Title: Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning

Authors: Guanghui Li; Yusuke Tamura; Atsushi Yamashita; Hajime Asama

Addresses: Department of Precision Engineering, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-8656, Japan ' Faculty of Science and Engineering, Chuo University, Kasuga 1-3-27, Bunkyo-ku, Tokyo, 112-8551, Japan ' Department of Precision Engineering, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-8656, Japan ' Department of Precision Engineering, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-8656, Japan

Abstract: This paper presents an effective improved artificial potential field-based regression search (improved APF-based RS) method that can obtain a better and shorter path efficiently without local minima and oscillations in an environment including known, partially known or unknown, static, and dynamic environments. We redefine potential functions to eliminate oscillations and local minima problems, and use improved wall-following methods for the robots to escape non-reachable target problems. Meanwhile, we develop a regression search method to optimise the planned path. The optimisation path is calculated by connecting the sequential points produced by improved APF. The simulations demonstrate that the improved APF method easily escapes from local minima, oscillations, and non-reachable target problems. Moreover, the simulation results confirm that our proposed path planning approach can calculate a shorter or more nearly optimal than the general APF can. Results prove our improved APF-based RS method's feasibility and efficiency for solving path planning.

Keywords: autonomous robots; mobile robots; path planning; robot navigation; artificial potential field; bidirectional APF; regression search; simulation.

DOI: 10.1504/IJMA.2013.055612

International Journal of Mechatronics and Automation, 2013 Vol.3 No.3, pp.141 - 170

Received: 22 Oct 2012
Accepted: 24 Jan 2013
Published online: 30 Apr 2014 *

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