An Experimental Evaluation of Collision Detection Packages for Robot Motion Planning

Randomized motion planners are greatly affected by the efficiency and robustness of algorithms for collision checking of robot configurations. A number of powerful collision detection algorithms are currently available to the research community although their relative capabilities and performance cannot be easily compared due to the many factors involved. We have experimentally evaluated collision detection packages within the context of motion planning for rigid and articulated robots in 3D workspaces. Artificial and realistic problems have been chosen as benchmarks to assess package behavior with different object models. Reported experimental results should help the user in choosing the appropriate collision detection package. In this paper we also present the framework we exploited in our planner to allow pluggable distance computation functions and keep the code simple and easy to maintain.