A modular and less complex environment representation algorithm [for mobile robots]

Abstract

The purpose of environment representation is to map the external real-world of the robot (workspace) and its evolution to an internal data structure usable by the motion planning algorithm. This operation is essential in the development of goal-attaining (complete) motion commands for an autonomous robot. In this paper, the authors present a modular environment representation which can readily be used by a hill-climbing search method to find a goal-attaining path for the robot. Capitalizing on the properties of harmonic potential functions and absorbing Markov chains, this paper presents a new method of environment representation which: (i) is able to map the robot environment to local-minima-free potential fields; (ii) is capable of handling exact geometries so that no geometric approximation is required; (iii) requires less memory for data storage than commonly used methods of environment representation; and (iv) is computationally less complex than the existing methods of representation. The process of environment representation is carried out in two stages, as described in the paper.