InformedStateSampler.cpp

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/* Authors: Jonathan Gammell */

#include "ompl/base/samplers/InformedStateSampler.h"
#include "ompl/util/Exception.h"
#include "ompl/base/OptimizationObjective.h"
// The goal definitions
#include "ompl/base/Goal.h"

namespace ompl
{
    namespace base
    {
        //InformedSampler
        InformedSampler::InformedSampler(const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls)
          : probDefn_(probDefn),
            space_(probDefn->getSpaceInformation()->getStateSpace()),
            numIters_(maxNumberCalls)
        {
            // Sanity check the problem.
            // Check that there is an optimization objective
            if (probDefn_->hasOptimizationObjective() == false)
            {
                throw Exception ("InformedSampler: An optimization objective must be specified at construction.");
            }
            // No else

            // Make sure we have at least one start and warn if we have more than one
            if (probDefn_->getStartStateCount() == 0u)
            {
                throw Exception ("InformedSampler: At least one start state must be specified at construction.");
            }
            // No else

            // Store the optimization objective for later ease.
            opt_ = probDefn_->getOptimizationObjective();
        }

        double InformedSampler::getInformedMeasure(const Cost &minCost, const Cost &maxCost) const
        {
            // Subtract the measures defined by the max and min costs. These will be defined in the deriving class.
            return getInformedMeasure(maxCost) - getInformedMeasure(minCost);
        }

        Cost InformedSampler::heuristicSolnCost(const State *statePtr) const
        {
            // Return the best heuristic estimate of the cost-to-come and cost-to-go from the state considering all starts.

            // If there's only one start, be simple:
            if (probDefn_->getStartStateCount() == 1u)
            {
                // Calculate and from the one and only start
                return opt_->combineCosts(opt_->motionCostHeuristic(probDefn_->getStartState(0u), statePtr), opt_->costToGo(statePtr, probDefn_->getGoal().get()));
            }
            else
            {
                // Calculate and return the best

                // Variable
                // The best cost so far
                Cost bestCost = opt_->infiniteCost();

                // Iterate over each start and store the best
                for (unsigned int i = 0u; i < probDefn_->getStartStateCount(); ++i)
                {
                    // Store the best
                    bestCost = opt_->betterCost(bestCost, opt_->combineCosts(opt_->motionCostHeuristic(probDefn_->getStartState(i), statePtr), opt_->costToGo(statePtr, probDefn_->getGoal().get())));
                }

                // Return the best
                return bestCost;
            }
        }






        //InformedStateSampler
        InformedStateSampler::InformedStateSampler(const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls, const GetCurrentCostFunc &costFunc)
          : StateSampler(probDefn->getSpaceInformation()->getStateSpace().get())
        {
            // Call the common constructor with the default informed sampler
            commonConstructor(costFunc, probDefn->getOptimizationObjective()->allocInformedStateSampler(probDefn, maxNumberCalls));
        }

        InformedStateSampler::InformedStateSampler(const ProblemDefinitionPtr &probDefn, const GetCurrentCostFunc &costFunc, const InformedSamplerPtr &infSampler)
          : StateSampler(probDefn->getSpaceInformation()->getStateSpace().get())
        {
            // Call the common constructor with the given informed sampler
            commonConstructor(costFunc, infSampler);
        }

        void InformedStateSampler::commonConstructor(const GetCurrentCostFunc &costFunc, const InformedSamplerPtr &infSampler)
        {
            // Store the cost function
            bestCostFunc_ = costFunc;

            // Store the informed sampler
            infSampler_ = infSampler;

            // Allocate a base sampler
            baseSampler_ = StateSampler::space_->allocDefaultStateSampler();
        }

        void InformedStateSampler::sampleUniform(State *statePtr)
        {
            // Variable
            // Whether informed sampling was successful
            bool informedSuccess;

            // Call sample uniform with the current best cost, check returning function:
            informedSuccess = infSampler_->sampleUniform(statePtr, bestCostFunc_());

            // If we were unsuccessful, return a regular sample
            if (informedSuccess == false)
            {
                baseSampler_->sampleUniform(statePtr);
            }
            // No else.
        }

        void InformedStateSampler::sampleUniformNear(State *statePtr, const State *near, const double distance)
        {
            //Warn:
            OMPL_WARN("sampleUniformNear is not informed.");
            return baseSampler_->sampleUniformNear(statePtr, near, distance);
        }

        void InformedStateSampler::sampleGaussian(State *statePtr, const State *mean, const double stdDev)
        {
            //Warn:
            OMPL_WARN("sampleGaussian is not informed.");
            return baseSampler_->sampleGaussian(statePtr, mean, stdDev);
        }
    }; // base
};  // ompl