Class EST

Nested Relationships

class EST : public ompl::base::Planner 

Expansive Space Trees.

Short description: EST is a tree-based motion planner that attempts to detect the less explored area of the space by measuring the density of the explored space, biasing exploration toward parts of the space with lowest density.
External documentation: D. Hsu, J.-C. Latombe, and R. Motwani, Path planning in expansive configuration spaces, Intl. J. Computational Geometry and Applications, vol. 9, no. 4-5, pp. 495–512, 1999. DOI: 10.1142/S0218195999000285 [PDF]

Public Functions

virtual 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.

virtual void clear() override: Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.

inline 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.

inline void 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.

virtual 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.

virtual 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).

Protected Functions

inline double distanceFunction(const Motion *a, const Motion *b) const: Compute distance between motions (actually distance between contained states)

void addMotion(Motion *motion, const std::vector<Motion*> &neighbors): Add a motion to the exploration tree.

Protected Attributes

std::shared_ptr<NearestNeighbors<Motion*>> nn_: A nearest-neighbors datastructure containing the tree of motions.

PDF<Motion*> pdf_: The probability distribution function over states in the tree.

double goalBias_ = {0.05}: 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.

Motion *lastGoalMotion_ = {nullptr}: The most recent goal motion. Used for PlannerData computation.

class Motion

The definition of a motion.

Public Functions

inline Motion(const base::SpaceInformationPtr &si): Constructor that allocates memory for the state.

Public Members

PDF<Motion*>::Element *element = {nullptr}: A pointer to the corresponding element in the probability distribution function.