ompl::base::ProblemDefinition Class Reference

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.
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#include <ompl/base/ProblemDefinition.h>

Inheritance diagram for ompl::base::ProblemDefinition:

## Public Member Functions

ProblemDefinition (const ProblemDefinition &)=delete

ProblemDefinitionoperator= (const ProblemDefinition &)=delete

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

const SpaceInformationPtrgetSpaceInformation () const
Get the space information this problem definition is for.

Add a start state. The state is copied.

Add a start state. The state is copied. More...

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.

void clearStartStates ()
Clear all start states (memory is freed)

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

const StategetStartState (unsigned int index) const
Returns a specific start state.

StategetStartState (unsigned int index)
Returns a specific start state. More...

void setGoal (const GoalPtr &goal)
Set the goal.

void clearGoal ()
Clear the goal. Memory is freed.

const GoalPtrgetGoal () 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. More...

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()

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. More...

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() More...

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

const OptimizationObjectivePtrgetOptimizationObjective () const
Get the optimization objective to be considered during planning.

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

const ReportIntermediateSolutionFngetIntermediateSolutionCallback () 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.

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. More...

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)

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 the shortest one that was found, preference being given to solutions that are not approximate. More...

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 the shortest one 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< PlannerSolutiongetSolutions () 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 SolutionNonExistenceProofPtrgetSolutionNonExistenceProof () 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 Member 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.

## Detailed Description

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.

Definition at line 213 of file ProblemDefinition.h.

## Member Function Documentation

 void ompl::base::ProblemDefinition::addStartState ( const ScopedState<> & state )
inline

Add a start state. The state is copied.

Definition at line 241 of file ProblemDefinition.h.

## ◆ getSolutionPath()

 ompl::base::PathPtr ompl::base::ProblemDefinition::getSolutionPath ( ) const

Return the top solution path, if one is found. The top path is the shortest one 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

Definition at line 400 of file ProblemDefinition.cpp.

## ◆ getStartState()

 State* ompl::base::ProblemDefinition::getStartState ( unsigned int index )
inline

Returns a specific start state.

Definition at line 272 of file ProblemDefinition.h.

## ◆ isStraightLinePathValid()

 ompl::base::PathPtr ompl::base::ProblemDefinition::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()
When planning under differential constraints, the system is propagated forward in time using the null control.

Definition at line 276 of file ProblemDefinition.cpp.

## ◆ setGoalState()

 void ompl::base::ProblemDefinition::setGoalState ( const ScopedState<> & goal, const double threshold = std::numeric_limits::epsilon() )
inline

A simple form of setting the goal. This is called by setStartAndGoalStates(). A more general form is setGoal()

Definition at line 323 of file ProblemDefinition.h.

## ◆ setStartAndGoalStates() [1/2]

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

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.

Definition at line 316 of file ProblemDefinition.h.

## ◆ setStartAndGoalStates() [2/2]

 void ompl::base::ProblemDefinition::setStartAndGoalStates ( const State * start, const State * goal, double threshold = std::numeric_limits::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.

Definition at line 167 of file ProblemDefinition.cpp.

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