Path planning algorithm based on Improved Artificial Potential Field method

Eryi Zhang

doi:10.54254/2755-2721/10/20230170

Research Article

Open access

Published on 25 September 2023

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Zhang,E. (2023). Path planning algorithm based on Improved Artificial Potential Field method. Applied and Computational Engineering,10,167-174.

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Path planning algorithm based on Improved Artificial Potential Field method

Eryi Zhang *^,1,

¹ School of University College Dublin

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/10/20230170

Abstract

The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.

Keywords

path planning, A-star, obstacle avoidance, artificial potential field.

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References

[1]. Mansur M T, Dissanayake R L, and Gamage M W M. 2006 Obstacle avoidance in mobile robots using randomized escape strategy. In Proc. IEEE International Conference on Robotics and Automation (ICRA), pp: 1109-1114.

[2]. Gao Y, Wang X, and Song Z. 2017 A novel potential field-based path planning method using heuristic search for mobile robots. Journal of Intelligent and Robotic Systems, 90(1), pp: 19-34.

[3]. Cho Y J, Oh S Y, and Kim K S. 2002 Path planning for mobile robots using a modified harmonic potential field method. IEEE Transactions on Robotics and Automation, 18(5), pp: 710-723.

[4]. Xie J, and Inami M. 2014 Obstacle avoidance in complex environments by a simulated annealing algorithm with a human-like learning capability. In Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO), pp: 1409-1414.

[5]. Roy S, and Roy A B. 2014 Path planning of mobile robot using potential field and semi-landmarking techniques. International Journal of Advanced Research in Computer and Communication Engineering, 3(8), pp: 7289-7293.

[6]. Alzugaray S, Unzueta L, and Alonso J M. 2013 A fuzzy artificial potential field for autonomous mobile robot navigation. Robotics and Autonomous Systems, 61(2), pp: 121-134.

[7]. Kucuk G B, Temeltas H, and Ozturk S. 2010 Extremal optimization-based artificial potential field method for obstacle avoidance of mobile robots. Robotics and Autonomous Systems, 58(2), pp: 179-186.

[8]. Mukhopadhyay S C, Acharjee S and Sanyal S. 2017 A wall-following mobile robot using artificial potential field and modified fuzzy logic controller. International Journal of Advanced Robotic Systems, 14(6), pp: 1-13.

[9]. Yu L, Li S, and Fu X. 2018 An obstacle avoidance method for mobile robots based on virtual flow field and modified artificial potential field. In Proc. IEEE International Conference on Mechatronics and Automation (ICMA), pp: 1471-1476.

[10]. Sha C, Guangming M, and Xinqing Wu. 2019 Path planning for mobile robots based on improved artificial potential field method. Computer Applications, 39(5), pp: 1385-1390.

[11]. Shuqin Z, lin Y, and Yang L. 2018 A multi-robot path planning algorithm based on the artificial potential field method. Computer Engineering and Applications, 54(5), pp: 183-188.

[12]. Jia J, and Wang J. 2019 Robot path planning algorithm based on improved artificial potential field method. Proceedings of the 2019 9th International Conference on Measuring Technology and Mechatronics Automation, pp: 534-538.

[13]. Yanhui L, Guangming, M., and Guang, C. 2019 Path planning for mobile robots based on dynamic artificial potential field method. Computer Applications, 45(2), pp: 77-82.

[14]. Guo C, and Wu H. 2020 Robot path planning algorithm based on a hybrid artificial potential field and genetic algorithm. Soft Computing, 24(3), pp: 1833-1847.

[15]. Yang C, and Li M. 2018 A new artificial potential field algorithm for multi-robot path planning. Journal of Intelligent and Fuzzy Systems, 34(1), pp: 33-40.

[16]. Yufeim W, Xin L, and Donglin, Zhao. 2018 Research on UAV path planning based on artificial potential field method. Computer Engineering and Design, 39(6), pp: 1487-1492.

[17]. Lin Y, and Su, Y. 2018 A hybrid potential field and particle swarm optimization algorithm for mobile robot pathplanning. International Journal of Advanced Robotic Systems, 15(4), 1729881418781316.

[18]. Li H, Li L, and Li D. 2020 Path planning for mobile robots based on improved artificial potential field algorithm. International Journal of Advanced Robotic Systems, 17(1), 1729881420903005.

[19]. Zhibin C, Wei W, and Weitao L. 2018 Robot path planning based on multi-objective artificial potential field method. Computer Applications and Software, 35(3), pp: 186-191.

[20]. Sun H, Zhu H, and Zhang Y. 2018 A path planning method for mobile robots based on improved artificial potential field algorithm. Proceedings of the 2018 3rd International Conference on Robotics and Automation Engineering, pp: 128-132.

Cite this article

Zhang,E. (2023). Path planning algorithm based on Improved Artificial Potential Field method. Applied and Computational Engineering,10,167-174.

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About volume

Volume title: Proceedings of the 2023 International Conference on Mechatronics and Smart Systems

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

ISBN：978-1-83558-009-7(Print) / 978-1-83558-010-3(Online)

Conference date: 24 June 2023

Editor：Alan Wang, Seyed Ghaffar

Series: Applied and Computational Engineering

Volume number: Vol.10

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

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