Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields
Author:
Zheng Li1, Yu Wenjie2, Li Guangxu2, Qin Guangxu3, Luo Yunchuan4
Affiliation:
1. School of Automation and Electrical Engineering, Chengdu Technological University, Chengdu 611730, China 2. School of Automation, Chengdu University of Information Technology, Chengdu 610225, China 3. Chengdu Shengke Information Technology Co., Ltd., Chengdu 610017, China 4. Sichuan Research Institute of Chemical Quality and Safety Inspection, Chengdu 610031, China
Abstract
Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive.
Funder
Key R&D Projects in Sichuan Province Sichuan Provincial Market Supervision Administration Science and Technology Plan Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference37 articles.
1. Brancalião, L., Gonçalves, J., Conde, M., and Costa, P. (2022). Systematic Mapping Literature Review of Mobile Robotics Competitions. Sensors, 22. 2. Chakraborty, S., Elangovan, D., Govindarajan, P.L., ELnaggar, M.F., Alrashed, M.M., and Kamel, S. (2022). A Comprehensive Review of Path Planning for Agricultural Ground Robots. Sustain, 14. 3. Ravankar, A., Ravankar, A.A., Kobayashi, Y., Hoshino, Y., and Peng, C.C. (2018). Path smoothing techniques in robot navigation: State-of-the-art, current and future challenges. Sensors, 18. 4. Sánchez-Ibáñez, J.R., Pérez-Del-pulgar, C.J., and García-Cerezo, A. (2021). Path planning for autonomous mobile robots: A review. Sensors, 21. 5. Sugihara, K., and Smith, J. (1997, January 10–11). Genetic algorithms for adaptive motion planning of an autonomous mobile robot. Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97, Monterey, CA, USA.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|