EST.cpp
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34 
35 /* Author: Ryan Luna */
36 
37 #include "ompl/geometric/planners/est/EST.h"
38 #include "ompl/base/goals/GoalSampleableRegion.h"
39 #include "ompl/tools/config/SelfConfig.h"
40 #include <limits>
41 #include <cassert>
42 
43 ompl::geometric::EST::EST(const base::SpaceInformationPtr &si) : base::Planner(si, "EST")
44 {
46  specs_.directed = true;
47 
48  Planner::declareParam<double>("range", this, &EST::setRange, &EST::getRange, "0.:1.:10000.");
49  Planner::declareParam<double>("goal_bias", this, &EST::setGoalBias, &EST::getGoalBias, "0.:.05:1.");
50 }
51 
52 ompl::geometric::EST::~EST()
53 {
54  freeMemory();
55 }
56 
58 {
59  Planner::setup();
60  tools::SelfConfig sc(si_, getName());
61  sc.configurePlannerRange(maxDistance_);
62 
63  // Make the neighborhood radius smaller than sampling range to keep probabilities relatively high for rejection
64  // sampling
65  nbrhoodRadius_ = maxDistance_ / 3.0;
66 
67  if (!nn_)
68  nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
69  nn_->setDistanceFunction([this](const Motion *a, const Motion *b)
70  {
71  return distanceFunction(a, b);
72  });
73 }
74 
76 {
77  Planner::clear();
78  sampler_.reset();
79  freeMemory();
80  if (nn_)
81  nn_->clear();
82 
83  motions_.clear();
84  pdf_.clear();
85  lastGoalMotion_ = nullptr;
86 }
87 
89 {
90  for (auto &motion : motions_)
91  {
92  if (motion->state != nullptr)
93  si_->freeState(motion->state);
94  delete motion;
95  }
96 }
97 
99 {
100  checkValidity();
101  base::Goal *goal = pdef_->getGoal().get();
102  auto *goal_s = dynamic_cast<base::GoalSampleableRegion *>(goal);
103 
104  std::vector<Motion *> neighbors;
105 
106  while (const base::State *st = pis_.nextStart())
107  {
108  auto *motion = new Motion(si_);
109  si_->copyState(motion->state, st);
110 
111  nn_->nearestR(motion, nbrhoodRadius_, neighbors);
112  addMotion(motion, neighbors);
113  }
114 
115  if (motions_.empty())
116  {
117  OMPL_ERROR("%s: There are no valid initial states!", getName().c_str());
119  }
120 
121  if (!sampler_)
122  sampler_ = si_->allocValidStateSampler();
123 
124  OMPL_INFORM("%s: Starting planning with %u states already in datastructure", getName().c_str(), motions_.size());
125 
126  Motion *solution = nullptr;
127  Motion *approxsol = nullptr;
128  double approxdif = std::numeric_limits<double>::infinity();
129  base::State *xstate = si_->allocState();
130  auto *xmotion = new Motion();
131 
132  while (!ptc)
133  {
134  // Select a state to expand from
135  Motion *existing = pdf_.sample(rng_.uniform01());
136  assert(existing);
137 
138  // Sample random state in the neighborhood (with goal biasing)
139  if ((goal_s != nullptr) && rng_.uniform01() < goalBias_ && goal_s->canSample())
140  {
141  goal_s->sampleGoal(xstate);
142 
143  // Compute neighborhood of candidate motion
144  xmotion->state = xstate;
145  nn_->nearestR(xmotion, nbrhoodRadius_, neighbors);
146  }
147  else
148  {
149  // Sample a state in the neighborhood
150  if (!sampler_->sampleNear(xstate, existing->state, maxDistance_))
151  continue;
152 
153  // Compute neighborhood of candidate state
154  xmotion->state = xstate;
155  nn_->nearestR(xmotion, nbrhoodRadius_, neighbors);
156 
157  // reject state with probability proportional to neighborhood density
158  if (!neighbors.empty() )
159  {
160  double p = 1.0 - (1.0 / neighbors.size());
161  if (rng_.uniform01() < p)
162  continue;
163  }
164  }
165 
166  // Is motion good?
167  if (si_->checkMotion(existing->state, xstate))
168  {
169  // create a motion
170  auto *motion = new Motion(si_);
171  si_->copyState(motion->state, xstate);
172  motion->parent = existing;
173 
174  // add it to everything
175  addMotion(motion, neighbors);
176 
177  // done?
