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      <li>The Open Motion Planning Library</li>
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<div class="textblock"><h2><a class="anchor" id="gettingStarted"></a>
Getting Started</h2>
<ul>
<li>Follow the instructions to <a class="el" href="download.html">download</a> and <a class="el" href="installation.html">install</a> OMPL.</li>
<li>Read the <a class="el" href="api_overview.html">API overview</a>.</li>
<li>Learn how to integrate your own code with <a class="el" href="buildSystem.html">OMPL's build system</a>.</li>
<li>Read about the <a class="el" href="availablePlanners.html">available planners</a> and the <a class="el" href="availableStateSpaces.html">available state spaces</a>.</li>
<li>If interested in using Python, make sure to read <a class="el" href="python.html">the documentation for the python bindings</a>.</li>
<li>Get more information about <a class="el" href="education.html">using OMPL for educational purposes</a>.</li>
<li>Take a look at <a class="el" href="thirdparty.html">Third-party contributions.</a> (<a class="el" href="contrib.html">Contribute your own extensions!</a>)</li>
</ul>
<h2><a class="anchor" id="tutorials"></a>
Tutorials</h2>
<p>Below we include a number of tutorials we believe are useful for understanding how OMPL works, grouped by level of difficulty. Users are encouraged to look at the examples in <b>demos/</b> as well.</p>
<dl class="user"><dt><b>Beginner</b></dt><dd><ul>
<li><a class="el" href="geometricPlanningSE3.html">Geometric planning for a rigid body in 3D</a></li>
<li><a class="el" href="stateValidation.html">Setting up state validity checking</a></li>
<li><a class="el" href="workingWithStates.html">Working with states and state spaces</a></li>
<li><a class="el" href="samplers.html">Using existing samplers and creating new ones</a></li>
</ul>
</dd></dl>
<dl class="user"><dt><b>Intermediate</b></dt><dd><ul>
<li><a class="el" href="genericPlanning.html">Generic instructions for setting up a planning context</a></li>
<li><a class="el" href="implementingStateSpaces.html">Implementing new state spaces</a></li>
<li><a class="el" href="goalRepresentation.html">Representing goals</a></li>
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<dl class="user"><dt><b>Advanced</b></dt><dd><ul>
<li><a class="el" href="benchmark.html">Benchmarking planners</a></li>
<li><a class="el" href="newPlanner.html">Adding a new planner</a></li>
<li><a class="el" href="projections.html">Setting up a projection</a></li>
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<h2><a class="anchor" id="demos"></a>
Demos</h2>
<ul>
<li><a href="RigidBodyPlanning_8cpp_source.html"><b>Rigid body planning</b></a> <a href="RigidBodyPlanning_8py_source.html"><b>[Python version]</b></a>. This demo is essentially the same example described in the first tutorial. It illustrates how to use the main classes.</li>
<li><a href="StateSampling_8cpp_source.html"><b>State sampling</b></a> <a href="StateSampling_8py_source.html"><b>[Python version]</b></a>. This is the demo program used in the last beginner tutorial.</li>
<li><a href="RigidBodyPlanningWithControls_8cpp_source.html"><b>Rigid body planning with controls</b></a> <a href="RigidBodyPlanningWithControls_8py_source.html"><b>[Python version]</b></a>. This demo shows how to perform planning under differential constraints for a simple car-like vehicle.</li>
<li><a href="RigidBodyPlanningWithIntegrationAndControls_8cpp_source.html"><b>Rigid body planning with integration and controls. </b></a> This example extends the previous example by showing how one can plan for systems of ordinary differential equations in a generic way. This example uses simple Euler integration. For higher accuracy it is recommended to use higher-order methods. Other libraries such as the <a href="http://www.gnu.org/software/gsl/">GNU Scientific Library</a> or <a href="http://headmyshoulder.github.com/odeint-v2/">Boost.Numeric.Odeint</a> can be used for this purpose.</li>
<li><a href="RigidBodyPlanningWithIK_8cpp_source.html"><b>Rigid body planning with an Inverse Kinematics solver generating goal states in a separate thread. </b></a> This demo shows off two neat features of OMPL: a genetic algorithm-based Inverse Kinematics solver and a lazy goal state sampler. In a separate thread goal states are computed by the IK solver. While solving a motion planning problem, the planning algorithms select a random goal state from the ones computed so far.</li>
<li><a href="ODERigidBodyPlanning_8cpp_source.html"><b>Rigid body planning using the Open Dynamics Engine (ODE). </b></a> When ODE is installed, OMPL will compile an extension that makes is easier to use ODE for forward propagation of models of motion. In this example, a box is pushed around in the plane from a start position to a goal position. </li>
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  <a href="http://www.kavrakilab.org">Physical and Biological Computing Group</a> &bull;
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