Efficient Incremental Penetration Depth Estimation between Convex Geometries

Abstract

Penetration depth (PD) is essential for robotics due to its extensive applications in dynamic simulation, motion planning, haptic rendering, etc. The Expanding Polytope Algorithm (EPA) is the de facto standard for this problem, which estimates PD by expanding an inner polyhedral approximation of an implicit set. In this paper, we propose a novel optimization-based algorithm that incrementally estimates minimum penetration depth and its direction. One major advantage of our method is the capability to be warm-started by leveraging the spatial and temporal coherence. This coherence emerges naturally in many robotic applications (e.g., the temporal coherence between adjacent simulation time knots). As a result, our algorithm achieves substantial speedup — we demonstrate it is 5-30x faster than EPA on several benchmarks. Moreover, our approach is built upon the same implicit geometry representation as EPA, which enables easy integration into existing software stacks. The code and supplemental document are available on: https://github.com/weigao95/mind-fcl.