demos/PlannerProgressProperties.cpp
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00034 
00035 /* Author: Luis G. Torres */
00036 
00037 #include <ompl/geometric/SimpleSetup.h>
00038 #include <ompl/tools/benchmark/Benchmark.h>
00039 #include <ompl/base/spaces/RealVectorStateSpace.h>
00040 #include <ompl/geometric/planners/rrt/RRTstar.h>
00041 
00042 namespace ob = ompl::base;
00043 namespace og = ompl::geometric;
00044 
00046 //
00047 // In this demo we define a simple 2D planning problem to get from
00048 // (0,0) to (1,1) without obstacles. We're interested in collecting
00049 // data over the execution of a planner using the Benchmark class.
00050 //
00051 // This program will output two files: a .console file, and a .log
00052 // file. You can generate plots in a file called "plot.pdf" from this
00053 // experiment using the ompl/scripts/ompl_benchmark_statistics.py
00054 // script and the .log file like so:
00055 //
00056 // python path/to/ompl/scripts/ompl_benchmark_statistics.py <your_log_filename>.log -p plot.pdf
00057 //
00058 // Toward the bottom of the generated plot.pdf file, you'll see plots
00059 // for how various properties change, on average, during planning
00060 // runs.
00061 int main(int argc, char** argv)
00062 {
00063     ob::StateSpacePtr space(new ob::RealVectorStateSpace(2));
00064     space->as<ob::RealVectorStateSpace>()->setBounds(0, 1);
00065     og::SimpleSetup ss(space);
00066 
00067     // Set our robot's starting state to be the bottom-left corner of
00068     // the environment, or (0,0).
00069     ob::ScopedState<> start(space);
00070     start->as<ob::RealVectorStateSpace::StateType>()->values[0] = 0.0;
00071     start->as<ob::RealVectorStateSpace::StateType>()->values[1] = 0.0;
00072 
00073     // Set our robot's goal state to be the top-right corner of the
00074     // environment, or (1,1).
00075     ob::ScopedState<> goal(space);
00076     goal->as<ob::RealVectorStateSpace::StateType>()->values[0] = 1.0;
00077     goal->as<ob::RealVectorStateSpace::StateType>()->values[1] = 1.0;
00078 
00079     // Goal tolerance is 0.05
00080     ss.setStartAndGoalStates(start, goal, 0.05);
00081 
00082     // Create a new Benchmark object and set the RRTstar algorithm as
00083     // the planner. We're using RRTstar because currently, only the
00084     // RRTstar algorithm reports interesting planner progress
00085     // properties.
00086     ompl::tools::Benchmark b(ss, "my experiment");
00087     og::RRTstar* rrt = new og::RRTstar(ss.getSpaceInformation());
00088     rrt->setName("rrtstar");
00089 
00090     // We disable goal biasing so that the straight-line path doesn't
00091     // get found immediately. We want to demonstrate the steady
00092     // downward trend in path cost that characterizes RRTstar.
00093     rrt->setGoalBias(0.0);
00094 
00095     b.addPlanner(ob::PlannerPtr(rrt));
00096 
00097     // Here we specify options for this benchmark. Maximum time spent
00098     // per planner execution is 2.0 seconds, maximum memory is 100MB,
00099     // number of planning runs is 50, planner progress properties will
00100     // be queried every 0.01 seconds, and a progress bar will be
00101     // displayed to show how the benchmarking is going.
00102     ompl::tools::Benchmark::Request req;
00103     req.maxTime = 2.0;
00104     req.maxMem = 100;
00105     req.runCount = 50;
00106     req.timeBetweenUpdates = 0.01;
00107     req.displayProgress = true;
00108     b.benchmark(req);
00109 
00110     b.saveResultsToFile();
00111 
00112     return 0;
00113 
00114 }
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