Improved artificial potential field method for mobile robot path planning

Shuo Tao

doi:10.54254/2755-2721/33/20230259

Research Article

Open access

Published on 4 February 2024

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Tao,S. (2024). Improved artificial potential field method for mobile robot path planning. Applied and Computational Engineering,33,157-166.

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Improved artificial potential field method for mobile robot path planning

Shuo Tao *^,1,

¹ Department of Advanced Engineering, University of Science and Technology Beijing, Beijing, 100083, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/33/20230259

Abstract

Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments. A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm. The simulation results demonstrate the effectiveness of the method. In comparison to APF, the improved APF could prevent the local minimum and reach the target position with fewer processing steps and better performance.

Keywords

path planning, artificial potential field, local minimum

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References

[1]. Szczepanski, R., Tarczewski, T., & Erwinski, K. 2022. Energy efficient local path planning algorithm based on predictive artificial potential field. IEEE Access, 10, 39729-39742.

[2]. Zafar, M. N., & Mohanta, J. C. 2018. Methodology for path planning and optimization of mobile robots: A review. Procedia computer science, 133, 141-152.

[3]. Gul, F., Mir, I., Abualigah, L., Sumari, P., & Forestiero, A. 2021. A consolidated review of path planning and optimization techniques: Technical perspectives and future directions. Electronics, 10(18), 2250.

[4]. Hirata, M., Tsukada, M., Okumura, K., Tamura, Y., Ochiai, H., & Défago, X. 2021, September. Roadside-assisted cooperative planning using future path sharing for autonomous driving. In 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) (pp. 1-7). IEEE.

[5]. Szczepanski, R., Bereit, A., & Tarczewski, T. 2021. Efficient local path planning algorithm using artificial potential field supported by augmented reality. Energies, 14(20), 6642.

[6]. Fan, X., Guo, Y., Liu, H., Wei, B., & Lyu, W. 2020. Improved artificial potential field method applied for AUV path planning. Mathematical Problems in Engineering, 2020, 1-21.

[7]. Rostami, S. M. H., Sangaiah, A. K., Wang, J., & Liu, X. 2019. Obstacle avoidance of mobile robots using modified artificial potential field algorithm. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1-19.

[8]. Li, Y., Tian, B., Yang, Y., & Li, C. 2022, March. Path planning of robot based on artificial potential field method. In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) (Vol. 6, pp. 91-94). IEEE.

[9]. Yuan, X. 2022, November. Research on the Limitations of UAV Path Planning Based on Artificial Potential Field Method. In 2022 9th International Forum on Electrical Engineering and Automation (IFEEA) (pp. 619-622). IEEE.

[10]. Song, J., Hao, C., & Su, J. 2020. Path planning for unmanned surface vehicle based on predictive artificial potential field. International Journal of Advanced Robotic Systems, 17(2), 1729881420918461.

Cite this article

Tao,S. (2024). Improved artificial potential field method for mobile robot path planning. Applied and Computational Engineering,33,157-166.

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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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

Conference website: https://2023.confmla.org/

ISBN：978-1-83558-291-6(Print) / 978-1-83558-292-3(Online)

Conference date: 18 October 2023

Editor：Mustafa İSTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.33

ISSN：2755-2721(Print) / 2755-273X(Online)

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