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SST.cpp
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34
35/* Authors: Zakary Littlefield */
36
37#include "ompl/geometric/planners/sst/SST.h"
38#include "ompl/base/goals/GoalSampleableRegion.h"
39#include "ompl/base/objectives/MinimaxObjective.h"
40#include "ompl/base/objectives/MaximizeMinClearanceObjective.h"
41#include "ompl/base/objectives/PathLengthOptimizationObjective.h"
42#include "ompl/tools/config/SelfConfig.h"
43#include <limits>
44
45ompl::geometric::SST::SST(const base::SpaceInformationPtr &si) : base::Planner(si, "SST")
46{
48 specs_.directed = true;
49 prevSolution_.clear();
50
51 Planner::declareParam<double>("range", this, &SST::setRange, &SST::getRange, ".1:.1:100");
52 Planner::declareParam<double>("goal_bias", this, &SST::setGoalBias, &SST::getGoalBias, "0.:.05:1.");
53 Planner::declareParam<double>("selection_radius", this, &SST::setSelectionRadius, &SST::getSelectionRadius, "0.:.1:"
54 "100");
55 Planner::declareParam<double>("pruning_radius", this, &SST::setPruningRadius, &SST::getPruningRadius, "0.:.1:100");
56
57 addPlannerProgressProperty("best cost REAL", [this] { return std::to_string(this->prevSolutionCost_.value()); });
58}
59
60ompl::geometric::SST::~SST()
61{
62 freeMemory();
63}
64
66{
68 if (!nn_)
69 nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
70 nn_->setDistanceFunction([this](const Motion *a, const Motion *b)
71 {
72 return distanceFunction(a, b);
73 });
74 if (!witnesses_)
75 witnesses_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
76 witnesses_->setDistanceFunction([this](const Motion *a, const Motion *b)
77 {
78 return distanceFunction(a, b);
79 });
80
81 if (pdef_)
82 {
83 if (pdef_->hasOptimizationObjective())
84 {
85 opt_ = pdef_->getOptimizationObjective();
86 if (dynamic_cast<base::MaximizeMinClearanceObjective *>(opt_.get()) ||
87 dynamic_cast<base::MinimaxObjective *>(opt_.get()))
88 OMPL_WARN("%s: Asymptotic near-optimality has only been proven with Lipschitz continuous cost "
89 "functions w.r.t. state and control. This optimization objective will result in undefined "
90 "behavior",
91 getName().c_str());
92 }
93 else
94 {
95 OMPL_WARN("%s: No optimization object set. Using path length", getName().c_str());
96 opt_ = std::make_shared<base::PathLengthOptimizationObjective>(si_);
97 pdef_->setOptimizationObjective(opt_);
98 }
99 }
100 else
101 {
102 OMPL_WARN("%s: No optimization object set. Using path length", getName().c_str());
103 opt_ = std::make_shared<base::PathLengthOptimizationObjective>(si_);
104 }
105 prevSolutionCost_ = opt_->infiniteCost();
106}
107
109{
110 Planner::clear();
111 sampler_.reset();
112 freeMemory();
113 if (nn_)
114 nn_->clear();
115 if (witnesses_)
116 witnesses_->clear();
117 if (opt_)
118 prevSolutionCost_ = opt_->infiniteCost();
119}
120
122{
123 if (nn_)
124 {
125 std::vector<Motion *> motions;
126 nn_->list(motions);
127 for (auto &motion : motions)
128 {
129 if (motion->state_)
130 si_->freeState(motion->state_);
131 delete motion;
132 }
133 }
134 if (witnesses_)
135 {
136 std::vector<Motion *> witnesses;
137 witnesses_->list(witnesses);
138 for (auto &witness : witnesses)
139 {
140 if (witness->state_)
141 si_->freeState(witness->state_);
142 delete witness;
143 }
144 }
145
146 for (auto &i : prevSolution_)
147 {
148 if (i)
149 si_->freeState(i);
150 }
151 prevSolution_.clear();
152}
153
155{
156 std::vector<Motion *> ret;
157 Motion *selected = nullptr;
158 base::Cost bestCost = opt_->infiniteCost();
159 nn_->nearestR(sample, selectionRadius_, ret);
160 for (auto &i : ret)
161 {
162 if (!i->inactive_ && opt_->isCostBetterThan(i->accCost_, bestCost))
163 {
164 bestCost = i->accCost_;
165 selected = i;
166 }
167 }
168 if (selected == nullptr)
