RRTConnect.cpp
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34 
35 /* Author: Ioan Sucan */
36 
37 #include "ompl/geometric/planners/rrt/RRTConnect.h"
38 #include "ompl/base/goals/GoalSampleableRegion.h"
39 #include "ompl/tools/config/SelfConfig.h"
40 #include "ompl/util/String.h"
41 
43  : base::Planner(si, addIntermediateStates ? "RRTConnectIntermediate" : "RRTConnect")
44 {
46  specs_.directed = true;
47 
48  Planner::declareParam<double>("range", this, &RRTConnect::setRange, &RRTConnect::getRange, "0.:1.:10000.");
49  Planner::declareParam<bool>("intermediate_states", this, &RRTConnect::setIntermediateStates,
51 
52  connectionPoint_ = std::make_pair<base::State *, base::State *>(nullptr, nullptr);
53  distanceBetweenTrees_ = std::numeric_limits<double>::infinity();
54  addIntermediateStates_ = addIntermediateStates;
55 }
56 
57 ompl::geometric::RRTConnect::~RRTConnect()
58 {
59  freeMemory();
60 }
61 
63 {
64  Planner::setup();
65  tools::SelfConfig sc(si_, getName());
66  sc.configurePlannerRange(maxDistance_);
67 
68  if (!tStart_)
69  tStart_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
70  if (!tGoal_)
71  tGoal_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
72  tStart_->setDistanceFunction([this](const Motion *a, const Motion *b) { return distanceFunction(a, b); });
73  tGoal_->setDistanceFunction([this](const Motion *a, const Motion *b) { return distanceFunction(a, b); });
74 }
75 
77 {
78  std::vector<Motion *> motions;
79 
80  if (tStart_)
81  {
82  tStart_->list(motions);
83  for (auto &motion : motions)
84  {
85  if (motion->state != nullptr)
86  si_->freeState(motion->state);
87  delete motion;
88  }
89  }
90 
91  if (tGoal_)
92  {
93  tGoal_->list(motions);
94  for (auto &motion : motions)
95  {
96  if (motion->state != nullptr)
97  si_->freeState(motion->state);
98  delete motion;
99  }
100  }
101 }
102 
104 {
105  Planner::clear();
106  sampler_.reset();
107  freeMemory();
108  if (tStart_)
109  tStart_->clear();
110  if (tGoal_)
111  tGoal_->clear();
112  connectionPoint_ = std::make_pair<base::State *, base::State *>(nullptr, nullptr);
113  distanceBetweenTrees_ = std::numeric_limits<double>::infinity();
114 }
115 
117  Motion *rmotion)
118 {
119  /* find closest state in the tree */
120  Motion *nmotion = tree->nearest(rmotion);
121 
122  /* assume we can reach the state we go towards */
123  bool reach = true;
124 
125  /* find state to add */
126  base::State *dstate = rmotion->state;
127  double d = si_->distance(nmotion->state, rmotion->state);
128  if (d > maxDistance_)
129  {
130  si_->getStateSpace()->interpolate(nmotion->state, rmotion->state, maxDistance_ / d, tgi.xstate);
131 
132  /* Check if we have moved at all. Due to some stranger state spaces (e.g., the constrained state spaces),
133  * interpolate can fail and no progress is made. Without this check, the algorithm gets stuck in a loop as it
134  * thinks it is making progress, when none is actually occurring. */
135  if (si_->equalStates(nmotion->state, tgi.xstate))
136  return TRAPPED;
137 
138  dstate = tgi.xstate;
139  reach = false;
140  }
141 
142  bool validMotion = tgi.start ? si_->checkMotion(nmotion->state, dstate) :
143  si_->isValid(dstate) && si_->checkMotion(dstate, nmotion->state);
144 
145  if (!validMotion)
146  return TRAPPED;
147 
148  if (addIntermediateStates_)
149  {
150  const base::State *astate = tgi.start ? nmotion->state : dstate;
151  const base::State *bstate = tgi.start ? dstate : nmotion->state;
152 
153  std::vector<base::State *> states;
154  const unsigned int count = si_->getStateSpace()->validSegmentCount(astate, bstate);
155 
156  if (si_->getMotionStates(astate, bstate, states, count, true, true))
157  si_->freeState(states[0]);
158 
159  for (std::size_t i = 1; i < states.size(); ++i)
160  {
161  Motion *motion = new Motion;
