Class ProblemDefinition

Defined in File ProblemDefinition.h

Inheritance Relationships

Derived Type

public ompl::control::LTLProblemDefinition (Class LTLProblemDefinition)

Class Documentation

class ProblemDefinition

Definition of a problem to be solved. This includes the start state(s) for the system and a goal specification. Will contain solutions, if found.

Subclassed by ompl::control::LTLProblemDefinition

Public Functions

ProblemDefinition(const ProblemDefinition&) = delete

ProblemDefinition &operator=(const ProblemDefinition&) = delete

ProblemDefinition(SpaceInformationPtr si): Create a problem definition given the SpaceInformation it is part of.

ProblemDefinitionPtr clone() const

Return a copy of the problem definition.

A deep copy is made of the start and goal states. A shallow copy is made of shared ptrs. The set of solutions paths and the intermediate solution callback function are not copied.

inline virtual ~ProblemDefinition()

inline const SpaceInformationPtr &getSpaceInformation() const: Get the space information this problem definition is for.

inline void addStartState(const State *state): Add a start state. The state is copied.

inline void addStartState(const ScopedState<> &state): Add a start state. The state is copied.

bool hasStartState(const State *state, unsigned int *startIndex = nullptr) const: Check whether a specified starting state is already included in the problem definition and optionally return the index of that starting state.

inline void clearStartStates(): Clear all start states (memory is freed)

inline unsigned int getStartStateCount() const: Returns the number of start states.

inline const State *getStartState(unsigned int index) const: Returns a specific start state.

inline State *getStartState(unsigned int index): Returns a specific start state.

inline void setGoal(const GoalPtr &goal): Set the goal.

inline void clearGoal(): Clear the goal. Memory is freed.

inline const GoalPtr &getGoal() const: Return the current goal.

void getInputStates(std::vector<const State*> &states) const: Get all the input states. This includes start states and states that are part of goal regions that can be casted as ompl::base::GoalState or ompl::base::GoalStates.

void setStartAndGoalStates(const State *start, const State *goal, double threshold = std::numeric_limits<double>::epsilon())

In the simplest case possible, we have a single starting state and a single goal state.

This function simply configures the problem definition using these states (performs the needed calls to addStartState(), creates an instance of ompl::base::GoalState and calls setGoal() on it.

void setGoalState(const State *goal, double threshold = std::numeric_limits<double>::epsilon()): A simple form of setting the goal. This is called by setStartAndGoalStates(). A more general form is setGoal()

inline void setStartAndGoalStates(const ScopedState<> &start, const ScopedState<> &goal, const double threshold = std::numeric_limits<double>::epsilon())

In the simplest case possible, we have a single starting state and a single goal state.

This function simply configures the problem definition using these states (performs the needed calls to addStartState(), creates an instance of ompl::base::GoalState and calls setGoal() on it.

inline void setGoalState(const ScopedState<> &goal, const double threshold = std::numeric_limits<double>::epsilon()): A simple form of setting the goal. This is called by setStartAndGoalStates(). A more general form is setGoal()

inline bool hasOptimizationObjective() const: Check if an optimization objective was defined for planning

inline const OptimizationObjectivePtr &getOptimizationObjective() const: Get the optimization objective to be considered during planning.

inline void setOptimizationObjective(const OptimizationObjectivePtr &optimizationObjective): Set the optimization objective to be considered during planning.

inline const ReportIntermediateSolutionFn &getIntermediateSolutionCallback() const: When this function returns a valid function pointer, that function should be called by planners that compute intermediate solutions every time a better solution is found.

inline void setIntermediateSolutionCallback(const ReportIntermediateSolutionFn &callback): Set the callback to be called by planners that can compute intermediate solutions.

bool isTrivial(unsigned int *startIndex = nullptr, double *distance = nullptr) const: A problem is trivial if a given starting state already in the goal region, so we need no motion planning. startID will be set to the index of the starting state that satisfies the goal. The distance to the goal can optionally be returned as well.

