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Improved artificial potential field method for local minima
- 1 Jiangnan University
* Author to whom correspondence should be addressed.
Abstract
With the development of autonomous driving technology, its applications permeate many aspects of work and life, providing convenience while reducing labor costs. Path planning has always been important for autonomous driving, where APF is widely used thanks to its simplicity of calculation and effectiveness. However, there’re still problems existing such as local minima, influenced by initial positions and parameters, and so on. In this study, a better approach to solving the local minimum issue is suggested. Firstly, the odometry method is used for the determination of falling into local minima by saving and computing the relationships between adjacent steps. Subsequently, a variable step length method is designed for escaping local minimum points and bypassing obstacles in front. The feasibility and robustness of the method were verified by simulations, and this method proved capable of solving the local minima and planning a reasonable trajectory.
Keywords
autonomous driving, artificial potential field, local minima.
[1]. Z. Liangbo, Z. Guangsheng, Z. Ling and J. Pinghui 2021 Improved Research on Target Unreachable Problem of Path Planning Based on Artificial Potential Field for an Unmanned Aerial Vehicle, 2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE), Qingdao, China pp. 136-142.
[2]. L. Tian and C. Collins 2014 An effective AGV trajectory planning method using a genetic algorithm, Mechatronics, Vol.14, pp.455-470.
[3]. A. Ammar, H. Bennaceur, I. Châari, A. Koubâa, and M. Alajlan 2015 Relaxed Dijkstra and A* with linear complexity for AGV path planning problems in large-scale grid environments, Soft Computing, Vol. 20, pp. 4149-4174.A. Ammar, H. Bennaceur, I. Châari, A. Koubâa, and M. Alajlan 2015 Relaxed Dijkstra and A* with linear complexity for AGV path planning problems in large-scale grid environments, Soft Computing, Vol. 20, pp. 4149-4174.A. Ammar, H. Bennaceur, I. Châari, A. Koubâa, and M. Alajlan 2015 Relaxed Dijkstra and A* with linear complexity for AGV path planning problems in large-scale grid environments, Soft Computing, Vol. 20, pp. 4149-4174.
[4]. C. Zheyi and X. Bing 2021 AGV Path Planning Based on Improved Artificial Potential Field Method, 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China pp. 32-37.
[5]. O. Khatib 1986 Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, International Journal of Robotics Research, vol. 5, pp. 90-98.
[6]. O. Khatib 1986 The Potential Field Approach and Operational Space Formulation in Robot Control. Springer, New York.
[7]. Li, B. Tian, Y. Yang and C. Li 2022 Path planning of robot based on artificial potential field method, 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China pp. 91-94.
[8]. Y. Qin, Z. Liu, L. Fu, Z. Dong, Q. Sun and D. He 2021 Impulsive Consensus Algorithms for Second-order Multi-agent Formation Based on the Improved Artificial Potential Field, 2021 International Conference on Neuromorphic Computing（ICNC）, Wuhan, China, pp.188-193.
[9]. Y. Sun, W. Chen and J. Lv 2022 Uav Path Planning Based on Improved Artificial Potential Field Method, 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi'an, China, pp. 95-100.
[10]. He, Y. Su, j. Guo, X. Fan, Z. Liu and B. Wang 2020 Dynamic path planning of mobile robot based on artificial potential field, 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), Sanya, China, pp. 259-264.
Cite this article
Miao,F. (2023). Improved artificial potential field method for local minima. Applied and Computational Engineering,10,47-54.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Mechatronics and Smart Systems
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