162  motion->state = states[i];
163  motion->parent = nmotion;
164  motion->root = nmotion->root;
165  tree->add(motion);
166 
167  nmotion = motion;
168  }
169 
170  tgi.xmotion = nmotion;
171  }
172  else
173  {
174  Motion *motion = new Motion(si_);
175  si_->copyState(motion->state, dstate);
176  motion->parent = nmotion;
177  motion->root = nmotion->root;
178  tree->add(motion);
179 
180  tgi.xmotion = motion;
181  }
182 
183  return reach ? REACHED : ADVANCED;
184 }
185 
187 {
188  checkValidity();
189  auto *goal = dynamic_cast<base::GoalSampleableRegion *>(pdef_->getGoal().get());
190 
191  if (goal == nullptr)
192  {
193  OMPL_ERROR("%s: Unknown type of goal", getName().c_str());
195  }
196 
197  while (const base::State *st = pis_.nextStart())
198  {
199  auto *motion = new Motion(si_);
200  si_->copyState(motion->state, st);
201  motion->root = motion->state;
202  tStart_->add(motion);
203  }
204 
205  if (tStart_->size() == 0)
206  {
207  OMPL_ERROR("%s: Motion planning start tree could not be initialized!", getName().c_str());
209  }
210 
211  if (!goal->couldSample())
212  {
213  OMPL_ERROR("%s: Insufficient states in sampleable goal region", getName().c_str());
215  }
216 
217  if (!sampler_)
218  sampler_ = si_->allocStateSampler();
219 
220  OMPL_INFORM("%s: Starting planning with %d states already in datastructure", getName().c_str(),
221  (int)(tStart_->size() + tGoal_->size()));
222 
223  TreeGrowingInfo tgi;
224  tgi.xstate = si_->allocState();
225 
226  Motion *approxsol = nullptr;
227  double approxdif = std::numeric_limits<double>::infinity();
228  auto *rmotion = new Motion(si_);
229  base::State *rstate = rmotion->state;
230  bool startTree = true;
231  bool solved = false;
232 
233  while (!ptc)
234  {
235  TreeData &tree = startTree ? tStart_ : tGoal_;
236  tgi.start = startTree;
237  startTree = !startTree;
238  TreeData &otherTree = startTree ? tStart_ : tGoal_;
239 
240  if (tGoal_->size() == 0 || pis_.getSampledGoalsCount() < tGoal_->size() / 2)
241  {
242  const base::State *st = tGoal_->size() == 0 ? pis_.nextGoal(ptc) : pis_.nextGoal();
243  if (st != nullptr)
244  {
245  auto *motion = new Motion(si_);
246  si_->copyState(motion->state, st);
247  motion->root = motion->state;
248  tGoal_->add(motion);
249  }
250 
251  if (tGoal_->size() == 0)
252  {
253  OMPL_ERROR("%s: Unable to sample any valid states for goal tree", getName().c_str());
254  break;
255  }
256  }
257 
258  /* sample random state */
259  sampler_->sampleUniform(rstate);
260 
261  GrowState gs = growTree(tree, tgi, rmotion);
262 
263  if (gs != TRAPPED)
264  {
265  /* remember which motion was just added */
266  Motion *addedMotion = tgi.xmotion;
267 
268  /* attempt to connect trees */
269 
270  /* if reached, it means we used rstate directly, no need top copy again */
271  if (gs != REACHED)
272  si_->copyState(rstate, tgi.xstate);
273 
274  GrowState gsc = ADVANCED;
275  tgi.start = startTree;
276  while (gsc == ADVANCED)
277  gsc = growTree(otherTree, tgi, rmotion);
278 
279  /* update distance between trees */
280  const double newDist = tree->getDistanceFunction()(addedMotion, otherTree->nearest(addedMotion));
281  if (newDist < distanceBetweenTrees_)
282  {
283  distanceBetweenTrees_ = newDist;
284  // OMPL_INFORM("Estimated distance to go: %f", distanceBetweenTrees_);
285  }
286 
287  Motion *startMotion = startTree ? tgi.xmotion : addedMotion;
288  Motion *goalMotion = startTree ? addedMotion : tgi.xmotion;
289 
290  /* if we connected the trees in a valid way (start and goal pair is valid)*/
291  if (gsc == REACHED && goal->isStartGoalPairValid(startMotion->root, goalMotion->root))
292  {
293  // it must be the case that either the start tree or the goal tree has made some progress
294  // so one of the parents is not nullptr. We go one step 'back' to avoid having a duplicate state
