ompl_constrained_planning.rst

OMPL Constrained Planning

This tutorial shows you how to use OMPL's Constrained planning capabilities from MoveIt. Three planning problems are solved with different types of path constraints. It will be easier to follow if you have read through the tutorial on the move group Python interface.

For a short walkthrough of running the tutorial, you can watch this video.

When can I use this planner?

The interface currently only supports position constraints on a robot link, where the constrained regions is represented using a box. The planning approach provides an alternative for the enforce_joint_model_state_space option. It is expected to be most valuable for constraints regions that have a small (or zero) volume in Cartesian space, where the rejection sampling does not always works. For example, keeping the end-effector on a plane or along a line.

Configure OMPL

OMPL reads configurations parameters from a file called ompl_planning.yaml. This tutorial uses the Panda robot, for which this file can be found in panda_moveit_config/config/ompl_planning.yaml. We will add a parameter to tell OMPL to plan in a constrained state space by setting enforce_constrained_state_space. In addition, if the parameter projection_evaluator was not yet specified we also need to add it.

panda_arm:
   enforce_constrained_state_space: true
   projection_evaluator: joints(panda_joint1,panda_joint2)

Note that, as we are changing configuration files, you should also clone the repository panda_moveit_config in you're workspace. (Instead of using the version installed with sudo apt install.

cd catkin_ws/src
git clone https://github.com/ros-planning/panda_moveit_config.git
cd ../..
catkin build

Run the tutorial

After you changed the configuration as explained in the previous section, launch the move group node for the panda robot:

roslaunch panda_moveit_config demo.launch

Then add a Marker display to Rviz:

Open a new terminal window to run the tutorial node:

rosrun moveit_tutorials ompl_constrained_planning_tutorial.py

A red and green sphere should appear in Rviz to show the start and goal states. In addition, a grey box should appear that represents the position constraint on the link panda_link8. If planning succeeds, you should see a preview of the trajectory that was planned.

The following message appears in the terminal:

============ Press enter to continue with the second planning problem.

After pressing enter, the next planning problem is solved.

Again, if planning succeeds, the trajectory is animated in rviz. And finally the last planning problem is solved after pressing enter again.

To see the output from the planner, look in the terminal window where you launched the Panda's move group node. To replay the planned trajectory, you can add a "Trajectory Slider" panel in Rviz.

The code explained

.. tutorial-formatter:: ./scripts/ompl_constrained_planning_tutorial.py