178  double dist = 0.0;
179  bool solved = goal->isSatisfied(motion->state, &dist);
180  if (solved)
181  {
182  approxdif = dist;
183  solution = motion;
184  break;
185  }
186  if (dist < approxdif)
187  {
188  approxdif = dist;
189  approxsol = motion;
190  }
191  }
192  }
193 
194  bool solved = false;
195  bool approximate = false;
196  if (solution == nullptr)
197  {
198  solution = approxsol;
199  approximate = true;
200  }
201 
202  if (solution != nullptr)
203  {
204  lastGoalMotion_ = solution;
205 
206  // construct the solution path
207  std::vector<Motion *> mpath;
208  while (solution != nullptr)
209  {
210  mpath.push_back(solution);
211  solution = solution->parent;
212  }
213 
214  // set the solution path
215  auto path(std::make_shared<PathGeometric>(si_));
216  for (int i = mpath.size() - 1; i >= 0; --i)
217  path->append(mpath[i]->state);
218  pdef_->addSolutionPath(path, approximate, approxdif, getName());
219  solved = true;
220  }
221 
222  si_->freeState(xstate);
223  delete xmotion;
224 
225  OMPL_INFORM("%s: Created %u states", getName().c_str(), motions_.size());
226 
227  return {solved, approximate};
228 }
229 
230 void ompl::geometric::EST::addMotion(Motion *motion, const std::vector<Motion *> &neighbors)
231 {
232  // Updating neighborhood size counts
233  for (auto neighbor : neighbors)
234  {
235  PDF<Motion *>::Element *elem = neighbor->element;
236  double w = pdf_.getWeight(elem);
237  pdf_.update(elem, w / (w + 1.));
238  }
239 
240  // now add new motion to the data structures
241  motion->element = pdf_.add(motion, 1. / (neighbors.size() + 1.)); // +1 for self
242  motions_.push_back(motion);
243  nn_->add(motion);
244 }
245 
247 {
248  Planner::getPlannerData(data);
249 
250  if (lastGoalMotion_ != nullptr)
251  data.addGoalVertex(base::PlannerDataVertex(lastGoalMotion_->state));
252 
253  for (auto motion : motions_)
254  {
255  if (motion->parent == nullptr)
256  data.addStartVertex(base::PlannerDataVertex(motion->state));
257  else
258  data.addEdge(base::PlannerDataVertex(motion->parent->state), base::PlannerDataVertex(motion->state));
259  }
260 }
void configurePlannerRange(double &range)
Compute what a good length for motion segments is.
Definition: SelfConfig.cpp:225
double getRange() const
Get the range the planner is using.
Definition: EST.h:205
Definition of an abstract state.
Definition: State.h:114
This class contains methods that automatically configure various parameters for motion planning....
Definition: SelfConfig.h:124
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: EST.cpp:246
void setGoalBias(double goalBias)
In the process of randomly selecting states in the state space to attempt to go towards,...
Definition: EST.h:180
PDF< Motion * >::Element * element
A pointer to the corresponding element in the probability distribution function.
Definition: EST.h:235
#define OMPL_INFORM(fmt,...)
Log a formatted information string.
Definition: Console.h:68
double getGoalBias() const
Get the goal bias the planner is using.
Definition: EST.h:186
EST(const base::SpaceInformationPtr &si)
Constructor.
Definition: EST.cpp:43
Object containing planner generated vertex and edge data. It is assumed that all vertices are unique,...
Definition: PlannerData.h:239
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:493
void setRange(double distance)
Set the range the planner is supposed to use.
Definition: EST.h:196
bool directed
Flag indicating whether the planner is able to account for the fact that the validity of a motion fro...
Definition: Planner.h:276
base::State * state
The state contained by the motion.
Definition: EST.h:229
A class to store the exit status of Planner::solve()
void addMotion(Motion *motion, const std::vector< Motion * > &neighbors)
Add a motion to the exploration tree.
Definition: EST.cpp:230
Abstract definition of goals.
Definition: Goal.h:127
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...
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: EST.cpp:98
#define OMPL_ERROR(fmt,...)
Log a formatted error string.
Definition: Console.h:64
bool approximateSolutions
Flag indicating whether the planner is able to compute approximate solutions.
Definition: Planner.h:266
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...
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...
Abstract definition of a goal region that can be sampled.
The definition of a motion.
Definition: EST.h:217
void clear() override
Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() wil...
Definition: EST.cpp:75
A class that will hold data contained in the PDF.
Definition: PDF.h:117
@ INVALID_START
Invalid start state or no start state specified.
Base class for a vertex in the PlannerData structure. All derived classes must implement the clone an...
Definition: PlannerData.h:123
Motion * parent
The parent motion in the exploration tree.
Definition: EST.h:232
void setup() override
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition: EST.cpp:57
void freeMemory()
Free the memory allocated by this planner.
Definition: EST.cpp:88