Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field
- 1 University of Bristol
* Author to whom correspondence should be addressed.
Abstract
With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.
Keywords
unmanned aerial vehicle, obstacle avoidance method, artificial potential field method, path-planning.
[1]. Wu Jianfa, Wang Honglun, Liu Yiheng, et al 2020 A review of research on drone obstacle avoidance route planning methods. Unmanned System Technology, 3(1), pp.1-10.
[2]. hubhani Aggarwal and Neeraj Kumar. 2020 Path Planning Techniques for Unmanned Aerial Vehicles: A Review, Solutions, and Challenges. Computer Communications, vol. 149, Jan. 2020, pp. 270-299.
[3]. Waschi, H. Wang, M. Cao, H. Jiang and L. Xie, 2018 Feasible Computationally Efficient Path Planning for UAV Collision Avoidance, IEEE 14th International Conference on Control and Automation (ICCA), Anchorage, AK, USA, 2018, pp. 576-581.
[4]. LIU Xuanbing, ZHOU Shaolei, XIAO Zhicai, et al 2022 Review on UAV obstacle avoidance methods Journal of Ordnance Equipment Engineering, ,43(05)：40-47.
[5]. Liu Chengli. 2021 Research status and prospects of drone obstacle avoidance control. Science and Technology Shangpin, (4): 97-98.
[6]. L. Keyu, L. Yonggen and Z. Yanchi 2020 Dynamic obstacle avoidance path planning of UAV Based on improved APF 5th International Conference on Communication, Image, and Signal Processing (CCISP), Chengdu, China, pp. 159-163.
[7]. Y. Koren and J. Borenstein 1991 Potential field methods and their inherent limitations for mobile robot navigation, Proc. IEEE Conf. Robotics and Automation, Sacramento, CA, Apr. 7–12, pp. 1398–1404.
[8]. Y. Koren and J. Borenstein 1991 Potential field methods and their inherent limitations for mobile robot navigation, Proc. IEEE Conf. Robotics and Automation, Sacramento, CA, Apr. 7–12, pp. 1398–1404.
[9]. Ge S S,Cui Y J 2002 Dynamic motion planning for mobile robots using potential field method, Autonomous Robots, ,13 (3): pp.207-222.
[10]. Palmieri, Luigi. 2015. A behavioural approach to obstacle avoidance for mobile manipulators based on distributed sensing. ArXiv, abs/1502.02465.
[11]. Cao L,Qiao D,Xu J 2018 Suboptimal artificial potential function sliding mode control for spacecraft rendezvous with obstacle avoidance, Acta Astronautica, 143:pp.133-146.
[12]. Luo G,Yu J,Mei Y,et al. 2015 UAV path planning in mixed- obstacle environment via artificial potential field method improved by additional control force. Asian Journal of Control, 17(5):pp.1600-1610.
[13]. Sullivan J,Waydo S,Campbell M. 2003 Using stream functions for complex behavior and path generation. AIAA Guidance, Navigation and Control Conference and Exhibit.
[14]. Waydo S,Murray R M. 2003 Vehicle motion planning using stream functions. IEEE International Conference on Robotics and Automation.
[15]. Y. Liu and Y. Zhao, A virtual waypoint based artificial potential field method for UAV path planning, 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing, China, pp. 949-953.
[16]. L. Dongcheng and D. Jiyang, 2020 Research on Multi-UAV Path Planning and Obstacle Avoidance Based on Improved Artificial Potential Field Method, 2020 3rd International Conference on Mechatronics, Robotics and Automation (ICMRA), Shanghai, China, pp. 84-88
[17]. A. Ma’Arif, W. Rahmaniar, M. A. M. Vera, A. A. Nuryono, R. Majdoubi and A. Çakan 2021 Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment, 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), Purwokerto, Indonesia, pp. 184-189
[18]. J. Sun, J. Tang and S. Lao, Collision Avoidance for Cooperative UAVs With Optimized Artificial Potential Field Algorithm, IEEE Access, 2019, vol. 5, pp. 18382-18390
[19]. Y. Du, X. Zhang and Z. Nie 2019 A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm, IEEE Access, vol. 7, pp. 169469-169479.
[20]. O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, Proceedings. 1985 IEEE International Conference on Robotics and Automation, St. Louis, MO, USA, pp. 500-505.
[21]. R. L. Galvez et al. 2017 Obstacle avoidance algorithm for swarm of quadrotor unmanned aerial vehicle using artificial potential fields, TENCON 2017 - 2017 IEEE Region 10 Conference, Penang, Malaysia, pp. 2307-2312.
[22]. Lan Yubin, Wang Linlin, Zhang Yali, 2018 Application Status and Prospects of Obstacle Avoidance Technology for Agricultural Drones, Transactions of the Chinese Society of Agricultural Engineering Vol. 34 No. 9, p104-113.
[23]. Shubhani Aggarwal, Neeraj Kumar, 2020 Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges, Computer Communications, Volume 149, pp.270-299.
Cite this article
Xing,H. (2023). Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field. Applied and Computational Engineering,10,55-63.
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