ompl::base::PathLengthOptimizationObjective Class Reference
An optimization objective which corresponds to optimizing path length. More...
#include <ompl/base/objectives/PathLengthOptimizationObjective.h>
Inheritance diagram for ompl::base::PathLengthOptimizationObjective:
Public Member Functions | |
| PathLengthOptimizationObjective (const SpaceInformationPtr &si) | |
| Cost | stateCost (const State *s) const override |
| Returns identity cost. | |
| Cost | motionCost (const State *s1, const State *s2) const override |
| Motion cost for this objective is defined as the configuration space distance between s1 and s2, using the method SpaceInformation::distance(). | |
| Cost | motionCostHeuristic (const State *s1, const State *s2) const override |
| the motion cost heuristic for this objective is simply the configuration space distance between s1 and s2, since this is the optimal cost between any two states assuming no obstacles. | |
| InformedSamplerPtr | allocInformedStateSampler (const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls) const override |
| Allocate a state sampler for the path-length objective (i.e., direct ellipsoidal sampling). | |
 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 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. | |
| const SpaceInformationPtr & | getSpaceInformation () const |
| Returns this objective's SpaceInformation. Needed for operators in MultiOptimizationObjective. | |
| virtual void | print (std::ostream &out) const |
| Print information about this optimization objective. | |
Additional Inherited Members | |
| 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
An optimization objective which corresponds to optimizing path length.
Definition at line 47 of file PathLengthOptimizationObjective.h.
The documentation for this class was generated from the following files:
- ompl/base/objectives/PathLengthOptimizationObjective.h
- ompl/base/objectives/src/PathLengthOptimizationObjective.cpp