RejectionInfSampler.cpp
1/*********************************************************************
2* Software License Agreement (BSD License)
3*
4* Copyright (c) 2014, University of Toronto
5* All rights reserved.
6*
7* Redistribution and use in source and binary forms, with or without
8* modification, are permitted provided that the following conditions
9* are met:
10*
11* * Redistributions of source code must retain the above copyright
12* notice, this list of conditions and the following disclaimer.
13* * Redistributions in binary form must reproduce the above
14* copyright notice, this list of conditions and the following
15* disclaimer in the documentation and/or other materials provided
16* with the distribution.
17* * Neither the name of the University of Toronto nor the names of its
18* contributors may be used to endorse or promote products derived
19* from this software without specific prior written permission.
20*
21* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
22* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
23* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
24* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
25* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
26* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
27* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
28* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
29* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
30* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
31* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
32* POSSIBILITY OF SUCH DAMAGE.
33*********************************************************************/
34
35/* Authors: Jonathan Gammell */
36
37#include "ompl/base/samplers/informed/RejectionInfSampler.h"
38#include "ompl/base/OptimizationObjective.h"
39
40namespace ompl
41{
42 namespace base
43 {
44 // The default rejection-sampling class:
45 RejectionInfSampler::RejectionInfSampler(const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls)
46 : InformedSampler(probDefn, maxNumberCalls)
47 {
48 // Create the basic sampler
49 baseSampler_ = InformedSampler::space_->allocDefaultStateSampler();
50
51 // Warn if a cost-to-go heuristic is not defined
52 if (!InformedSampler::opt_->hasCostToGoHeuristic())
53 {
54 OMPL_WARN("RejectionInfSampler: The optimization objective does not have a cost-to-go heuristic "
55 "defined. Informed sampling will likely have little to no effect.");
56 }
57 // No else
58 }
59
60 bool RejectionInfSampler::sampleUniform(State *statePtr, const Cost &maxCost)
61 {
62 // Variable
63 // The persistent iteration counter:
64 unsigned int iter = 0u;
65
66 // Call the sampleUniform helper function with my iteration counter:
67 return sampleUniform(statePtr, maxCost, &iter);
68 }
69
70 bool RejectionInfSampler::sampleUniform(State *statePtr, const Cost &minCost, const Cost &maxCost)
71 {
72 // Variable
73 // Whether we were successful in creating an informed sample. Initially not:
74 bool foundSample = false;
75
76 // Spend numIters_ iterations trying to find an informed sample:
77 for (unsigned int i = 0u; i < InformedSampler::numIters_ && !foundSample; ++i)
78 {
79 // Call the helper function for the larger cost. It will move our iteration counter:
80 foundSample = sampleUniform(statePtr, maxCost, &i);
81
82 // Did we find a sample?
83 if (foundSample)
84 {
85 // We did, but it only satisfied the upper bound. Check that it meets the lower bound.
86
87 // Variables
88 // The cost of the sample we found:
89 Cost sampledCost = InformedSampler::heuristicSolnCost(statePtr);
90
91 // Check if the sample's cost is greater than or equal to the lower bound
92 foundSample = InformedSampler::opt_->isCostEquivalentTo(minCost, sampledCost) ||
93 InformedSampler::opt_->isCostBetterThan(minCost, sampledCost);
94 }
95 // No else, no sample was found.
96 }
97
98 // One way or the other, we're done:
99 return foundSample;
100 }
101
103 {
104 return false;
105 }
106
107 double RejectionInfSampler::getInformedMeasure(const Cost & /*currentCost*/) const
108 {
109 return InformedSampler::space_->getMeasure();
110 }
111
112 double RejectionInfSampler::getInformedMeasure(const Cost & /*minCost*/, const Cost & /*maxCost*/) const
113 {
114 return InformedSampler::space_->getMeasure();
115 }
116
117 bool RejectionInfSampler::sampleUniform(State *statePtr, const Cost &maxCost, unsigned int *iterPtr)
118 {
119 // Variable
120 // Whether we were successful in creating an informed sample. Initially not:
121 bool foundSample = false;
122
123 // Make numIters_ attempts at finding a sample whose heuristic estimate of solution cost through the sample
124 // is better than maxCost by sampling the entire planning domain
125 for (/* Provided iteration counter */; *iterPtr < InformedSampler::numIters_ && !foundSample;
126 ++(*iterPtr))
127 {
128 // Get a sample:
129 baseSampler_->sampleUniform(statePtr);
130
131 // Check if it's found, i.e., if f(state) <= maxCost
132 foundSample =
133 InformedSampler::opt_->isCostBetterThan(InformedSampler::heuristicSolnCost(statePtr), maxCost);
134 }
135
136 // All done, one way or the other:
137 return foundSample;
138 }
139 }; // base
140}; // ompl
Definition of a cost value. Can represent the cost of a motion or the cost of a state.
Definition: Cost.h:48
An abstract class for the concept of using information about the state space and the current solution...
OptimizationObjectivePtr opt_
A copy of the optimization objective.
virtual Cost heuristicSolnCost(const State *statePtr) const
A helper function to calculate the heuristic estimate of the solution cost for a given state using th...
unsigned int numIters_
The number of iterations I'm allowed to attempt.
StateSpacePtr space_
A copy of the state space.
A shared pointer wrapper for ompl::base::ProblemDefinition.
RejectionInfSampler(const ProblemDefinitionPtr &probDefn, unsigned int maxNumberCalls)
Construct a rejection sampler that only generates states with a heuristic solution estimate that is l...
bool hasInformedMeasure() const override
Whether the sampler can provide a measure of the informed subset.
bool sampleUniform(State *statePtr, const Cost &maxCost) override
Sample uniformly in the subset of the state space whose heuristic solution estimates are less than th...
double getInformedMeasure(const Cost &) const override
The measure of the subset of the state space defined by the current solution cost that is being searc...
Definition of an abstract state.
Definition: State.h:50
#define OMPL_WARN(fmt,...)
Log a formatted warning string.
Definition: Console.h:66
Main namespace. Contains everything in this library.