169 {
170 int k = 1;
171 while (selected == nullptr)
172 {
173 nn_->nearestK(sample, k, ret);
174 for (unsigned int i = 0; i < ret.size() && selected == nullptr; i++)
175 if (!ret[i]->inactive_)
176 selected = ret[i];
177 k += 5;
178 }
179 }
180 return selected;
181}
182
184{
185 if (witnesses_->size() > 0)
186 {
187 auto *closest = static_cast<Witness *>(witnesses_->nearest(node));
188 if (distanceFunction(closest, node) > pruningRadius_)
189 {
190 closest = new Witness(si_);
191 closest->linkRep(node);
192 si_->copyState(closest->state_, node->state_);
193 witnesses_->add(closest);
194 }
195 return closest;
196 }
197 else
198 {
199 auto *closest = new Witness(si_);
200 closest->linkRep(node);
201 si_->copyState(closest->state_, node->state_);
202 witnesses_->add(closest);
203 return closest;
204 }
205}
206
208{
209 // sample random point to serve as a direction
210 base::State *xstate = si_->allocState();
211 sampler_->sampleUniform(xstate);
212
213 // sample length of step from (0 - maxDistance_]
214 double step = rng_.uniformReal(0, maxDistance_);
215
216 // take a step of length step towards the random state
217 double d = si_->distance(m->state_, xstate);
218 si_->getStateSpace()->interpolate(m->state_, xstate, step / d, xstate);
219 si_->enforceBounds(xstate);
220
221 return xstate;
222}
223
225{
226 checkValidity();
227 base::Goal *goal = pdef_->getGoal().get();
228 auto *goal_s = dynamic_cast<base::GoalSampleableRegion *>(goal);
229
230 while (const base::State *st = pis_.nextStart())
231 {
232 auto *motion = new Motion(si_);
233 si_->copyState(motion->state_, st);
234 nn_->add(motion);
235 motion->accCost_ = opt_->identityCost();
236 findClosestWitness(motion);
237 }
238
239 if (nn_->size() == 0)
240 {
241 OMPL_ERROR("%s: There are no valid initial states!", getName().c_str());
243 }
244
245 if (!sampler_)
246 sampler_ = si_->allocStateSampler();
247
248 const base::ReportIntermediateSolutionFn intermediateSolutionCallback = pdef_->getIntermediateSolutionCallback();
249
250 OMPL_INFORM("%s: Starting planning with %u states already in datastructure", getName().c_str(), nn_->size());
251
252 Motion *solution = nullptr;
253 Motion *approxsol = nullptr;
254 double approxdif = std::numeric_limits<double>::infinity();
255 bool sufficientlyShort = false;
256 auto *rmotion = new Motion(si_);
257 base::State *rstate = rmotion->state_;
258 base::State *xstate = si_->allocState();
259
260 unsigned iterations = 0;
261
262 while (ptc == false)
263 {
264 /* sample random state (with goal biasing) */
265 bool attemptToReachGoal = (goal_s && rng_.uniform01() < goalBias_ && goal_s->canSample());
266 if (attemptToReachGoal)
267 goal_s->sampleGoal(rstate);
268 else
269 sampler_->sampleUniform(rstate);
270
271 /* find closest state in the tree */
272 Motion *nmotion = selectNode(rmotion);
273
274 base::State *dstate = rstate;
275 double d = si_->distance(nmotion->state_, rstate);
276
277 attemptToReachGoal = rng_.uniform01() < .5;
278 if (attemptToReachGoal)
279 {
280 if (d > maxDistance_)
281 {
282 si_->getStateSpace()->interpolate(nmotion->state_, rstate, maxDistance_ / d, xstate);
283 dstate = xstate;
284 }
285 }
286 else
287 {
288 dstate = monteCarloProp(nmotion);
289 }
290
291 si_->copyState(rstate, dstate);
292
293 if (si_->checkMotion(nmotion->state_, rstate))
294 {
295 base::Cost incCost = opt_->motionCost(nmotion->state_, rstate);
296 base::Cost cost = opt_->combineCosts(nmotion->accCost_, incCost);
297 Witness *closestWitness = findClosestWitness(rmotion);
298
299 if (closestWitness->rep_ == rmotion || opt_->isCostBetterThan(cost, closestWitness->rep_->accCost_))
300 {
301 Motion *oldRep = closestWitness->rep_;
302 /* create a motion */
303 auto *motion = new Motion(si_);
304 motion->accCost_ = cost;
