An autonomous mobile robot path planning strategy using an enhanced slime mold algorithm

Ling Zheng; Chengzhi Hong; Huashan Song; Rong Chen

doi:10.3389/fnbot.2023.1270860

An autonomous mobile robot path planning strategy using an enhanced slime mold algorithm

Front Neurorobot. 2023 Oct 17:17:1270860. doi: 10.3389/fnbot.2023.1270860. eCollection 2023.

Authors

Ling Zheng^{1

2}, Chengzhi Hong³, Huashan Song⁴, Rong Chen⁵

Affiliations

¹ School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
² Shenzhen Research Institute of Central China Normal University, Shenzhen, China.
³ State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China.
⁴ Space-Time Information Department, China Mobile Intelligent Mobility Network Technology Co., Ltd., Wuhan, China.
⁵ Institute of Traffic Engineering, Wuhan Technical College of Communications, Wuhan, China.

Abstract

Introduction: Autonomous mobile robot encompasses modules such as perception, path planning, decision-making, and control. Among these modules, path planning serves as a prerequisite for mobile robots to accomplish tasks. Enhancing path planning capability of mobile robots can effectively save costs, reduce energy consumption, and improve work efficiency. The primary slime mold algorithm (SMA) exhibits characteristics such as a reduced number of parameters, strong robustness, and a relatively high level of exploratory ability. SMA performs well in path planning for mobile robots. However, it is prone to local optimization and lacks dynamic obstacle avoidance, making it less effective in real-world settings.

Methods: This paper presents an enhanced SMA (ESMA) path-planning algorithm for mobile robots. The ESMA algorithm incorporates adaptive techniques to enhance global search capabilities and integrates an artificial potential field to improve dynamic obstacle avoidance.

Results and discussion: Compared to the SMA algorithm, the SMA-AGDE algorithm, which combines the Adaptive Guided Differential Evolution algorithm, and the Lévy Flight-Rotation SMA (LRSMA) algorithm, resulted in an average reduction in the minimum path length of (3.92%, 8.93%, 2.73%), along with corresponding reductions in path minimum values and processing times. Experiments show ESMA can find shortest collision-free paths for mobile robots in both static and dynamic environments.

Keywords: artificial potential field; autonomous mobile robots; dynamic environment; path planning; slime mold algorithm.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by China Postdoctoral Science Foundation 2021M691114, the Central Funds Guiding the Local Science and Technology Development 2021Szvup045 and Fundamental Research Funds for the Central Universities 2042022kf0049.