Improved artificial potential field to solve the problem of local minimum

Research Article
Open access

Improved artificial potential field to solve the problem of local minimum

Hao Jiang 1*
  • 1 Nanjing Agricultural University    
  • *corresponding author 9203011213@stu.njau.edu.cn
Published on 25 September 2023 | https://doi.org/10.54254/2755-2721/10/20230169
ACE Vol.10
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-009-7
ISBN (Online): 978-1-83558-010-3

Abstract

The artificial potential field approach is a traditional method that is frequently used for local path planning, but in order for it to be successful, it requires more than unreachable target sites, local minimums, and oscillation difficulties. An enhanced method that is based on discretized sampling was presented as a potential solution to these issues as a result of this information. In the first place, a decision must be made regarding whether or not to enter the local minimum mark. In order to free ourselves from the confines of the local minimum, we subsequently devised a motion that was perpendicular to the direction in which the object was being moved. In the end, a regression search is carried out in order to further hone in on the correct path. The results of the simulation show that this method is capable of efficiently handling the local minimum problem and providing appropriate paths, as demonstrated by a comparison between the conventional method and the enhanced method.

Keywords:

local minimum, artificial potential field, path optimization.

Jiang,H. (2023). Improved artificial potential field to solve the problem of local minimum. Applied and Computational Engineering,10,159-166.
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References

[1]. Wang Xinmin,Wang Xiaoyan, Xiao Kun 2015 Formation Flight Technology of UAV Xi an: Northwestern Polytechnic University Press 70-83

[2]. Chen Haiyun, Chen Huayun, Liu Qiang 2020 Multi-UAV 3D formation path planning based on improved artificial potential field method Journal of System Simulation, 2020(3):7

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

[4]. Gao Xi na, Wu Lijuan, Li Weiwei, et al. 2014 Formation control of multi robots with artificial potential field method Journal of University of Science and Technology Liaoning 37(4): 381-386

[5]. Zhu Yi, Zhang Tao, Song Jingyan. 2010 Study on the Local Minima Problem of Path Planning Using Potential Field Method in Unknown Environments Acta Automatica Sinica 36(8): 1122-1130

[6]. Luo Qianyou, Zhang Hua, Wang Da, et al. 2011 Application of improved artificial potential field approach in local path planning for mobile robot Computer Engineering and Design 32(4): 1411-1413

[7]. Yang X, Yang W, Zhang H et al. 2016 A new method for robot path planning based artificial potential field 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). IEEE 2016: 1294-1299

[8]. Sun F, Han S 2016 A flight path planning method based on improved artificial potential field International Conference on Computer, Information and Telecommunication Systems. IEEE 2016: 1-5

[9]. Sun Jingliang, Liu Chunsheng, Shi Haoming 2015 Optimal consensus algorithm for obstacle avoidance based on dynamic potential field Flight Dynamics 33(4): 376-380

[10]. Guo Xiaopeng 2017 Research on Improved Artificial Potential Field Path Planning AlgorithmHarbin: Harbin Institute of Technology 2017: 26-42

Cite this article

Jiang,H. (2023). Improved artificial potential field to solve the problem of local minimum. Applied and Computational Engineering,10,159-166.

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 Mechatronics and Smart Systems

ISBN：978-1-83558-009-7(Print) / 978-1-83558-010-3(Online)
Editor：Alan Wang, Seyed Ghaffar
Conference website: https://2023.confmss.org/
Conference date: 24 June 2023
Series: Applied and Computational Engineering
Volume number: Vol.10
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Wang Xinmin,Wang Xiaoyan, Xiao Kun 2015 Formation Flight Technology of UAV Xi an: Northwestern Polytechnic University Press 70-83

[2]. Chen Haiyun, Chen Huayun, Liu Qiang 2020 Multi-UAV 3D formation path planning based on improved artificial potential field method Journal of System Simulation, 2020(3):7

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

[4]. Gao Xi na, Wu Lijuan, Li Weiwei, et al. 2014 Formation control of multi robots with artificial potential field method Journal of University of Science and Technology Liaoning 37(4): 381-386

[5]. Zhu Yi, Zhang Tao, Song Jingyan. 2010 Study on the Local Minima Problem of Path Planning Using Potential Field Method in Unknown Environments Acta Automatica Sinica 36(8): 1122-1130

[6]. Luo Qianyou, Zhang Hua, Wang Da, et al. 2011 Application of improved artificial potential field approach in local path planning for mobile robot Computer Engineering and Design 32(4): 1411-1413

[7]. Yang X, Yang W, Zhang H et al. 2016 A new method for robot path planning based artificial potential field 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). IEEE 2016: 1294-1299

[8]. Sun F, Han S 2016 A flight path planning method based on improved artificial potential field International Conference on Computer, Information and Telecommunication Systems. IEEE 2016: 1-5

[9]. Sun Jingliang, Liu Chunsheng, Shi Haoming 2015 Optimal consensus algorithm for obstacle avoidance based on dynamic potential field Flight Dynamics 33(4): 376-380

[10]. Guo Xiaopeng 2017 Research on Improved Artificial Potential Field Path Planning AlgorithmHarbin: Harbin Institute of Technology 2017: 26-42

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