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Soft subdivision motion planning for complex planar robots

COMPUTATIONAL geometry-THEORY AND APPLICATIONS 2021年 92卷 101683-101683页

作者： Zhou, Bo Chiang, Yi-Jen Yap, Chee NYU Dept Comp Sci & Engn Brooklyn NY 11201 USA NYU Dept Comp Sci New York NY USA

The design and implementation of theoretically-sound robot motion planning algorithms is challenging. Within the framework of resolution-exact algorithms, it is possible to exploit soft predicates 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 decomposability property of soft collision detection predicates of such robots. We introduce a general technique to produce such a decomposition. If the robot is an m-gon, the complexity of this approach scales linearly in m. This contrasts with the O (m(3)) 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 Soft Subdivision Search (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 Core Library. The experiments show that our algorithms are effective, perform in real time on non-trivial environments, and can outperform many sampling-based methods. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Computational geometry Algorithmic motion planning Resolution-exact algorithms Soft predicates planar robots with complex geometry

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