Software for project-based learning of robot motion planning

Abstract

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can be explained in a simplified two-dimensional setting, but this masks many of the subtleties and complexities of the underlying problem. We have developed software for project-based learning of motion planning that enables deep learning. The projects that we have developed allow advanced undergraduate students and graduate students to reflect on the performance of existing textbook algorithms and their own variations on such algorithms. Formative assessment has been conducted at three institutions. The core of the software used for this teaching module is also used within the Robot Operating System, a widely adopted platform by the robotics research community. This allows for transfer of knowledge and skills to robotics research projects involving a large variety robot hardware platforms.

Keywords:

• robotics education
• motion planning
• project-based learning
• educational software

Acknowledgments

The authors are indebted to Ioan Şucan for his significant role in the development of OMPL and to other members of the Kavraki Lab for their contributions to OMPL and other motion planning software that preceded it and provided some inspiration for the current design. We are grateful to the faculty members and students at NUS and WPI for their participation in our project.

Notes

This project was funded by National Science Foundation [grant number CCLI 0920721] and by Willow Garage.

1 Both problem-based and project-based learning are abbreviated as PBL in the literature. In this article PBL will refer to project-based learning.