ompl/geometric/planners/rrt/src/RRT.cpp
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00034 
00035 /* Author: Ioan Sucan */
00036 
00037 #include "ompl/geometric/planners/rrt/RRT.h"
00038 #include "ompl/base/goals/GoalSampleableRegion.h"
00039 #include "ompl/tools/config/SelfConfig.h"
00040 #include <limits>
00041 
00042 ompl::geometric::RRT::RRT(const base::SpaceInformationPtr &si) : base::Planner(si, "RRT")
00043 {
00044     specs_.approximateSolutions = true;
00045     specs_.directed = true;
00046 
00047     goalBias_ = 0.05;
00048     maxDistance_ = 0.0;
00049     lastGoalMotion_ = NULL;
00050 
00051     Planner::declareParam<double>("range", this, &RRT::setRange, &RRT::getRange, "0.:1.:10000.");
00052     Planner::declareParam<double>("goal_bias", this, &RRT::setGoalBias, &RRT::getGoalBias, "0.:.05:1.");
00053 }
00054 
00055 ompl::geometric::RRT::~RRT()
00056 {
00057     freeMemory();
00058 }
00059 
00060 void ompl::geometric::RRT::clear()
00061 {
00062     Planner::clear();
00063     sampler_.reset();
00064     freeMemory();
00065     if (nn_)
00066         nn_->clear();
00067     lastGoalMotion_ = NULL;
00068 }
00069 
00070 void ompl::geometric::RRT::setup()
00071 {
00072     Planner::setup();
00073     tools::SelfConfig sc(si_, getName());
00074     sc.configurePlannerRange(maxDistance_);
00075 
00076     if (!nn_)
00077         nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion*>(si_->getStateSpace()));
00078     nn_->setDistanceFunction(boost::bind(&RRT::distanceFunction, this, _1, _2));
00079 }
00080 
00081 void ompl::geometric::RRT::freeMemory()
00082 {
00083     if (nn_)
00084     {
00085         std::vector<Motion*> motions;
00086         nn_->list(motions);
00087         for (unsigned int i = 0 ; i < motions.size() ; ++i)
00088         {
00089             if (motions[i]->state)
00090                 si_->freeState(motions[i]->state);
00091             delete motions[i];
00092         }
00093     }
00094 }
00095 
00096 ompl::base::PlannerStatus ompl::geometric::RRT::solve(const base::PlannerTerminationCondition &ptc)
00097 {
00098     checkValidity();
00099     base::Goal                 *goal   = pdef_->getGoal().get();
00100     base::GoalSampleableRegion *goal_s = dynamic_cast<base::GoalSampleableRegion*>(goal);
00101 
00102     while (const base::State *st = pis_.nextStart())
00103     {
00104         Motion *motion = new Motion(si_);
00105         si_->copyState(motion->state, st);
00106         nn_->add(motion);
00107     }
00108 
00109     if (nn_->size() == 0)
00110     {
00111         OMPL_ERROR("%s: There are no valid initial states!", getName().c_str());
00112         return base::PlannerStatus::INVALID_START;
00113     }
00114 
00115     if (!sampler_)
00116         sampler_ = si_->allocStateSampler();
00117 
00118     OMPL_INFORM("%s: Starting planning with %u states already in datastructure", getName().c_str(), nn_->size());
00119 
00120     Motion *solution  = NULL;
00121     Motion *approxsol = NULL;
00122     double  approxdif = std::numeric_limits<double>::infinity();
00123     Motion *rmotion   = new Motion(si_);
00124     base::State *rstate = rmotion->state;
00125     base::State *xstate = si_->allocState();
00126 
00127     while (ptc == false)
00128     {
00129 
00130         /* sample random state (with goal biasing) */
00131         if (goal_s && rng_.uniform01() < goalBias_ && goal_s->canSample())
00132             goal_s->sampleGoal(rstate);
00133         else
00134             sampler_->sampleUniform(rstate);
00135 
00136         /* find closest state in the tree */
00137         Motion *nmotion = nn_->nearest(rmotion);
00138         base::State *dstate = rstate;
00139 
00140         /* find state to add */
00141         double d = si_->distance(nmotion->state, rstate);
00142         if (d > maxDistance_)
00143         {
00144             si_->getStateSpace()->interpolate(nmotion->state, rstate, maxDistance_ / d, xstate);
00145             dstate = xstate;
00146         }
00147 
00148         if (si_->checkMotion(nmotion->state, dstate))
00149         {
00150             /* create a motion */
00151             Motion *motion = new Motion(si_);
00152             si_->copyState(motion->state, dstate);
00153             motion->parent = nmotion;
00154 
00155             nn_->add(motion);
00156             double dist = 0.0;
00157             bool sat = goal->isSatisfied(motion->state, &dist);
00158             if (sat)
00159             {
00160                 approxdif = dist;
00161                 solution = motion;
00162                 break;
00163             }
00164             if (dist < approxdif)
00165             {
00166                 approxdif = dist;
00167                 approxsol = motion;
00168             }
00169         }
00170     }
00171 
00172     bool solved = false;
00173     bool approximate = false;
00174     if (solution == NULL)
00175     {
00176         solution = approxsol;
00177         approximate = true;
00178     }
00179 
00180     if (solution != NULL)
00181     {
00182         lastGoalMotion_ = solution;
00183 
00184         /* construct the solution path */
00185         std::vector<Motion*> mpath;
00186         while (solution != NULL)
00187         {
00188             mpath.push_back(solution);
00189             solution = solution->parent;
00190         }
00191 
00192         /* set the solution path */
00193         PathGeometric *path = new PathGeometric(si_);
00194         for (int i = mpath.size() - 1 ; i >= 0 ; --i)
00195             path->append(mpath[i]->state);
00196         pdef_->addSolutionPath(base::PathPtr(path), approximate, approxdif, getName());
00197         solved = true;
00198     }
00199 
00200     si_->freeState(xstate);
00201     if (rmotion->state)
00202         si_->freeState(rmotion->state);
00203     delete rmotion;
00204 
00205     OMPL_INFORM("%s: Created %u states", getName().c_str(), nn_->size());
00206 
00207     return base::PlannerStatus(solved, approximate);
00208 }
00209 
00210 void ompl::geometric::RRT::getPlannerData(base::PlannerData &data) const
00211 {
00212     Planner::getPlannerData(data);
00213 
00214     std::vector<Motion*> motions;
00215     if (nn_)
00216         nn_->list(motions);
00217 
00218     if (lastGoalMotion_)
00219         data.addGoalVertex(base::PlannerDataVertex(lastGoalMotion_->state));
00220 
00221     for (unsigned int i = 0 ; i < motions.size() ; ++i)
00222     {
00223         if (motions[i]->parent == NULL)
00224             data.addStartVertex(base::PlannerDataVertex(motions[i]->state));
00225         else
00226             data.addEdge(base::PlannerDataVertex(motions[i]->parent->state),
00227                          base::PlannerDataVertex(motions[i]->state));
00228     }
00229 }
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