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

Frontiers in Neurorobotics

Neuroscience

Rong Chen¹  Ling Zheng²  Huashan Song³  Chengzhi Hong⁴ 

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

关键词: autonomous mobile robots;    path planning;    slime mold algorithm;    dynamic environment;    artificial potential field;   

DOI  :  10.3389/fnbot.2023.1270860

 received in 2023-08-01, accepted in 2023-09-25,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionAutonomous 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.MethodsThis 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 discussionCompared 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.

【 授权许可】

Unknown   
Copyright © 2023 Zheng, Hong, Song and Chen.