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RRT.cpp
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34
35/* Author: Ioan Sucan */
36
37#include "ompl/geometric/planners/rrt/RRT.h"
38#include <limits>
39#include "ompl/base/goals/GoalSampleableRegion.h"
40#include "ompl/tools/config/SelfConfig.h"
41
42ompl::geometric::RRT::RRT(const base::SpaceInformationPtr &si, bool addIntermediateStates)
43 : base::Planner(si, addIntermediateStates ? "RRTintermediate" : "RRT")
44{
46 specs_.directed = true;
47
48 Planner::declareParam<double>("range", this, &RRT::setRange, &RRT::getRange, "0.:1.:10000.");
49 Planner::declareParam<double>("goal_bias", this, &RRT::setGoalBias, &RRT::getGoalBias, "0.:.05:1.");
50 Planner::declareParam<bool>("intermediate_states", this, &RRT::setIntermediateStates, &RRT::getIntermediateStates,
51 "0,1");
52
53 addIntermediateStates_ = addIntermediateStates;
54}
55
56ompl::geometric::RRT::~RRT()
57{
58 freeMemory();
59}
60
62{
63 Planner::clear();
64 sampler_.reset();
65 freeMemory();
66 if (nn_)
67 nn_->clear();
68 lastGoalMotion_ = nullptr;
69}
70
72{
73 Planner::setup();
74 tools::SelfConfig sc(si_, getName());
75 sc.configurePlannerRange(maxDistance_);
76
77 if (!nn_)
78 nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
79 nn_->setDistanceFunction([this](const Motion *a, const Motion *b) { return distanceFunction(a, b); });
80}
81
83{
84 if (nn_)
85 {
86 std::vector<Motion *> motions;
87 nn_->list(motions);
88 for (auto &motion : motions)
89 {
90 if (motion->state != nullptr)
91 si_->freeState(motion->state);
92 delete motion;
93 }
94 }
95}
96
98{
99 checkValidity();
100 base::Goal *goal = pdef_->getGoal().get();
101 auto *goal_s = dynamic_cast<base::GoalSampleableRegion *>(goal);
102
103 while (const base::State *st = pis_.nextStart())
104 {
105 auto *motion = new Motion(si_);
106 si_->copyState(motion->state, st);
107 nn_->add(motion);
108 }
109
110 if (nn_->size() == 0)
111 {
112 OMPL_ERROR("%s: There are no valid initial states!", getName().c_str());
114 }
115
116 if (!sampler_)
117 sampler_ = si_->allocStateSampler();
118
119 OMPL_INFORM("%s: Starting planning with %u states already in datastructure", getName().c_str(), nn_->size());
120
121 Motion *solution = nullptr;
122 Motion *approxsol = nullptr;
123 double approxdif = std::numeric_limits<double>::infinity();
124 auto *rmotion = new Motion(si_);
125 base::State *rstate = rmotion->state;
126 base::State *xstate = si_->allocState();
127
128 while (!ptc)
129 {
130 /* sample random state (with goal biasing) */
131 if ((goal_s != nullptr) && rng_.uniform01() < goalBias_ && goal_s->canSample())
132 goal_s->sampleGoal(rstate);
133 else
134 sampler_->sampleUniform(rstate);
135
136 /* find closest state in the tree */
137 Motion *nmotion = nn_->nearest(rmotion);
138 base::State *dstate = rstate;
139
140 /* find state to add */
141 double d = si_->distance(nmotion->state, rstate);
142 if (d > maxDistance_)
143 {
144 si_->getStateSpace()->interpolate(nmotion->state, rstate, maxDistance_ / d, xstate);
145 dstate = xstate;
146 }
147
148 if (si_->checkMotion(nmotion->state, dstate))
149 {
150 if (addIntermediateStates_)
151 {
152 std::vector<base::State *> states;
153 const unsigned int count = si_->getStateSpace()->validSegmentCount(nmotion->state, dstate);
154
155 if (si_->getMotionStates(nmotion->state, dstate, states, count, true, true))
156 si_->freeState(states[0]);
157
158 for (std::size_t i = 1; i < states.size(); ++i)
159 {
160 auto *motion = new Motion;
161 motion->state = states[i];
162 motion->parent = nmotion;
163 nn_->add(motion);
164
165 nmotion = motion;
166 }
167 }
168 else
169 {
170 auto *motion = new Motion(si_);
171 si_->copyState(motion->state, dstate);
172 motion->parent = nmotion;
173 nn_->add(motion);
174
175 nmotion = motion;
176 }
177
178 double dist = 0.0;
179 bool sat = goal->isSatisfied(nmotion->state, &dist);
180 if (sat)
181 {
182 approxdif = dist;
183 solution = nmotion;
184 break;
185 }
186 if (dist < approxdif)
187 {
188 approxdif = dist;
189 approxsol = nmotion;
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 if (rmotion->state != nullptr)
224 si_->freeState(rmotion->state);
225 delete rmotion;
226
227 OMPL_INFORM("%s: Created %u states", getName().c_str(), nn_->size());
228
229 return {solved, approximate};
230}
231
233{
234 Planner::getPlannerData(data);
235
236 std::vector<Motion *> motions;
237 if (nn_)
238 nn_->list(motions);
239
240 if (lastGoalMotion_ != nullptr)
241 data.addGoalVertex(base::PlannerDataVertex(lastGoalMotion_->state));
242
243 for (auto &motion : motions)
244 {
245 if (motion->parent == nullptr)
246 data.addStartVertex(base::PlannerDataVertex(motion->state));
247 else
248 data.addEdge(base::PlannerDataVertex(motion->parent->state), base::PlannerDataVertex(motion->state));
249 }
250}
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.
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...
PlannerSpecs specs_
The specifications of the planner (its capabilities)
Definition Planner.h:429
Definition of an abstract state.
Definition State.h:50
Representation of a motion.
Definition RRT.h:148
Motion * parent
The parent motion in the exploration tree.
Definition RRT.h:163
base::State * state
The state contained by the motion.
Definition RRT.h:160
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 RRT.cpp:97
void setup() override
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition RRT.cpp:71
bool addIntermediateStates_
Flag indicating whether intermediate states are added to the built tree of motions.
Definition RRT.h:189
void setGoalBias(double goalBias)
Set the goal bias.
Definition RRT.h:88
void setIntermediateStates(bool addIntermediateStates)
Specify whether the intermediate states generated along motions are to be added to the tree itself.
Definition RRT.h:108
double getGoalBias() const
Get the goal bias the planner is using.
Definition RRT.h:94
bool getIntermediateStates() const
Return true if the intermediate states generated along motions are to be added to the tree itself.
Definition RRT.h:101
RRT(const base::SpaceInformationPtr &si, bool addIntermediateStates=false)
Constructor.
Definition RRT.cpp:42
void clear() override
Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() wil...
Definition RRT.cpp:61
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 RRT.cpp:232
void setRange(double distance)
Set the range the planner is supposed to use.
Definition RRT.h:118
void freeMemory()
Free the memory allocated by this planner.
Definition RRT.cpp:82
double getRange() const
Get the range the planner is using.
Definition RRT.h:124
This class contains methods that automatically configure various parameters for motion planning....
Definition SelfConfig.h:60
void configurePlannerRange(double &range)
Compute what a good length for motion segments is.
#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
bool directed
Flag indicating whether the planner is able to account for the fact that the validity of a motion fro...
Definition Planner.h:212
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()
@ INVALID_START
Invalid start state or no start state specified.