KPIECE1.cpp
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34
35/* Author: Ioan Sucan */
36
37#include "ompl/control/planners/kpiece/KPIECE1.h"
38#include "ompl/base/goals/GoalSampleableRegion.h"
39#include "ompl/tools/config/SelfConfig.h"
40#include "ompl/util/Exception.h"
41#include <limits>
42#include <cassert>
43
44ompl::control::KPIECE1::KPIECE1(const SpaceInformationPtr &si) : base::Planner(si, "KPIECE1")
45{
47
48 siC_ = si.get();
50
51 Planner::declareParam<double>("goal_bias", this, &KPIECE1::setGoalBias, &KPIECE1::getGoalBias, "0.:.05:1.");
52 Planner::declareParam<double>("border_fraction", this, &KPIECE1::setBorderFraction, &KPIECE1::getBorderFraction,
53 "0.:0.05:1.");
54 Planner::declareParam<unsigned int>("max_close_samples", this, &KPIECE1::setMaxCloseSamplesCount,
56 Planner::declareParam<double>("bad_score_factor", this, &KPIECE1::setBadCellScoreFactor,
58 Planner::declareParam<double>("good_score_factor", this, &KPIECE1::setGoodCellScoreFactor,
60}
61
62ompl::control::KPIECE1::~KPIECE1()
63{
64 freeMemory();
65}
66
68{
69 Planner::setup();
70 tools::SelfConfig sc(si_, getName());
71 sc.configureProjectionEvaluator(projectionEvaluator_);
72
73 if (badScoreFactor_ < std::numeric_limits<double>::epsilon() || badScoreFactor_ > 1.0)
74 throw Exception("Bad cell score factor must be in the range (0,1]");
75 if (goodScoreFactor_ < std::numeric_limits<double>::epsilon() || goodScoreFactor_ > 1.0)
76 throw Exception("Good cell score factor must be in the range (0,1]");
77 if (selectBorderFraction_ < std::numeric_limits<double>::epsilon() || selectBorderFraction_ > 1.0)
78 throw Exception("The fraction of time spent selecting border cells must be in the range (0,1]");
79
80 tree_.grid.setDimension(projectionEvaluator_->getDimension());
81}
82
84{
85 Planner::clear();
86 controlSampler_.reset();
87 freeMemory();
88 tree_.grid.clear();
89 tree_.size = 0;
90 tree_.iteration = 1;
91 lastGoalMotion_ = nullptr;
92}
93
95{
96 freeGridMotions(tree_.grid);
97}
98
100{
101 for (const auto &it : grid)
102 freeCellData(it.second->data);
103}
104
106{
107 for (auto &motion : cdata->motions)
108 freeMotion(motion);
109 delete cdata;
110}
111
113{
114 if (motion->state)
115 si_->freeState(motion->state);
116 if (motion->control)
117 siC_->freeControl(motion->control);
118 delete motion;
119}
120
122{
123 if (samples.empty())
124 {
125 CloseSample cs(cell, motion, distance);
126 samples.insert(cs);
127 return true;
128 }
129 // if the sample we're considering is closer to the goal than the worst sample in the
130 // set of close samples, we include it
131 if (samples.rbegin()->distance > distance)
132 {
133 // if the inclusion would go above the maximum allowed size,
134 // remove the last element
135 if (samples.size() >= maxSize)
136 samples.erase(--samples.end());
137 CloseSample cs(cell, motion, distance);
138 samples.insert(cs);
139 return true;
140 }
141
142 return false;
143}
144
146// this is the factor by which distances are inflated when considered for addition to closest samples
147static const double CLOSE_MOTION_DISTANCE_INFLATION_FACTOR = 1.1;
149
151{
152 if (samples.size() > 0)
153 {
154 scell = samples.begin()->cell;
155 smotion = samples.begin()->motion;
156 // average the highest & lowest distances and multiply by CLOSE_MOTION_DISTANCE_INFLATION_FACTOR
157 // (make the distance appear artificially longer)
158 double d =
159 (samples.begin()->distance + samples.rbegin()->distance) * (CLOSE_MOTION_DISTANCE_INFLATION_FACTOR / 2.0);
160 samples.erase(samples.begin());
161 consider(scell, smotion, d);
162 return true;
163 }
164 return false;