PathPtr isStraightLinePathValid() const: Check if a straight line path is valid. If it is, return an instance of a path that represents the straight line.

Note

When planning under geometric constraints, this works only if the goal region can be sampled. If the goal region cannot be sampled, this call is equivalent to calling isTrivial()

Note

When planning under differential constraints, the system is propagated forward in time using the null control.

bool fixInvalidInputStates(double distStart, double distGoal, unsigned int attempts): Many times the start or goal state will barely touch an obstacle. In this case, we may want to automatically find a nearby state that is valid so motion planning can be performed. This function enables this behaviour. The allowed distance for both start and goal states is specified. The number of attempts is also specified. Returns true if all states are valid after completion.

bool hasSolution() const: Returns true if a solution path has been found (could be approximate)

inline bool hasExactSolution() const: Returns true if an exact solution path has been found. Specifically returns hasSolution && !hasApproximateSolution()

bool hasApproximateSolution() const: Return true if the top found solution is approximate (does not actually reach the desired goal, but hopefully is closer to it)

double getSolutionDifference() const: Get the distance to the desired goal for the top solution. Return -1.0 if there are no solutions available.

bool hasOptimizedSolution() const: Return true if the top found solution is optimized (satisfies the specified optimization objective)

PathPtr getSolutionPath() const

Return the top solution path, if one is found. The top path is a shortest path that was found, preference being given to solutions that are not approximate.

This will need to be casted into the specialization computed by the planner

bool getSolution(PlannerSolution &solution) const: Return true if a top solution is found, with the top solution passed by reference in the function header The top path is a shortest path that was found, preference being given to solutions that are not approximate. This will need to be casted into the specialization computed by the planner.

void addSolutionPath(const PathPtr &path, bool approximate = false, double difference = -1.0, const std::string &plannerName = "Unknown") const: Add a solution path in a thread-safe manner. Multiple solutions can be set for a goal. If a solution does not reach the desired goal it is considered approximate. Optionally, the distance between the desired goal and the one actually achieved is set by difference. Optionally, the name of the planner that generated the solution.

void addSolutionPath(const PlannerSolution &sol) const: Add a solution path in a thread-safe manner. Multiple solutions can be set for a goal.

std::size_t getSolutionCount() const: Get the number of solutions already found.

std::vector<PlannerSolution> getSolutions() const: Get all the solution paths available for this goal.

void clearSolutionPaths() const: Forget the solution paths (thread safe). Memory is freed.

bool hasSolutionNonExistenceProof() const: Returns true if the problem definition has a proof of non existence for a solution.

void clearSolutionNonExistenceProof(): Removes any existing instance of SolutionNonExistenceProof.

const SolutionNonExistenceProofPtr &getSolutionNonExistenceProof() const: Retrieve a pointer to the SolutionNonExistenceProof instance for this problem definition.

void setSolutionNonExistenceProof(const SolutionNonExistenceProofPtr &nonExistenceProof): Set the instance of SolutionNonExistenceProof for this problem definition.

void print(std::ostream &out = std::cout) const: Print information about the start and goal states and the optimization objective.

Protected Functions

bool fixInvalidInputState(State *state, double dist, bool start, unsigned int attempts): Helper function for fixInvalidInputStates(). Attempts to fix an individual state.

Protected Attributes

SpaceInformationPtr si_: The space information this problem definition is for.

std::vector<State*> startStates_: The set of start states.

GoalPtr goal_: The goal representation.

SolutionNonExistenceProofPtr nonExistenceProof_: A Representation of a proof of non-existence of a solution for this problem definition.

OptimizationObjectivePtr optimizationObjective_: The objective to be optimized while solving the planning problem.

ReportIntermediateSolutionFn intermediateSolutionCallback_: Callback function which is called when a new intermediate solution has been found.