This class allows for the definition of multiobjective optimal planning problems. Objectives are added to this compound object, and motion costs are computed by taking a weighted sum of the individual objective costs. More...
#include <ompl/base/OptimizationObjective.h>
Classes  
struct  Component 
Defines a pairing of an objective and its weight. More...  
Public Member Functions  
MultiOptimizationObjective (const SpaceInformationPtr &si)  
void  addObjective (const OptimizationObjectivePtr &objective, double weight) 
Adds a new objective for this multiobjective. A weight must also be specified for specifying importance of this objective in planning.  
std::size_t  getObjectiveCount () const 
Returns the number of objectives that make up this multiobjective.  
const OptimizationObjectivePtr &  getObjective (unsigned int idx) const 
Returns a specific objective from this multiobjective, where the individual objectives are in order of addition to the multiobjective, and idx is the zerobased index into this ordering.  
double  getObjectiveWeight (unsigned int idx) const 
Returns the weighing factor of a specific objective.  
void  setObjectiveWeight (unsigned int idx, double weight) 
Sets the weighing factor of a specific objective.  
void  lock () 
This method "freezes" this multiobjective so that no more objectives can be added to it.  
bool  isLocked () const 
Returns whether this multiobjective has been locked from adding further objectives.  
Cost  stateCost (const State *s) const override 
Cost  motionCost (const State *s1, const State *s2) const override 
Public Member Functions inherited from ompl::base::OptimizationObjective  
OptimizationObjective (const OptimizationObjective &)=delete  
OptimizationObjective &  operator= (const OptimizationObjective &)=delete 
OptimizationObjective (SpaceInformationPtr si)  
Constructor. The objective must always know the space information it is part of. The cost threshold for objective satisfaction defaults to 0.0.  
const std::string &  getDescription () const 
Get the description of this optimization objective.  
virtual bool  isSatisfied (Cost c) const 
Check if the the given cost c satisfies the specified cost objective, defined as better than the specified threshold.  
Cost  getCostThreshold () const 
Returns the cost threshold currently being checked for objective satisfaction.  
void  setCostThreshold (Cost c) 
Set the cost threshold for objective satisfaction. When a path is found with a cost better than the cost threshold, the objective is considered satisfied.  
virtual bool  isCostBetterThan (Cost c1, Cost c2) const 
Check whether the the cost c1 is considered better than the cost c2. By default, this returns true if if c1 is less than c2.  
virtual bool  isCostEquivalentTo (Cost c1, Cost c2) const 
Compare whether cost c1 and cost c2 are equivalent. By default defined as !isCostBetterThan(c1, c2) && !isCostBetterThan(c2, c1), as if c1 is not better than c2, and c2 is not better than c1, then they are equal.  
virtual bool  isFinite (Cost cost) const 
Returns whether the cost is finite or not.  
virtual Cost  betterCost (Cost c1, Cost c2) const 
Return the minimum cost given c1 and c2. Uses isCostBetterThan.  
virtual Cost  combineCosts (Cost c1, Cost c2) const 
Get the cost that corresponds to combining the costs c1 and c2. Default implementation defines this combination as an addition.  
virtual Cost  identityCost () const 
Get the identity cost value. The identity cost value is the cost c_i such that, for all costs c, combineCosts(c, c_i) = combineCosts(c_i, c) = c. In other words, combining a cost with the identity cost does not change the original cost. By default, a cost with the value 0.0 is returned. It's very important to override this with the proper identity value for your optimization objectives, or else optimal planners may not work.  
virtual Cost  infiniteCost () const 
Get a cost which is greater than all other costs in this OptimizationObjective; required for use in Dijkstra/Astar. Defaults to returning the double value inf.  
virtual Cost  initialCost (const State *s) const 
Returns a cost value corresponding to starting at a state s. No optimal planners currently support this method. Defaults to returning the objective's identity cost.  
virtual Cost  terminalCost (const State *s) const 
Returns a cost value corresponding to a path ending at a state s. No optimal planners currently support this method. Defaults to returning the objective's identity cost.  