165}
166
167unsigned int ompl::control::KPIECE1::findNextMotion(const std::vector<Grid::Coord> &coords, unsigned int index,
168 unsigned int count)
169{
170 for (unsigned int i = index + 1; i < count; ++i)
171 if (coords[i] != coords[index])
172 return i - 1;
173
174 return count - 1;
175}
176
178{
179 checkValidity();
180 base::Goal *goal = pdef_->getGoal().get();
181
182 while (const base::State *st = pis_.nextStart())
183 {
184 auto *motion = new Motion(siC_);
185 si_->copyState(motion->state, st);
186 siC_->nullControl(motion->control);
187 addMotion(motion, 1.0);
188 }
189
190 if (tree_.grid.size() == 0)
191 {
192 OMPL_ERROR("%s: There are no valid initial states!", getName().c_str());
194 }
195
196 if (!controlSampler_)
197 controlSampler_ = siC_->allocControlSampler();
198
199 OMPL_INFORM("%s: Starting planning with %u states already in datastructure", getName().c_str(), tree_.size);
200
201 Motion *solution = nullptr;
202 Motion *approxsol = nullptr;
203 double approxdif = std::numeric_limits<double>::infinity();
204
205 Control *rctrl = siC_->allocControl();
206
207 std::vector<base::State *> states(siC_->getMaxControlDuration() + 1);
208 std::vector<Grid::Coord> coords(states.size(), Grid::Coord(projectionEvaluator_->getDimension()));
209 std::vector<Grid::Cell *> cells(coords.size());
210
211 for (auto &state : states)
212 state = si_->allocState();
213
214 // samples that were found to be the best, so far
215 CloseSamples closeSamples(nCloseSamples_);
216
217 while (ptc == false)
218 {
219 tree_.iteration++;
220
221 /* Decide on a state to expand from */
222 Motion *existing = nullptr;
223 Grid::Cell *ecell = nullptr;
224
225 if (closeSamples.canSample() && rng_.uniform01() < goalBias_)
226 {
227 if (!closeSamples.selectMotion(existing, ecell))
228 selectMotion(existing, ecell);
229 }
230 else
231 selectMotion(existing, ecell);
232 assert(existing);
233
234 /* sample a random control */
235 controlSampler_->sampleNext(rctrl, existing->control, existing->state);
236
237 /* propagate */
238 unsigned int cd =
239 controlSampler_->sampleStepCount(siC_->getMinControlDuration(), siC_->getMaxControlDuration());
240 cd = siC_->propagateWhileValid(existing->state, rctrl, cd, states, false);
241
242 /* if we have enough steps */
243 if (cd >= siC_->getMinControlDuration())
244 {
245 std::size_t avgCov_two_thirds = (2 * tree_.size) / (3 * tree_.grid.size());
246 bool interestingMotion = false;
247
248 // split the motion into smaller ones, so we do not cross cell boundaries
249 for (unsigned int i = 0; i < cd; ++i)
250 {
251 projectionEvaluator_->computeCoordinates(states[i], coords[i]);
252 cells[i] = tree_.grid.getCell(coords[i]);
253 if (!cells[i])
254 interestingMotion = true;
255 else
256 {
257 if (!interestingMotion && cells[i]->data->motions.size() <= avgCov_two_thirds)
258 interestingMotion = true;
259 }
260 }
261
262 if (interestingMotion || rng_.uniform01() < 0.05)
263 {
264 unsigned int index = 0;
265 while (index < cd)
266 {
267 unsigned int nextIndex = findNextMotion(coords, index, cd);
268 auto *motion = new Motion(siC_);
269 si_->copyState(motion->state, states[nextIndex]);
270 siC_->copyControl(motion->control, rctrl);
271 motion->steps = nextIndex - index + 1;
272 motion->parent = existing;
273
274 double dist = 0.0;
275 bool solv = goal->isSatisfied(motion->state, &dist);
276 Grid::Cell *toCell = addMotion(motion, dist);
277
278 if (solv)
279 {
280 approxdif = dist;
281 solution = motion;
282 break;
283 }
284 if (dist < approxdif)
285 {
286 approxdif = dist;
287 approxsol = motion;
288 }
289
290 closeSamples.consider(toCell, motion, dist);
