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TSRRT.cpp
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34
35/* Author: Ryan Luna */
36
37#include "ompl/geometric/planners/rrt/TSRRT.h"
38#include "ompl/base/goals/GoalSampleableRegion.h"
39#include "ompl/tools/config/SelfConfig.h"
40#include <limits>
41
42ompl::geometric::TSRRT::TSRRT(const base::SpaceInformationPtr &si, const TaskSpaceConfigPtr &task_space)
43 : base::Planner(si, "TSRRT"), task_space_(task_space)
44{
46 specs_.directed = true;
47
48 Planner::declareParam<double>("range", this, &TSRRT::setRange, &TSRRT::getRange, "0.:1.:10000.");
49 Planner::declareParam<double>("goal_bias", this, &TSRRT::setGoalBias, &TSRRT::getGoalBias, "0.:.05:1.");
50}
51
52ompl::geometric::TSRRT::~TSRRT()
53{
54 freeMemory();
55}
56
58{
59 Planner::clear();
60 freeMemory();
61 if (nn_)
62 nn_->clear();
63 lastGoalMotion_ = nullptr;
64}
65
67{
68 Planner::setup();
69 tools::SelfConfig sc(si_, getName());
70 sc.configurePlannerRange(maxDistance_);
71
72 if (!nn_)
73 nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors<Motion *>(this));
74 nn_->setDistanceFunction([this](const Motion *a, const Motion *b) { return distanceFunction(a, b); });
75}
76
78{
79 if (nn_)
80 {
81 std::vector<Motion *> motions;
82 nn_->list(motions);
83 for (auto &motion : motions)
84 {
85 if (motion->state)
86 si_->freeState(motion->state);
87 delete motion;
88 }
89 }
90}
91
93{
94 checkValidity();
95 base::Goal *goal = pdef_->getGoal().get();
96 auto *goal_s = dynamic_cast<base::GoalSampleableRegion *>(goal);
97
98 if (!task_space_)
99 throw ompl::Exception("Task Space info is not set. Cannot solve");
100
101 while (const base::State *st = pis_.nextStart())
102 {
103 auto *motion = new Motion(si_);
104 si_->copyState(motion->state, st);
105 motion->proj.resize(task_space_->getDimension());
106 task_space_->project(motion->state, motion->proj);
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 OMPL_INFORM("%s: Starting with %u states already in datastructure. %d dimensional projection", getName().c_str(),
117 nn_->size(), task_space_->getDimension());
118
119 Motion *solution = nullptr;
120 Motion *approxsol = nullptr;
121 double approxdif = std::numeric_limits<double>::infinity();
122 auto *rmotion = new Motion(si_);
123 base::State *rstate = rmotion->state;
124 base::State *xstate = si_->allocState();
125
126 auto &r_proj = rmotion->proj;
127 r_proj.resize(task_space_->getDimension());
128
129 while (ptc == false)
130 {
131 // Nearest state in the tree to the configuration we're about to sample.
132 Motion *nmotion = nullptr;
133
134 // Sample state (with goal biasing)
135 if (goal_s != nullptr && rng_.uniform01() < goalBias_ && goal_s->canSample())
136 {
137 // Sample the goal in configuration space, then project to the task-space.
138 goal_s->sampleGoal(rstate);
139 task_space_->project(rstate, r_proj);
140 nmotion = nn_->nearest(rmotion);
141 }
142 else
143 {
144 // Sample the state in task-space, then lift this to the configuration space.
145 task_space_->sample(r_proj);
146 nmotion = nn_->nearest(rmotion);
147 if (!task_space_->lift(r_proj, nmotion->state, rstate))
148 continue;
149 }
150 base::State *dstate = rstate;
151
152 // Truncate the maximum extension if necessary.
