Real-time path planning based on improved artificial potential field method

Research Article
Open access

Real-time path planning based on improved artificial potential field method

Guangyi Wang 1*
  • 1 New York University    
  • *corresponding author gw2315@nyu.edu
Published on 25 September 2023 | https://doi.org/10.54254/2755-2721/10/20230167
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

Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.

Keywords:

robots, improved artificial potential field method, local minima, oscillation, unreachable target point.

Wang,G. (2023). Real-time path planning based on improved artificial potential field method. Applied and Computational Engineering,10,139-149.
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References

[1]. Q. Liu, J. Liu, Y. Zhao, R. Shen, L. Hou, and Y. Zhang, 2022 “Local path planning for multi-robot systems based on improved artificial potential field algorithm,” in 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 5. IEEE, pp. 1540–1544.

[2]. Y. Li, B. Tian, Y. Yang, and C. Li, 2022 “Path planning of robot based on artificial potential field method,” in 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), vol. 6. IEEE, pp. 91–94.

[3]. F. Li, Z. Huang, and L. Xu, 2019 “Path planning of 6-dof venipuncture robot arm based on improved a-star and collision detection algorithms,” in 2019 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp. 2971–2976.

[4]. O. Erkut and F. Hardalac¸, 2021 “Comparison of ant colony optimization and artificial bee colony algorithms for solving electronic support search dwell scheduling problem,” in 2021 15th Turkish National Software Engineering Symposium (UYMS). IEEE, pp. 1–6.

[5]. B. Wan, Y. Qin, and W. W. Song, 2018 “Path planning strategy of mobile nodes based on improved rrt algorithm,” in 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA). IEEE, pp. 228–234.

[6]. P. V. S. Reddy, 2021 “Generalized fuzzy logic with twofold fuzzy set: learning through neural net and application to business intelligence,” in 2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, pp. 1–5.

[7]. Y. Sun, W. Chen, and J. Lv, 2022 “Uav path planning based on improved artificial potential field method,” in 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA). IEEE, pp. 95–100.

[8]. N. He, Y. Su, X. Fan, Z. Liu, B. Wang et al., 2020 “Dynamic path planning of mobile robot based on artificial potential field,” in 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). IEEE, pp. 259–264.

[9]. C. Jiang, Y. Gao, and P. Yang, 2021 “Mobile robot obstacle avoidance based on improved artificial potential field method,” in 2021 3rd International Conference on Robotics and Computer Vision (ICRCV). IEEE, pp. 18–23.

[10]. X. Chen and J. Zhang, 2013 “The three-dimension path planning of uav based on improved artificial potential field in dynamic environment,” in 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2. IEEE, pp. 144–147.

[11]. 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.

Cite this article

Wang,G. (2023). Real-time path planning based on improved artificial potential field method. Applied and Computational Engineering,10,139-149.

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]. Q. Liu, J. Liu, Y. Zhao, R. Shen, L. Hou, and Y. Zhang, 2022 “Local path planning for multi-robot systems based on improved artificial potential field algorithm,” in 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 5. IEEE, pp. 1540–1544.

[2]. Y. Li, B. Tian, Y. Yang, and C. Li, 2022 “Path planning of robot based on artificial potential field method,” in 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), vol. 6. IEEE, pp. 91–94.

[3]. F. Li, Z. Huang, and L. Xu, 2019 “Path planning of 6-dof venipuncture robot arm based on improved a-star and collision detection algorithms,” in 2019 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp. 2971–2976.

[4]. O. Erkut and F. Hardalac¸, 2021 “Comparison of ant colony optimization and artificial bee colony algorithms for solving electronic support search dwell scheduling problem,” in 2021 15th Turkish National Software Engineering Symposium (UYMS). IEEE, pp. 1–6.

[5]. B. Wan, Y. Qin, and W. W. Song, 2018 “Path planning strategy of mobile nodes based on improved rrt algorithm,” in 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA). IEEE, pp. 228–234.

[6]. P. V. S. Reddy, 2021 “Generalized fuzzy logic with twofold fuzzy set: learning through neural net and application to business intelligence,” in 2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, pp. 1–5.

[7]. Y. Sun, W. Chen, and J. Lv, 2022 “Uav path planning based on improved artificial potential field method,” in 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA). IEEE, pp. 95–100.

[8]. N. He, Y. Su, X. Fan, Z. Liu, B. Wang et al., 2020 “Dynamic path planning of mobile robot based on artificial potential field,” in 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). IEEE, pp. 259–264.

[9]. C. Jiang, Y. Gao, and P. Yang, 2021 “Mobile robot obstacle avoidance based on improved artificial potential field method,” in 2021 3rd International Conference on Robotics and Computer Vision (ICRCV). IEEE, pp. 18–23.

[10]. X. Chen and J. Zhang, 2013 “The three-dimension path planning of uav based on improved artificial potential field in dynamic environment,” in 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2. IEEE, pp. 144–147.

[11]. 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.

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