291
292 // new parent will be the newly created motion
293 existing = motion;
294 index = nextIndex + 1;
295 }
296
297 if (solution)
298 break;
299 }
300
301 // update cell score
302 ecell->data->score *= goodScoreFactor_;
303 }
304 else
305 ecell->data->score *= badScoreFactor_;
306
307 tree_.grid.update(ecell);
308 }
309
310 bool solved = false;
311 bool approximate = false;
312 if (solution == nullptr)
313 {
314 solution = approxsol;
315 approximate = true;
316 }
317
318 if (solution != nullptr)
319 {
320 lastGoalMotion_ = solution;
321
322 /* construct the solution path */
323 std::vector<Motion *> mpath;
324 while (solution != nullptr)
325 {
326 mpath.push_back(solution);
327 solution = solution->parent;
328 }
329
330 /* set the solution path */
331 auto path(std::make_shared<PathControl>(si_));
332 for (int i = mpath.size() - 1; i >= 0; --i)
333 if (mpath[i]->parent)
334 path->append(mpath[i]->state, mpath[i]->control, mpath[i]->steps * siC_->getPropagationStepSize());
335 else
336 path->append(mpath[i]->state);
337
338 pdef_->addSolutionPath(path, approximate, approxdif, getName());
339 solved = true;
340 }
341
342 siC_->freeControl(rctrl);
343 for (auto &state : states)
344 si_->freeState(state);
345
346 OMPL_INFORM("%s: Created %u states in %u cells (%u internal + %u external)", getName().c_str(), tree_.size,
347 tree_.grid.size(), tree_.grid.countInternal(), tree_.grid.countExternal());
348
349 return {solved, approximate};
350}
351
353{
354 scell = rng_.uniform01() < std::max(selectBorderFraction_, tree_.grid.fracExternal()) ? tree_.grid.topExternal() :
355 tree_.grid.topInternal();
356
357 // We are running on finite precision, so our update scheme will end up
358 // with 0 values for the score. This is where we fix the problem
359 if (scell->data->score < std::numeric_limits<double>::epsilon())
360 {
361 OMPL_DEBUG("%s: Numerical precision limit reached. Resetting costs.", getName().c_str());
362 std::vector<CellData *> content;
363 content.reserve(tree_.grid.size());
364 tree_.grid.getContent(content);
365 for (auto &it : content)
366 it->score += 1.0 + log((double)(it->iteration));
367 tree_.grid.updateAll();
368 }
369
370 if (scell && !scell->data->motions.empty())
371 {
372 scell->data->selections++;
373 smotion = scell->data->motions[rng_.halfNormalInt(0, scell->data->motions.size() - 1)];
374 return true;
375 }
376 else
377 return false;
378}
379
381// this is the offset added to estimated distances to the goal, so we avoid division by 0
382static const double DISTANCE_TO_GOAL_OFFSET = 1e-3;
384
386{
387 Grid::Coord coord(projectionEvaluator_->getDimension());
388 projectionEvaluator_->computeCoordinates(motion->state, coord);
389 Grid::Cell *cell = tree_.grid.getCell(coord);
390 if (cell)
391 {
392 cell->data->motions.push_back(motion);
393 cell->data->coverage += motion->steps;
394 tree_.grid.update(cell);
395 }
396 else
397 {
398 cell = tree_.grid.createCell(coord);
399 cell->data = new CellData();
400 cell->data->motions.push_back(motion);
401 cell->data->coverage = motion->steps;
402 cell->data->iteration = tree_.iteration;
403 cell->data->selections = 1;
404 cell->data->score = (1.0 + log((double)(tree_.iteration))) / (DISTANCE_TO_GOAL_OFFSET + dist);
405 tree_.grid.add(cell);
406 }
407 tree_.size++;
408 return cell;
409}
410
412{
413 Planner::getPlannerData(data);
414
415 Grid::CellArray cells;
416 tree_.grid.getCells(cells);
417
418 double delta = siC_->getPropagationStepSize();
419
420 if (lastGoalMotion_)
421 data.addGoalVertex(base::PlannerDataVertex(lastGoalMotion_->state));
422
423 for (auto &cell : cells)
