Learning the Voronoi Centers of a Mobile Robot's Configuration Space

Abstract

Partitioning the configuration space of a mo- bile robot is essential for the robot path planning task. However, most existing techniques either rely upon precise geometrical descriptions of the envi- ronment, or are static by nature. In this paper we propose a method by which the robot dynamically builds a Voronoi tessellation of its configuration space. In order to do