We aim to transform how college students learn robotics by offering a motion planning curriculum that enhances deep learning and is supported by OMPL.app, an integrated software environment. Students will be challenged to work on real-world robotics problems, and develop deeper knowledge by reflecting on and formally evaluating their results. Our intention is to scaffold learning by freeing students of tedious details and heavy programming and help them through a hands-on problem-based learning approach to develop critical thinking within robotics and outside robotics.
As part of this effort, we have developed a collection of assignments that can be used in robotics classes that include a module on sampling-based planners. The assignments vary in difficulty and can be tailored to the needs of a class. They have been tested at the “Robotics Algorithms” class offered at Rice University. The current set of assignments includes:
We are looking for educational partners to use and further develop the material. If you are interested in the lectures and assignments, please contact Mark Moll and Lydia Kavraki.
Students from Rice University's COMP450 Algorithmic Robotics class, Fall 2010.