ompl::base::MaximizeMinClearanceObjective Class Reference

Objective for attempting to maximize the minimum clearance along a path. More...

`#include <ompl/base/objectives/MaximizeMinClearanceObjective.h>`

Inheritance diagram for ompl::base::MaximizeMinClearanceObjective:

## Public Member Functions | |

MaximizeMinClearanceObjective (const SpaceInformationPtr &si) | |

Cost | stateCost (const State *s) const override |

Defined as the clearance of the state s, which is computed using the StateValidityChecker in this objective's SpaceInformation. | |

bool | isCostBetterThan (Cost c1, Cost c2) const override |

Since we wish to maximize clearance, and costs are equivalent to path clearance, we return the greater of the two cost values. | |

Cost | identityCost () const override |

Returns +infinity, since any cost combined with +infinity under this objective will always return the other cost. | |

Cost | infiniteCost () const override |

Returns -infinity, since no path clearance value can be considered worse than this. | |

Public Member Functions inherited from ompl::base::MinimaxObjective | |

MinimaxObjective (const SpaceInformationPtr &si) | |

Cost | stateCost (const State *s) const override |

Returns a cost with a value of 1. | |

Cost | motionCost (const State *s1, const State *s2) const override |

Interpolates between s1 and s2 to check for state costs along the motion between the two states. Assumes all costs are worse than identity. | |

Cost | combineCosts (Cost c1, Cost c2) const override |

Since we're only concerned about the "worst" state cost in the path, combining two costs just returns the worse of the two. | |

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 | 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 | 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. | |

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 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. | |

## 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

Objective for attempting to maximize the minimum clearance along a path.

Definition at line 47 of file MaximizeMinClearanceObjective.h.

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

- ompl/base/objectives/MaximizeMinClearanceObjective.h
- ompl/base/objectives/src/MaximizeMinClearanceObjective.cpp