305 si_->copyState(motion->state_, rstate);
306
307 if (!attemptToReachGoal)
308 si_->freeState(dstate);
309 motion->parent_ = nmotion;
310 nmotion->numChildren_++;
311 closestWitness->linkRep(motion);
312
313 nn_->add(motion);
314 double dist = 0.0;
315 bool solv = goal->isSatisfied(motion->state_, &dist);
316 if (solv && opt_->isCostBetterThan(motion->accCost_, prevSolutionCost_))
317 {
318 approxdif = dist;
319 solution = motion;
320
321 for (auto &i : prevSolution_)
322 if (i)
323 si_->freeState(i);
324 prevSolution_.clear();
325 Motion *solTrav = solution;
326 while (solTrav != nullptr)
327 {
328 prevSolution_.push_back(si_->cloneState(solTrav->state_));
329 solTrav = solTrav->parent_;
330 }
331 prevSolutionCost_ = solution->accCost_;
332
333 OMPL_INFORM("Found solution with cost %.2f", solution->accCost_.value());
334 if (intermediateSolutionCallback)
335 {
336 // the callback requires a vector with const elements -> create a copy
337 std::vector<const base::State *> prevSolutionConst(prevSolution_.begin(), prevSolution_.end());
338 intermediateSolutionCallback(this, prevSolutionConst, prevSolutionCost_);
339 }
340 sufficientlyShort = opt_->isSatisfied(solution->accCost_);
341 if (sufficientlyShort)
342 {
343 break;
344 }
345 }
346 if (solution == nullptr && dist < approxdif)
347 {
348 approxdif = dist;
349 approxsol = motion;
350
351 for (auto &i : prevSolution_)
352 {
353 if (i)
354 si_->freeState(i);
355 }
356 prevSolution_.clear();
357 Motion *solTrav = approxsol;
358 while (solTrav != nullptr)
359 {
360 prevSolution_.push_back(si_->cloneState(solTrav->state_));
361 solTrav = solTrav->parent_;
362 }
363 }
364
365 if (oldRep != rmotion)
366 {
367 while (oldRep->inactive_ && oldRep->numChildren_ == 0)
368 {
369 oldRep->inactive_ = true;
370 nn_->remove(oldRep);
371
372 if (oldRep->state_)
373 si_->freeState(oldRep->state_);
374
375 oldRep->state_ = nullptr;
376 oldRep->parent_->numChildren_--;
377 Motion *oldRepParent = oldRep->parent_;
378 delete oldRep;
379 oldRep = oldRepParent;
380 }
381 }
382 }
383 }
384 iterations++;
385 }
386
387 bool solved = false;
388 bool approximate = false;
389 if (solution == nullptr)
390 {
391 solution = approxsol;
392 approximate = true;
393 }
394
395 if (solution != nullptr)
396 {
397 /* set the solution path */
398 auto path(std::make_shared<PathGeometric>(si_));
399 for (int i = prevSolution_.size() - 1; i >= 0; --i)
400 path->append(prevSolution_[i]);
401 solved = true;
402 pdef_->addSolutionPath(path, approximate, approxdif, getName());
403 }
404
405 si_->freeState(xstate);
406 if (rmotion->state_)
407 si_->freeState(rmotion->state_);
408 rmotion->state_ = nullptr;
409 delete rmotion;
410
411 OMPL_INFORM("%s: Created %u states in %u iterations", getName().c_str(), nn_->size(), iterations);
412
413 return {solved, approximate};
414}
415
417{
418 Planner::getPlannerData(data);
419
420 std::vector<Motion *> motions;
421 std::vector<Motion *> allMotions;
422 if (nn_)
423 nn_->list(motions);
424
425 for (auto &motion : motions)
426 if (motion->numChildren_ == 0)
427 allMotions.push_back(motion);
428 for (unsigned i = 0; i < allMotions.size(); i++)
429 if (allMotions[i]->getParent() != nullptr)
430 allMotions.push_back(allMotions[i]->getParent());
431
432 if (prevSolution_.size() != 0)
433 data.addGoalVertex(base::PlannerDataVertex(prevSolution_[0]));
434
435 for (auto &allMotion : allMotions)
436 {
437 if (allMotion->getParent() == nullptr)
438 data.addStartVertex(base::PlannerDataVertex(allMotion->getState()));
439 else
440 data.addEdge(base::PlannerDataVertex(allMotion->getParent()->getState()),
441 base::PlannerDataVertex(allMotion->getState()));
442 }
443}
Definition of a cost value. Can represent the cost of a motion or the cost of a state.
