默认规划路径算法和RRTConnet路径规划算法生成路径

1.  源代码

#include <ompl/base/SpaceInformation.h>
#include <ompl/base/spaces/SE3StateSpace.h>
#include <ompl/geometric/planners/rrt/RRTConnect.h>
#include <ompl/geometric/SimpleSetup.h>

#include <ompl/config.h>
#include <iostream>

#include <fstream>
#include <ostream>

namespace ob = ompl::base;
namespace og = ompl::geometric;

bool isStateValid(const ob::State *state)
{
	// cast the abstract state type to the type we expect
	const auto *se3state = state->as<ob::SE3StateSpace::StateType>();

	// extract the first component of the state and cast it to what we expect
	const auto *pos = se3state->as<ob::RealVectorStateSpace::StateType>(0);

	// extract the second component of the state and cast it to what we expect
	const auto *rot = se3state->as<ob::SO3StateSpace::StateType>(1);

	// check validity of state defined by pos & rot


	// return a value that is always true but uses the two variables we define, so we avoid compiler warnings
	return (const void*)rot != (const void*)pos;
}

void planWithSimpleSetup()
{
	// construct the state space we are planning in
	auto space(std::make_shared<ob::SE3StateSpace>());

	// set the bounds for the R^3 part of SE(3)
	ob::RealVectorBounds bounds(3);
	bounds.setLow(-1);
	bounds.setHigh(1);

	space->setBounds(bounds);

	// define a simple setup class
	og::SimpleSetup ss(space);

	// set state validity checking for this space
	ss.setStateValidityChecker([](const ob::State *state) { return isStateValid(state); });

	// create a random start state
	ob::ScopedState<> start(space);
	start.random();

	// create a random goal state
	ob::ScopedState<> goal(space);
	goal.random();

	// set the start and goal states
	ss.setStartAndGoalStates(start, goal);

	// this call is optional, but we put it in to get more output information
	ss.setup();
	ss.print();

	// set planner 
	ob::PlannerPtr planner(new og::RRTConnect(ss.getSpaceInformation()));
	ss.setPlanner(planner);

	// attempt to solve the problem within one second of planning time
	ob::PlannerStatus solved = ss.solve(1.0);

	if (solved)
	{
		std::cout << "Found solution:" << std::endl;

		std::ofstream ofs0("../plot/path0.dat");
		ss.getSolutionPath().printAsMatrix(ofs0);

		// print the path to screen
		ss.simplifySolution();
		ss.getSolutionPath().print(std::cout);

		std::ofstream ofs("../plot/path.dat");
		ss.getSolutionPath().printAsMatrix(ofs);
	}
	else
		std::cout << "No solution found" << std::endl;
}

int main(int /*argc*/, char ** /*argv*/)
{
	std::cout << "OMPL version: " << OMPL_VERSION << std::endl;

	planWithSimpleSetup();

	return 0;
}

2. Python可视化生成的原始路径和简化路径

from mpl_toolkits.mplot3d import Axes3D
import numpy
import matplotlib.pyplot as plt
data = numpy.loadtxt('path.dat')
data1 = numpy.loadtxt('path0.dat')
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(data[:,0],data[:,1],data[:,2],'.-')
plt.hold('on')
plt.grid('on')
ax.plot(data1[:,0],data1[:,1],data1[:,2],'r-')
plt.show()

路径可视化方法可以参考官网 http://ompl.kavrakilab.org/pathVisualization.html

 

OMPL 参考列表

1. http://ompl.kavrakilab.org/group__demos.html

2. http://ompl.kavrakilab.org/tutorials.html

3. http://ompl.kavrakilab.org/gui.html#gui_paths