tree-based planners which expand from a randomly select endpoint of a path segment, PDST expands from a randomly selected point along a deterministically selected path segment. Because of this, it is recommended to increase the min. and max. control duration using ompl::control::SpaceInformation::setMinMaxControlDuration. It is important to set the projection the algorithm uses (setProjectionEvaluator() function). If no projection is set, the planner will attempt to use the default projection associated to the state space. An exception is thrown if no default projection is available either.
- External documentation
- A.M. Ladd and L.E. Kavraki, Motion planning in the presence of drift, underactuation and discrete system changes, in Robotics: Science and Systems I, pp. 233–241, MIT Press, June 2005. [PDF]
Definition at line 80 of file PDST.h.