UAV path planning based on optimized artificial potential field method

被引：3
|
作者：
Wang Q. [1 ]
Wu F. [2 ]
Zheng C. [1 ]
Li H. [1 ,2 ]
机构：
[1] School of Computer Science(Software), Sichuan University, Chengdu
[2] National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 05期
关键词：
artificial potential field method; collision predict; path planning; physical constraint; smooth trajectory;
D O I：
10.12305/j.issn.1001-506X.2023.05.22
中图分类号：
学科分类号：
摘要：
Aiming at the problems of local minimum point, excessive repulsion force, and unnecessary obstacle avoidance in the traditional artificial potential field (TAPF) method in unmanned aerial vehicle path planning, an optimized artificial potential field method is proposed. Firstly, the repulsion force is decomposed to avoid the local minimum point. Then, the calculation method of the resultant force is reconstructed to avoid excessive repulsion force when unmanned aerial vehicle in obstacle-intensive area. Finally, the two-time collision predict method is introduced to reduce the unnecessary obstacle avoidance and ensure a smooth trajectory. The path planning experiments are carried out with considering the physical constraints of unmanned aerial vehicle. Compared with the TAPF method, the proposed method not only shortens the length of planning trajectory, but also significantly improves the smoothness of trajectory. © 2023 Chinese Institute of Electronics. All rights reserved.
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收藏
页码：1461 / 1468
页数：7
相关论文
共 30 条
  • [1] SHRAIM H, AWADA A, YOUNESS R., A survey on quadrotors: configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control, IEEE Aerospace and Electronic Systems Magazine, 33, 7, pp. 14-33, (2018)
  • [2] ZHOU X H, YI Z M, LIU Y L, Et al., Survey on path and view planning for UAVs, Virtual Reality & Intelligent Hardware, 2, 1, pp. 56-69, (2020)
  • [3] AGGARWAL S, KUMAR N., Path planning techniques for unmanned aerial vehicles: a review, solutions, and challenges, Computer Communications, 149, pp. 270-299, (2020)
  • [4] SHORAKAEI H, VAHDANI M, IMANI B, Et al., Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm, Robotica, 34, 4, pp. 823-836, (2016)
  • [5] HUANG S Z, TIAN J W, QIAO L, Et al., Unmanned aerial vehicle path planning based on improved genetic algorithm[J], Journal of Computer Applications, 41, 2, pp. 390-397, (2021)
  • [6] MANDLOI D, ARYA R, VERMA A K., Unmanned aerial vehicle path planning based on A* algorithm and its variants in 3D environment, International Journal of System Assurance Engineering and Management, 12, 5, pp. 990-1000, (2021)
  • [7] GURUJI A K, AGARWAL H, PARSEDIYA D K., Time-efficient A* algorithm for robot path planning, Procedia Technology, 23, pp. 144-149, (2016)
  • [8] YILMAZ N K, EVANGELINOS C, LERMUSIAUX P F J, Et al., Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming, IEEE Journal of Oceanic Engineering, 33, 4, pp. 522-537, (2008)
  • [9] FANG Q, XU Q., 3D route planning for UAV based on improved PSO algorithm, Journal of Northwestern Polytechnical University, 35, 1, pp. 66-73, (2017)
  • [10] ZHANG L, ZHANG Y J, LI Y F., Mobile robot path planning based on improved localized particle swarm optimization, IEEE Sensors Journal, 21, 5, pp. 6962-6972, (2020)
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