Whether search directions number affects the efficiency of the path planning algorithm: Taking an improved ACO algorithm with 32 directions for example

Article type: Research Article

Authors: Zhang, Jianhua^a | Liu, Chan^a | Geng, Na^{a; *} | Zhang, Yixuan^a | Yang, Liqiang^{a; b}

Affiliations: [a] School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China | [b] Xuzhou Metro Group Corporation, Ltd., Xuzhou, China

Correspondence: [*] Corresponding author. Na Geng, School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China. E-mail: gengna@126.com.

Abstract: An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm.

Keywords: Ant colony optimization, mobile robot, path planning, search direction, area-inspired, grid map

DOI: 10.3233/JIFS-238095

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10535-10552, 2024