295  // on the solution path
296  if (startMotion->parent != nullptr)
297  startMotion = startMotion->parent;
298  else
299  goalMotion = goalMotion->parent;
300 
301  connectionPoint_ = std::make_pair(startMotion->state, goalMotion->state);
302 
303  /* construct the solution path */
304  Motion *solution = startMotion;
305  std::vector<Motion *> mpath1;
306  while (solution != nullptr)
307  {
308  mpath1.push_back(solution);
309  solution = solution->parent;
310  }
311 
312  solution = goalMotion;
313  std::vector<Motion *> mpath2;
314  while (solution != nullptr)
315  {
316  mpath2.push_back(solution);
317  solution = solution->parent;
318  }
319 
320  auto path(std::make_shared<PathGeometric>(si_));
321  path->getStates().reserve(mpath1.size() + mpath2.size());
322  for (int i = mpath1.size() - 1; i >= 0; --i)
323  path->append(mpath1[i]->state);
324  for (auto &i : mpath2)
325  path->append(i->state);
326 
327  pdef_->addSolutionPath(path, false, 0.0, getName());
328  solved = true;
329  break;
330  }
331  else
332  {
333  // We didn't reach the goal, but if we were extending the start
334  // tree, then we can mark/improve the approximate path so far.
335  if (!startTree)
336  {
337  // We were working from the startTree.
338  double dist = 0.0;
339  goal->isSatisfied(tgi.xmotion->state, &dist);
340  if (dist < approxdif)
341  {
342  approxdif = dist;
343  approxsol = tgi.xmotion;
344  }
345  }
346  }
347  }
348  }
349 
350  si_->freeState(tgi.xstate);
351  si_->freeState(rstate);
352  delete rmotion;
353 
354  OMPL_INFORM("%s: Created %u states (%u start + %u goal)", getName().c_str(), tStart_->size() + tGoal_->size(),
355  tStart_->size(), tGoal_->size());
356 
357  if (approxsol && !solved)
358  {
359  /* construct the solution path */
360  std::vector<Motion *> mpath;
361  while (approxsol != nullptr)
362  {
363  mpath.push_back(approxsol);
364  approxsol = approxsol->parent;
365  }
366 
367  auto path(std::make_shared<PathGeometric>(si_));
368  for (int i = mpath.size() - 1; i >= 0; --i)
369  path->append(mpath[i]->state);
370  pdef_->addSolutionPath(path, true, approxdif, getName());
372  }
373 
375 }
376 
378 {
379  Planner::getPlannerData(data);
380 
381  std::vector<Motion *> motions;
382  if (tStart_)
383  tStart_->list(motions);
384 
385  for (auto &motion : motions)
386  {
387  if (motion->parent == nullptr)
388  data.addStartVertex(base::PlannerDataVertex(motion->state, 1));
389  else
390  {
391  data.addEdge(base::PlannerDataVertex(motion->parent->state, 1), base::PlannerDataVertex(motion->state, 1));
392  }
393  }
394 
395  motions.clear();
396  if (tGoal_)
397  tGoal_->list(motions);
398 
399  for (auto &motion : motions)
400  {
401  if (motion->parent == nullptr)
402  data.addGoalVertex(base::PlannerDataVertex(motion->state, 2));
403  else
404  {
405  // The edges in the goal tree are reversed to be consistent with start tree
406  data.addEdge(base::PlannerDataVertex(motion->state, 2), base::PlannerDataVertex(motion->parent->state, 2));
407  }
408  }
409 
410  // Add the edge connecting the two trees
411  data.addEdge(data.vertexIndex(connectionPoint_.first), data.vertexIndex(connectionPoint_.second));
412 
413  // Add some info.
414  data.properties["approx goal distance REAL"] = ompl::toString(distanceBetweenTrees_);
415 }
Object containing planner generated vertex and edge data. It is assumed that all vertices are unique...
Definition: PlannerData.h:174
double distanceBetweenTrees_
Distance between the nearest pair of start tree and goal tree nodes.
Definition: RRTConnect.h:193
The planner failed to find a solution.
Definition: PlannerStatus.h:62
void setRange(double distance)
Set the range the planner is supposed to use.
Definition: RRTConnect.h:94
GoalType recognizedGoal
The type of goal specification the planner can use.
