PLRC*: A piecewise linear regression complex for approximating optimal robot motion
Luyang Zhao, Josiah Putman, Weifu Wang, Devin Balkcom · 2020
Discrete graphs are commonly used to approximately represent configuration spaces used in robot motion planning. This paper explores a representation in which the costs of crossing local regions of the configuration space are represented using piecewise linear regression (PLR). We explore a few simple motion planning problems, and show that for these problems, the memory required to store the representation compares favorably to that required for standard discrete vertex-and-edge models, while preserving the quality of paths returned from searches.