Local minimum enhanced artificial potential field method

Research Article
Open access

Local minimum enhanced artificial potential field method

Zhihao Bai 1*
  • 1 Zhengzhou University of Light Industry    
  • *corresponding author 542013460401@zzuli.edu.cn
Published on 25 September 2023 | https://doi.org/10.54254/2755-2721/10/20230168
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

Currently, the artificial potential field technique is commonly used in vehicle obstacle avoidance and route planning. However, this method may lead to local minima where the gravitational force and repulsive force are equal during the path planning process, and thus the target location cannot be reached. This paper suggests an approach to enhance the artificial potential field method in light of this. First, the calculation method of the attraction field in the traditional artificial potential field is modified for resolving the issue of unreachable targets. Second, for the local minimum, the path calculation is recovered by modifying the repulsive force range of obstacles and creating a virtual obstacle point, and applying an additional force to get rid of the gravitational force and repulsive force balance. The simulation results check the effectiveness of the designed method, which can solve the cases of unreachable targets and local minima and produce reasonable planned paths.

Keywords:

path planning, local minima, artificial potential field method.

Bai,Z. (2023). Local minimum enhanced artificial potential field method. Applied and Computational Engineering,10,150-158.
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References

[1]. C H Li, C W Zheng, C P Zhou, et al., 2002 Fast search algorithm for 3D-route planning, Journal of Astronautics, vol. 23, no. 3, pp. 13-17.

[2]. T Han, W C Wu, C Q Huang et al., , 2012 Path planning of UAV based on Voronoi diagram and DPSO, Procedia Engineering, vol. 29, pp. 4198-4203.

[3]. C. Saranya, Manju Unnikrishnan, Akbar Ali, et al., 2016 Terrain based D* algorithm for path planning, IFAC-PapersOnline, vol. 49, no. 1, pp. 178-182.

[4]. S. Jung and S. Pramanik, 2002 An Efficient Path Computation Model for Hierarchically Structured Topographical Road Maps, IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 5, pp. 1029-1046.

[5]. O. Khatib, 1986 Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, The International Journal of Robotics Research, vol. 5, no. 1, pp. 90-98.

[6]. H. Li, 2020 Robotic Path Planning Strategy Based on Improved Artificial Potential Field 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Beijing, China, pp. 67-71.

[7]. H. Shen and P. Li, 2020 Unmanned Aerial Vehicle (UAV) Path Planning Based on Improved Pre-planning Artificial Potential Field Method 2020 Chinese Control And Decision Conference (CCDC), Hefei, China, pp. 2727-2732.

[8]. H. Li, Z. Wang, and Y. Ou, 2019 Obstacle Avoidance of Manipulators Based on Improved Artificial Potential Field Method 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, pp. 564-569.

[9]. Q. Liu, J. Liu, Y. Zhao, R. Shen, L. Hou, and Y. Zhang, 2022 Local path planning for Route Planningmulti-robot systems based on improved artificial potential field algorithm 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, pp. 1540-1544.

[10]. Q. Liang, H. Zhou, W. Xiong and L. 2022 Zhou, Improved artificial potential field method for UAV path planning 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China, pp. 657-660.

[11]. 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.

[12]. W. Di, L. Caihong, G. Na, S. Yong, G. 2020 Tengteng and L. Guoming, Local Path Planning of Mobile Robot Based on Artificial Potential Field 2020 39th Chinese Control Conference (CCC), Shenyang, China, pp. 3677-3682.

[13]. Z. Yingkun, 2018 Flight path planning of agriculture UAV based on improved artificial potential field method 2018 Chinese Control And Decision Conference (CCDC), Shenyang, China, pp. 1526-1530.

Cite this article

Bai,Z. (2023). Local minimum enhanced artificial potential field method. Applied and Computational Engineering,10,150-158.

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]. C H Li, C W Zheng, C P Zhou, et al., 2002 Fast search algorithm for 3D-route planning, Journal of Astronautics, vol. 23, no. 3, pp. 13-17.

[2]. T Han, W C Wu, C Q Huang et al., , 2012 Path planning of UAV based on Voronoi diagram and DPSO, Procedia Engineering, vol. 29, pp. 4198-4203.

[3]. C. Saranya, Manju Unnikrishnan, Akbar Ali, et al., 2016 Terrain based D* algorithm for path planning, IFAC-PapersOnline, vol. 49, no. 1, pp. 178-182.

[4]. S. Jung and S. Pramanik, 2002 An Efficient Path Computation Model for Hierarchically Structured Topographical Road Maps, IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 5, pp. 1029-1046.

[5]. O. Khatib, 1986 Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, The International Journal of Robotics Research, vol. 5, no. 1, pp. 90-98.

[6]. H. Li, 2020 Robotic Path Planning Strategy Based on Improved Artificial Potential Field 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Beijing, China, pp. 67-71.

[7]. H. Shen and P. Li, 2020 Unmanned Aerial Vehicle (UAV) Path Planning Based on Improved Pre-planning Artificial Potential Field Method 2020 Chinese Control And Decision Conference (CCDC), Hefei, China, pp. 2727-2732.

[8]. H. Li, Z. Wang, and Y. Ou, 2019 Obstacle Avoidance of Manipulators Based on Improved Artificial Potential Field Method 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, pp. 564-569.

[9]. Q. Liu, J. Liu, Y. Zhao, R. Shen, L. Hou, and Y. Zhang, 2022 Local path planning for Route Planningmulti-robot systems based on improved artificial potential field algorithm 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, pp. 1540-1544.

[10]. Q. Liang, H. Zhou, W. Xiong and L. 2022 Zhou, Improved artificial potential field method for UAV path planning 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China, pp. 657-660.

[11]. 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.

[12]. W. Di, L. Caihong, G. Na, S. Yong, G. 2020 Tengteng and L. Guoming, Local Path Planning of Mobile Robot Based on Artificial Potential Field 2020 39th Chinese Control Conference (CCC), Shenyang, China, pp. 3677-3682.

[13]. Z. Yingkun, 2018 Flight path planning of agriculture UAV based on improved artificial potential field method 2018 Chinese Control And Decision Conference (CCDC), Shenyang, China, pp. 1526-1530.

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