virtual bool  isSymmetric () const 
Check if this objective has a symmetric cost metric, i.e. motionCost(s1, s2) = motionCost(s2, s1). Default implementation returns whether the underlying state space has symmetric interpolation.  
virtual Cost  averageStateCost (unsigned int numStates) const 
Compute the average state cost of this objective by taking a sample of numStates states.  
void  setCostToGoHeuristic (const CostToGoHeuristic &costToGo) 
Set the costtogo heuristic function for this objective. The costtogo heuristic is a function which returns an admissible estimate of the optimal path cost from a given state to a goal, where "admissible" means that the estimated cost is always less than the true optimal cost.  
bool  hasCostToGoHeuristic () const 
Check if this objective has a costtogo heuristic function.  
Cost  costToGo (const State *state, const Goal *goal) const 
Uses a costtogo heuristic to calculate an admissible estimate of the optimal cost from a given state to a given goal. If no costtogo heuristic has been specified with setCostToGoHeuristic(), this function just returns the identity cost, which is sure to be an admissible heuristic if there are no negative costs.  
virtual Cost  motionCostHeuristic (const State *s1, const State *s2) const 
Defines an admissible estimate on the optimal cost on the motion between states s1 and s2. An admissible estimate always undervalues the true optimal cost of the motion. Used by some planners to speed up planning. The default implementation of this method returns this objective's identity cost, which is sure to be an admissible heuristic if there are no negative costs.  
const SpaceInformationPtr &  getSpaceInformation () const 
Returns this objective's SpaceInformation. Needed for operators in MultiOptimizationObjective.  
virtual InformedSamplerPtr  allocInformedStateSampler (const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls) const 
Allocate a heuristicsampling state generator for this cost function, defaults to a basic rejection sampling scheme when the derived class does not provide a better method.  
virtual void  print (std::ostream &out) const 
Print information about this optimization objective.  
Protected Attributes  
std::vector< Component >  components_ 
List of objective/weight pairs.  
bool  locked_ 
Whether this multiobjective is locked from further additions.  
Protected Attributes inherited from ompl::base::OptimizationObjective  
SpaceInformationPtr  si_ 
The space information for this objective.  
std::string  description_ 
The description of this optimization objective.  
Cost  threshold_ 
The cost threshold used for checking whether this objective has been satisfied during planning.  
CostToGoHeuristic  costToGoFn_ 
The function used for returning admissible estimates on the optimal cost of the path between a given state and goal.  
Friends  
OptimizationObjectivePtr  operator+ (const OptimizationObjectivePtr &a, const OptimizationObjectivePtr &b) 
Given two optimization objectives, returns a MultiOptimizationObjective that combines the two objectives with both weights equal to 1.0.  
OptimizationObjectivePtr  operator* (double w, const OptimizationObjectivePtr &a) 
Given a weighing factor and an optimization objective, returns a MultiOptimizationObjective containing only this objective weighted by the given weight.  
OptimizationObjectivePtr  operator* (const OptimizationObjectivePtr &a, double w) 
Given a weighing factor and an optimization objective, returns a MultiOptimizationObjective containing only this objective weighted by the given weight.  
Detailed Description
This class allows for the definition of multiobjective optimal planning problems. Objectives are added to this compound object, and motion costs are computed by taking a weighted sum of the individual objective costs.
Definition at line 207 of file OptimizationObjective.h.
Member Function Documentation
◆ motionCost()

overridevirtual 
The default implementation of this method is to use addition to add up all the individual objectives' motion cost values, where each individual value is scaled by its weight
Implements ompl::base::OptimizationObjective.
Definition at line 259 of file OptimizationObjective.cpp.
◆ stateCost()

overridevirtual 
The default implementation of this method is to use addition to add up all the individual objectives' state cost values, where each individual value is scaled by its weight
Implements ompl::base::OptimizationObjective.
Definition at line 248 of file OptimizationObjective.cpp.
The documentation for this class was generated from the following files:
 ompl/base/OptimizationObjective.h
 ompl/base/src/OptimizationObjective.cpp