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();
62 
63  // Make the neighborhood radius smaller than sampling range to keep probabilities relatively high for rejection
64  // sampling
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 base::PlannerStatus(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)
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 }
bool approximateSolutions
Flag indicating whether the planner is able to compute approximate solutions.
Definition: Planner.h:203
RNG rng_
The random number generator.
Definition: EST.h:174
Object containing planner generated vertex and edge data. It is assumed that all vertices are unique...
Definition: PlannerData.h:174
PDF< Motion * >::Element * element
A pointer to the corresponding element in the probability distribution function.
Definition: EST.h:136
Motion * lastGoalMotion_
The most recent goal motion. Used for PlannerData computation.
Definition: EST.h:177
double getGoalBias() const
Get the goal bias the planner is using.
Definition: EST.h:90
double maxDistance_
The maximum length of a motion to be added to a tree.
Definition: EST.h:168
void setup() override
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition: EST.cpp:57
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 goals.
Definition: Goal.h:62
base::ValidStateSamplerPtr sampler_
Valid state sampler.
Definition: EST.h:161
std::vector< Motion * > motions_
The set of all states in the tree.
Definition: EST.h:149
Encapsulate a termination condition for a motion planner. Planners will call operator() to decide whe...
double nbrhoodRadius_
The radius considered for neighborhood.
Definition: EST.h:171
PDF< Motion * > pdf_
The probability distribution function over states in the tree.
Definition: EST.h:152
ProblemDefinitionPtr pdef_
The user set problem definition.
Definition: Planner.h:409
base::State * state
The state contained by the motion.
Definition: EST.h:130
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
The definition of a motion.
Definition: EST.h:117
double uniform01()
Generate a random real between 0 and 1.
Definition: RandomNumbers.h:68
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.
void clear() override
Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() wil...
Definition: EST.cpp:75
double distanceFunction(const Motion *a, const Motion *b) const
Compute distance between motions (actually distance between contained states)
Definition: EST.h:140
#define OMPL_ERROR(fmt,...)
Log a formatted error string.
Definition: Console.h:64
double getRange() const
Get the range the planner is using.
Definition: EST.h:106
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.
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
virtual void checkValidity()
Check to see if the planner is in a working state (setup has been called, a goal was set...
Definition: Planner.cpp:101
virtual bool isSatisfied(const State *st) const =0
Return true if the state satisfies the goal constraints.
void setRange(double distance)
Set the range the planner is supposed to use.
Definition: EST.h:100
PlannerInputStates pis_
Utility class to extract valid input states.
Definition: Planner.h:412
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
A class that will hold data contained in the PDF.
Definition: PDF.h:52
void addMotion(Motion *motion, const std::vector< Motion *> &neighbors)
Add a motion to the exploration tree.
Definition: EST.cpp:230
PlannerSpecs specs_
The specifications of the planner (its capabilities)
Definition: Planner.h:418
const State * nextStart()
Return the next valid start state or nullptr if no more valid start states are available.
Definition: Planner.cpp:227
void freeMemory()
Free the memory allocated by this planner.
Definition: EST.cpp:88
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
const std::string & getName() const
Get the name of the planner.
Definition: Planner.cpp:56
void setGoalBias(double goalBias)
In the process of randomly selecting states in the state space to attempt to go towards, the algorithm may in fact choose the actual goal state, if it knows it, with some probability. This probability is a real number between 0.0 and 1.0; its value should usually be around 0.05 and should not be too large. It is probably a good idea to use the default value.
Definition: EST.h:84
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
EST(const base::SpaceInformationPtr &si)
Constructor.
Definition: EST.cpp:43
SpaceInformationPtr si_
The space information for which planning is done.
Definition: Planner.h:406
std::shared_ptr< NearestNeighbors< Motion * > > nn_
A nearest-neighbors datastructure containing the tree of motions.
Definition: EST.h:146
double goalBias_
The fraction of time the goal is picked as the state to expand towards (if such a state is available)...
Definition: EST.h:165
#define OMPL_INFORM(fmt,...)
Log a formatted information string.
Definition: Console.h:68