Definition: Planner.h:197
unsigned int addGoalVertex(const PlannerDataVertex &v)
Adds the given vertex to the graph data, and marks it as a start vertex. The vertex index is returned...
void clear() override
Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() wil...
Definition: RRTConnect.cpp:103
bool getIntermediateStates() const
Return true if the intermediate states generated along motions are to be added to the tree itself...
Definition: RRTConnect.h:77
Encapsulate a termination condition for a motion planner. Planners will call operator() to decide whe...
unsigned int vertexIndex(const PlannerDataVertex &v) const
Return the index for the vertex associated with the given data. INVALID_INDEX is returned if this ver...
bool addIntermediateStates_
Flag indicating whether intermediate states are added to the built tree of motions.
Definition: RRTConnect.h:184
void freeMemory()
Free the memory allocated by this planner.
Definition: RRTConnect.cpp:76
std::pair< base::State *, base::State * > connectionPoint_
The pair of states in each tree connected during planning. Used for PlannerData computation.
Definition: RRTConnect.h:190
RRTConnect(const base::SpaceInformationPtr &si, bool addIntermediateStates=false)
Constructor.
Definition: RRTConnect.cpp:42
bool directed
Flag indicating whether the planner is able to account for the fact that the validity of a motion fro...
Definition: Planner.h:213
Base class for a vertex in the PlannerData structure. All derived classes must implement the clone an...
Definition: PlannerData.h:58
Invalid start state or no start state specified.
Definition: PlannerStatus.h:56
Abstract definition of a goal region that can be sampled.
double getRange() const
Get the range the planner is using.
Definition: RRTConnect.h:100
The goal is of a type that a planner does not recognize.
Definition: PlannerStatus.h:60
Representation of a motion.
Definition: RRTConnect.h:121
#define OMPL_ERROR(fmt,...)
Log a formatted error string.
Definition: Console.h:64
The planner found an exact solution.
Definition: PlannerStatus.h:66
A class to store the exit status of Planner::solve()
Definition: PlannerStatus.h:48
virtual bool addEdge(unsigned int v1, unsigned int v2, const PlannerDataEdge &edge=PlannerDataEdge(), Cost weight=Cost(1.0))
Adds a directed edge between the given vertex indexes. An optional edge structure and weight can be s...
A shared pointer wrapper for ompl::base::SpaceInformation.
GrowState
The state of the tree after an attempt to extend it.
Definition: RRTConnect.h:149
unsigned int addStartVertex(const PlannerDataVertex &v)
Adds the given vertex to the graph data, and marks it as a start vertex. The vertex index is returned...
Definition of an abstract state.
Definition: State.h:49
PlannerSpecs specs_
The specifications of the planner (its capabilities)
Definition: Planner.h:418
The planner found an approximate solution.
Definition: PlannerStatus.h:64
void setup() override
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition: RRTConnect.cpp:62
void configurePlannerRange(double &range)
Compute what a good length for motion segments is.
Definition: SelfConfig.cpp:225
This class contains methods that automatically configure various parameters for motion planning...
Definition: SelfConfig.h:59
std::string toString(float val)
convert float to string using classic "C" locale semantics
Definition: String.cpp:82
Information attached to growing a tree of motions (used internally)
Definition: RRTConnect.h:141
void getPlannerData(base::PlannerData &data) const override
Get information about the current run of the motion planner. Repeated calls to this function will upd...
Definition: RRTConnect.cpp:377
std::shared_ptr< NearestNeighbors< Motion * > > TreeData
A nearest-neighbor datastructure representing a tree of motions.
Definition: RRTConnect.h:138
base::PlannerStatus solve(const base::PlannerTerminationCondition &ptc) override
Function that can solve the motion planning problem. This function can be called multiple times on th...
Definition: RRTConnect.cpp:186
void setIntermediateStates(bool addIntermediateStates)
Specify whether the intermediate states generated along motions are to be added to the tree itself...
Definition: RRTConnect.h:84
GrowState growTree(TreeData &tree, TreeGrowingInfo &tgi, Motion *rmotion)
Grow a tree towards a random state.
Definition: RRTConnect.cpp:116
std::map< std::string, std::string > properties
Any extra properties (key-value pairs) the planner can set.
Definition: PlannerData.h:410
This bit is set if casting to sampleable goal regions (ompl::base::GoalSampleableRegion) is possible...
Definition: GoalTypes.h:56
#define OMPL_INFORM(fmt,...)
Log a formatted information string.
Definition: Console.h:68