Soft subdivision motion planning for complex planar robots

The design and implementation of theoretically-sound robot motion planning algorithms is challenging. Within the framework of , it is possible to exploit for collision detection. The design of soft predicates is a balancing act between their implementability and their accuracy/effectivity.In this paper, we focus on the class of planar polygonal rigid robots with arbitrarily complex geometry. We exploit the remarkable property of soft collision-detection predicates of such robots. We introduce a general technique to produce such a decomposition. If the robot is an -gon, the complexity of this approach scales linearly in . This contrasts with the complexity known for exact planners. It follows that we can now routinely produce soft predicates for any rigid polygonal robot. This results in resolution-exact planners for such robots within the general (SSS) framework. This is a significant advancement in the theory of sound and complete planners for planar robots.We implemented such decomposed predicates in our open-source . The experiments show that our algorithms are effective, perform in real time on non-trivial environments, and can outperform many sampling-based methods.