424 {
425 for (const auto &m : cell->data->motions)
426 {
427 if (m->parent)
428 {
429 if (data.hasControls())
430 data.addEdge(base::PlannerDataVertex(m->parent->state),
431 base::PlannerDataVertex(m->state, cell->border ? 2 : 1),
432 control::PlannerDataEdgeControl(m->control, m->steps * delta));
433 else
434 data.addEdge(base::PlannerDataVertex(m->parent->state),
435 base::PlannerDataVertex(m->state, cell->border ? 2 : 1));
436 }
437 else
438 data.addStartVertex(base::PlannerDataVertex(m->state, cell->border ? 2 : 1));
439
440 // A state created as a parent first may have an improper tag variable
441 data.tagState(m->state, cell->border ? 2 : 1);
442 }
443 }
444}
The exception type for ompl.
Definition: Exception.h:47
typename GridN< CellData * >::CellArray CellArray
The datatype for arrays of cells.
Definition: GridB.h:58
void onCellUpdate(EventCellUpdate event, void *arg)
Definition: GridB.h:99
typename GridN< CellData * >::Cell Cell
Definition of a cell in this grid.
Definition: GridB.h:55
Eigen::VectorXi Coord
Definition of a coordinate within this grid.
Definition: Grid.h:55
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.
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,...
Definition: PlannerData.h:175
bool tagState(const State *st, int tag)
Set the integer tag associated with the given state. If the given state does not exist in a vertex,...
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...
virtual bool hasControls() const
Indicate whether any information about controls (ompl::control::Control) is stored in this instance.
Encapsulate a termination condition for a motion planner. Planners will call operator() to decide whe...
PlannerSpecs specs_
The specifications of the planner (its capabilities)
Definition: Planner.h:429
A shared pointer wrapper for ompl::base::SpaceInformation.
Definition of an abstract state.
Definition: State.h:50
Definition of an abstract control.
Definition: Control.h:48
Grid::Cell * addMotion(Motion *motion, double dist)
Add a motion to the grid containing motions. As a hint, dist specifies the distance to the goal from ...
Definition: KPIECE1.cpp:385
void setGoodCellScoreFactor(double good)
Set the factor that is to be applied to a cell's score when an expansion from that cell succeedes.
Definition: KPIECE1.h:143
unsigned int getMaxCloseSamplesCount() const
Get the maximum number of samples to store in the queue of samples that are close to the goal.
Definition: KPIECE1.h:170
TreeData tree_
The tree datastructure.
Definition: KPIECE1.h:391
double getGoalBias() const
Definition: KPIECE1.h:100
void setMaxCloseSamplesCount(unsigned int nCloseSamples)
When motions reach close to the goal, they are stored in a separate queue to allow biasing towards th...
Definition: KPIECE1.h:164
double getBadCellScoreFactor() const
Get the factor that is multiplied to a cell's score if extending a motion from that cell failed.
Definition: KPIECE1.h:157
static void computeImportance(Grid::Cell *cell, void *)
This function is provided as a calback to the grid datastructure to update the importance of a cell.
Definition: KPIECE1.h:349
const SpaceInformation * siC_
The base::SpaceInformation cast as control::SpaceInformation, for convenience.
Definition: KPIECE1.h:394
void setup() override
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition: KPIECE1.cpp:67
void setBorderFraction(double bp)
Set the fraction of time for focusing on the border (between 0 and 1). This is the minimum fraction u...
Definition: KPIECE1.h:111
double getBorderFraction() const
Get the fraction of time to focus exploration on boundary.