153 double d = si_->distance(nmotion->state, rstate);
154 if (d > maxDistance_)
155 {
156 si_->getStateSpace()->interpolate(nmotion->state, rstate, maxDistance_ / d, xstate);
157 dstate = xstate;
158 }
159
160 // Check for validity along the motion.
161 if (si_->checkMotion(nmotion->state, dstate))
162 {
163 /* create a motion */
164 Motion *motion = new Motion(si_);
165 si_->copyState(motion->state, dstate);
166 motion->parent = nmotion;
167 motion->proj.resize(task_space_->getDimension());
168 // Project state into task-space. Store the projection.
169 task_space_->project(motion->state, motion->proj);
170 nn_->add(motion);
171 double dist = 0.0;
172 bool sat = goal->isSatisfied(motion->state, &dist);
173 if (sat)
174 {
175 approxdif = dist;
176 solution = motion;
177 break;
178 }
179 if (dist < approxdif)
180 {
181 approxdif = dist;
182 approxsol = motion;
183 }
184 }
185 }
186
187 bool solved = false;
188 bool approximate = false;
189 if (solution == nullptr)
190 {
191 solution = approxsol;
192 approximate = true;
193 }
194
195 if (solution != nullptr)
196 {
197 lastGoalMotion_ = solution;
198
199 /* construct the solution path */
200 std::vector<Motion *> mpath;
201 while (solution != nullptr)
202 {
203 mpath.push_back(solution);
204 solution = solution->parent;
205 }
206
207 /* set the solution path */
208 auto path(std::make_shared<PathGeometric>(si_));
209 for (int i = mpath.size() - 1; i >= 0; --i)
210 path->append(mpath[i]->state);
211 pdef_->addSolutionPath(path, approximate, approxdif, getName());
212 solved = true;
213 }
214
215 si_->freeState(xstate);
216 if (rmotion->state)
217 si_->freeState(rmotion->state);
218 delete rmotion;
219
220 OMPL_INFORM("%s: Created %u states", getName().c_str(), nn_->size());
221
222 return {solved, approximate};
223}
224
226{
227 Planner::getPlannerData(data);
228
229 std::vector<Motion *> motions;
230 if (nn_)
231 nn_->list(motions);
232
233 if (lastGoalMotion_)
234 data.addGoalVertex(base::PlannerDataVertex(lastGoalMotion_->state));
235
236 for (auto &motion : motions)
237 {
238 if (motion->parent == nullptr)
239 data.addStartVertex(base::PlannerDataVertex(motion->state));
240 else
241 data.addEdge(base::PlannerDataVertex(motion->parent->state), base::PlannerDataVertex(motion->state));
242 }
243}
The exception type for ompl.
Definition Exception.h:47
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 TSRRT.h:152
Motion * parent
The parent motion in the exploration tree.
Definition TSRRT.h:165
base::State * state
The state contained by the motion.
Definition TSRRT.h:162
void freeMemory()
Free the memory allocated by this planner.
Definition TSRRT.cpp:77
void setRange(double distance)
Set the range the planner is supposed to use.
Definition TSRRT.h:126
double getGoalBias() const
Get the goal bias the planner is using.
Definition TSRRT.h:116
double getRange() const
Get the range the planner is using.
Definition TSRRT.h:132
TSRRT(const base::SpaceInformationPtr &si, const TaskSpaceConfigPtr &task_space)
Constructor.
Definition TSRRT.cpp:42
virtual void getPlannerData(base::PlannerData &data) const
Get information about the current run of the motion planner. Repeated calls to this function will upd...
Definition TSRRT.cpp:225
virtual base::PlannerStatus solve(const base::PlannerTerminationCondition &ptc)
Function that can solve the motion planning problem. This function can be called multiple times on th...
Definition TSRRT.cpp:92
virtual void clear()
Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() wil...
Definition TSRRT.cpp:57
void setGoalBias(double goalBias)
Set the goal bias.
Definition TSRRT.h:110
virtual void setup()
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceIn...
Definition TSRRT.cpp:66
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.