Xiangju Jiang1 and Ye Deng This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China


 

Received: July 3, 2020
Accepted: September 29, 2020
Publication Date: April 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202104_24(2).0001  


ABSTRACT


The actual flying environment of quadrotor unmanned aircraft vehicle (UAV) is three-dimensional space, so this paper extends the traditional artificial potential field (APF) method from two-dimensional space to three dimensional space. According to the influence range of obstacle on UAV, the repulsive function is modified, and the repulsive forces exerted by obstacles on UAV at different distances is divided. Then, the improved algorithm is compared with other improved artificial potential field methods in the same simulation environment. Finally, the proposed algorithm is applied in the environment of electric tower, the idea of traveling salesman problem (TSP) is used for reference, and sub target points are set in the simulation environment to help UAV better complete the inspection tasks. The results of two simulation experiments show that the improved algorithm overcomes the shortcomings of the traditional algorithm, and the generated track is shorter and more feasible.


Keywords: Artificial Potential Field Method; Repulsive Function; Track Planning; Electric Tower Pole Inspection


REFERENCES


  1. [1] Hu, Z. H., M. Zhao, M. Yao, K. X. Li and R. Wu. An Improved Ant Algorithm for Multi-target 3D Trajectory Planning of UAV. Journal of Shenyang University of technology, 5:93–98, 2011.
  2. [2] Y Ji, C Dong, X Zhu, and Q Wu. Fair-energy trajectory planning for multi-target positioning based on cooperative unmanned aerial vehicles. IEEE Access, 8:9782–9795, 2020.
  3. [3] H Lee, H Kim, and HJ Kim. Planning and control for collision-free cooperative aerial transportation. IEEE Transactions on Automation Science and Engineering, 15(1):189–201, 2018.
  4. [4] Z Zhou, J Wang, Z Zhu, D Yang, and J Wu. Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik, 158:639–651, 2018.
  5. [5] A Lazarowska. Discrete Artificial Potential Field Approach to Mobile Robot Path Planning. IFACPapersOnLine, 52(8):334–337, 2019.
  6. [6] Zhang, S., X. R. Li, J. Y. Zhang, P. Zhang, B. Li, and X. L. Zhao. 3D real-time path planning of UAV Based on dynamic step length. Journal of Beijing University of Aeronautics and Astronautics, 42(12):2745–2754, 2016.
  7. [7] Liu, Q. X., J. Wang, C. X. Gao, Y. Song, C. Gao, G. Q. Hao, P. L. Li, and L. Zhu. 3D path planning method of UAV Based on guidance point. Journal of Beijing University of technology, 11:67–72, 2014.
  8. [8] Zhang, Y., J. Chen, and L. C. Shen. 3D trajectory planning technology of UCAV air to surface multi-target attack. Journal of National University of Defense Science and technology, 34(5):108–114, 2012.
  9. [9] Yu, Z. Z., J. H. Yan, J. Zhao, Z. P. Chen, and Y. H. Zhu. Path planning of mobile robot based on improved artificial potential field method. Journal of Harbin University of technology, 43(1):50–55, 2011.
  10. [10] Luo, L. F., D. Li, and H. Zhong. Route planning of UAV electric tower inspection based on improved RRT. Journal of Hunan University (NATURAL SCIENCE EDITION), 45(10):85–91, 2018.
  11. [11] Liu, C. A., G. T. Yang, H. Wu, and H. Zhou. Three dimensional trajectory generation method of electric line inspection flying robot. Journal of Tongji University (NATURAL SCIENCE EDITION), 38(12):1822–1827, 2010.
  12. [12] Manh Duong Phung, Hoang Quach, Tran Hiep Dinh, and Quang Ha. Enhanced Discrete Particle Swarm Optimization Path Planning for UAV Vision-based Surface Inspection. Automation in Construction, 81:25–33, 2017.
  13. [13] Ding, J. R., C. P. Du, Y. Zhao, and D. Y. Yin. UAV path planning algorithm based on improved artificial potential field method. Computer application, 1:287–290, 2016.
  14. [14] Cheng, H. H, S. Yang and X. H. Qi. Path planning method of quadrotor UAV online obstacle avoidance for urban environment. Computer science, 46(04):241– 246, 2019.
  15. [15] J Sun, J Tang, and S Lao. Collision avoidance for cooperative UAVs with optimized artificial potential field algorithm. IEEE Access, 5:18382–18390, 2017.


Latest Articles

    
 

0.6
2019CiteScore
 
 
27th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.