Definition Cost.h:48
double value() const
The value of the cost.
Definition Cost.h:56
Abstract definition of a goal region that can be sampled.
Abstract definition of goals.
Definition Goal.h:63
virtual bool isSatisfied(const State *st) const =0
Return true if the state satisfies the goal constraints.
Objective for attempting to maximize the minimum clearance along a path.
The cost of a path is defined as the worst state cost over the entire path. This objective attempts t...
Base class for a vertex in the PlannerData structure. All derived classes must implement the clone an...
Definition PlannerData.h:59
Object containing planner generated vertex and edge data. It is assumed that all vertices are unique,...
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...
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...
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...
Encapsulate a termination condition for a motion planner. Planners will call operator() to decide whe...
void addPlannerProgressProperty(const std::string &progressPropertyName, const PlannerProgressProperty &prop)
Add a planner progress property called progressPropertyName with a property querying function prop to...
Definition Planner.h:403
PlannerSpecs specs_
The specifications of the planner (its capabilities)
Definition Planner.h:422
virtual void setup()
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition Planner.cpp:92
Definition of an abstract state.
Definition State.h:50
Representation of a motion.
Definition SST.h:171
unsigned numChildren_
Number of children.
Definition SST.h:200
base::State * state_
The state contained by the motion.
Definition SST.h:194
Motion * parent_
The parent motion in the exploration tree.
Definition SST.h:197
bool inactive_
If inactive, this node is not considered for selection.
Definition SST.h:203
Motion * rep_
The node in the tree that is within the pruning radius.
Definition SST.h:229
double getPruningRadius() const
Get the pruning radius the planner is using.
Definition SST.h:148
base::Cost prevSolutionCost_
The best solution cost we found so far.
Definition SST.h:279
Witness * findClosestWitness(Motion *node)
Find the closest witness node to a newly generated potential node.
Definition SST.cpp:183
double getSelectionRadius() const
Get the selection radius the planner is using.
Definition SST.h:127
void setPruningRadius(double pruningRadius)
Set the radius for pruning nodes.
Definition SST.h:142
void setGoalBias(double goalBias)
Definition SST.h:86
double getRange() const
Get the range the planner is using.
Definition SST.h:108
double getGoalBias() const
Get the goal bias the planner is using.
Definition SST.h:92
void setup() override
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition SST.cpp:65
base::PlannerStatus solve(const base::PlannerTerminationCondition &ptc) override
Continue solving for some amount of time. Return true if solution was found.
Definition SST.cpp:224
Motion * selectNode(Motion *sample)
Finds the best node in the tree withing the selection radius around a random sample.
Definition SST.cpp:154
void clear() override
Clear datastructures. Call this function if the input data to the planner has changed and you do not ...
Definition SST.cpp:108
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 SST.cpp:416
void setSelectionRadius(double selectionRadius)
Set the radius for selecting nodes relative to random sample.
Definition SST.h:121
void freeMemory()
Free the memory allocated by this planner.
Definition SST.cpp:121
SST(const base::SpaceInformationPtr &si)
Constructor.
Definition SST.cpp:45
std::vector< base::State * > prevSolution_
The best solution we found so far.
Definition SST.h:276
base::State * monteCarloProp(Motion *m)
Randomly propagate a new edge.
Definition SST.cpp:207
void setRange(double distance)
Set the range the planner is supposed to use.
Definition SST.h:102
#define OMPL_INFORM(fmt,...)
Log a formatted information string.
Definition Console.h:68
#define OMPL_ERROR(fmt,...)
Log a formatted error string.
Definition Console.h:64
#define OMPL_WARN(fmt,...)
Log a formatted warning string.
Definition Console.h:66
std::function< void(const Planner *, const std::vector< const base::State * > &, const Cost)> ReportIntermediateSolutionFn
When a planner has an intermediate solution (e.g., optimizing planners), a function with this signatu...
bool directed
Flag indicating whether the planner is able to account for the fact that the validity of a motion fro...
Definition Planner.h:205
bool approximateSolutions
Flag indicating whether the planner is able to compute approximate solutions.
Definition Planner.h:195
A class to store the exit status of Planner::solve()
@ INVALID_START
Invalid start state or no start state specified.