Definition: KPIECE1.h:118
void freeMemory()
Free all the memory allocated by this planner.
Definition: KPIECE1.cpp:94
double getGoodCellScoreFactor() const
Get the factor that is multiplied to a cell's score if extending a motion from that cell succeeded.
Definition: KPIECE1.h:150
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: KPIECE1.cpp:177
void setGoalBias(double goalBias)
Definition: KPIECE1.h:94
KPIECE1(const SpaceInformationPtr &si)
Constructor.
Definition: KPIECE1.cpp:44
void clear() override
Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() wil...
Definition: KPIECE1.cpp:83
void freeMotion(Motion *motion)
Free the memory for a motion.
Definition: KPIECE1.cpp:112
bool selectMotion(Motion *&smotion, Grid::Cell *&scell)
Select a motion and the cell it is part of from the grid of motions. This is where preference is give...
Definition: KPIECE1.cpp:352
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: KPIECE1.cpp:411
void setBadCellScoreFactor(double bad)
Set the factor that is to be applied to a cell's score when an expansion from that cell fails.
Definition: KPIECE1.h:136
unsigned int findNextMotion(const std::vector< Grid::Coord > &coords, unsigned int index, unsigned int count)
When generated motions are to be added to the tree of motions, they often need to be split,...
Definition: KPIECE1.cpp:167
void freeCellData(CellData *cdata)
Free the memory for the data contained in a grid cell.
Definition: KPIECE1.cpp:105
void freeGridMotions(Grid &grid)
Free the memory for the motions contained in a grid.
Definition: KPIECE1.cpp:99
Representation of an edge in PlannerData for planning with controls. This structure encodes a specifi...
Definition: PlannerData.h:61
This class contains methods that automatically configure various parameters for motion planning....
Definition: SelfConfig.h:60
void configureProjectionEvaluator(base::ProjectionEvaluatorPtr &proj)
If proj is undefined, it is set to the default projection reported by base::StateSpace::getDefaultPro...
Definition: SelfConfig.cpp:231
#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_DEBUG(fmt,...)
Log a formatted debugging string.
Definition: Console.h:70
void log(const char *file, int line, LogLevel level, const char *m,...)
Root level logging function. This should not be invoked directly, but rather used via a logging macro...
Definition: Console.cpp:120
Definition of a cell in this grid.
Definition: Grid.h:59
_T data
The data we store in the cell.
Definition: Grid.h:61
bool approximateSolutions
Flag indicating whether the planner is able to compute approximate solutions.
Definition: Planner.h:202
A class to store the exit status of Planner::solve()
Definition: PlannerStatus.h:49
@ INVALID_START
Invalid start state or no start state specified.
Definition: PlannerStatus.h:56
The data held by a cell in the grid of motions.
Definition: KPIECE1.h:227
std::vector< Motion * > motions
The set of motions contained in this grid cell.
Definition: KPIECE1.h:233
Information about a known good sample (closer to the goal than others)
Definition: KPIECE1.h:271
Bounded set of good samples.
Definition: KPIECE1.h:296
bool canSample() const
Return true if samples can be selected from this set.
Definition: KPIECE1.h:317
bool consider(Grid::Cell *cell, Motion *motion, double distance)
Evaluate whether motion motion, part of cell cell is good enough to be part of the set of samples clo...
Definition: KPIECE1.cpp:121
bool selectMotion(Motion *&smotion, Grid::Cell *&scell)
Select the top sample (closest to the goal) and update its position in the set subsequently (pretend ...
Definition: KPIECE1.cpp:150
Representation of a motion for this algorithm.
Definition: KPIECE1.h:201
base::State * state
The state contained by this motion.
Definition: KPIECE1.h:213
Motion * parent
The parent motion in the exploration tree.
Definition: KPIECE1.h:222
Control * control
The control contained by this motion.
Definition: KPIECE1.h:216
unsigned int steps
The number of steps the control is applied for.
Definition: KPIECE1.h:219
Grid grid
A grid containing motions, imposed on a projection of the state space.
Definition: KPIECE1.h:336