ompl::base::VFUpstreamCriterionOptimizationObjective Class Reference

#include <ompl/base/objectives/VFUpstreamCriterionOptimizationObjective.h>

Inheritance diagram for ompl::base::VFUpstreamCriterionOptimizationObjective:

Public Member Functions

 VFUpstreamCriterionOptimizationObjective (const ompl::base::SpaceInformationPtr &si, geometric::VFRRT::VectorField vf)
bool isSatisfied (ompl::base::Cost c) const override
Cost stateCost (const State *s) const override
 Returns a cost with a value of 0.
ompl::base::Cost motionCost (const State *s1, const State *s2) const override
bool isSymmetric () const override
 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.
- Public Member Functions inherited from ompl::base::OptimizationObjective
 OptimizationObjective (const OptimizationObjective &)=delete
OptimizationObjectiveoperator= (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.
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 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 cost-to-go heuristic function for this objective. The cost-to-go 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 cost-to-go heuristic function.
Cost costToGo (const State *state, const Goal *goal) const
 Uses a cost-to-go heuristic to calculate an admissible estimate of the optimal cost from a given state to a given goal. If no cost-to-go 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 SpaceInformationPtrgetSpaceInformation () const
 Returns this objective's SpaceInformation. Needed for operators in MultiOptimizationObjective.
virtual InformedSamplerPtr allocInformedStateSampler (const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls) const
 Allocate a heuristic-sampling 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

geometric::VFRRT::VectorField vf_
- 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.

Detailed Description

Optimization objective that computes the upstream criterion between two states.

Definition at line 52 of file VFUpstreamCriterionOptimizationObjective.h.

Constructor & Destructor Documentation

◆ VFUpstreamCriterionOptimizationObjective()

ompl::base::VFUpstreamCriterionOptimizationObjective::VFUpstreamCriterionOptimizationObjective ( const ompl::base::SpaceInformationPtr si,
geometric::VFRRT::VectorField  vf 


Definition at line 56 of file VFUpstreamCriterionOptimizationObjective.h.

Member Function Documentation

◆ isSatisfied()

bool ompl::base::VFUpstreamCriterionOptimizationObjective::isSatisfied ( ompl::base::Cost  c) const

Assume we can always do better.

Reimplemented from ompl::base::OptimizationObjective.

Definition at line 64 of file VFUpstreamCriterionOptimizationObjective.h.

◆ motionCost()

ompl::base::Cost ompl::base::VFUpstreamCriterionOptimizationObjective::motionCost ( const State s1,
const State s2 
) const

Compute upstream criterion between two states.

Implements ompl::base::OptimizationObjective.

Definition at line 76 of file VFUpstreamCriterionOptimizationObjective.h.

Member Data Documentation

◆ vf_

geometric::VFRRT::VectorField ompl::base::VFUpstreamCriterionOptimizationObjective::vf_

VectorField associated with the space.

Definition at line 107 of file VFUpstreamCriterionOptimizationObjective.h.

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