Electrical Engineering

Xiangju Jiang This email address is being protected from spambots. You need JavaScript enabled to view it. and Bingde Huang

School of automation and electrical engineering, Lanzhou Jiaotong University, Lanzhou, 730070

 

Received: June 20, 2022
Accepted: November 3, 2022
Publication Date: February 9, 2023

 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.202310_26(10).0009  


ABSTRACT

Aiming at the problems that RRT∗ algorithm has poor directionality in path planning and the path is not sufficient for UAV flight, an improved RRT∗ algorithm is proposed by analyzing the kinematics model of fixed-wing UAV. Firstly, the sampling function is introduced to make the random tree grow towards the target point, which improves the efficiency of path search. Secondly, the expansion of algorithm nodes is constrained according to the flight dynamics of fixed-wing UAV, and then b-spline curve is used to optimize the path suitable for UAV flight. Finally, the feasibility of the algorithm is verified in two-dimensional and three-dimensional environments. The simulation results show that the improved RRT∗ algorithm greatly reduces the time cost of path planning and is a fast and effective global path planning algorithm.


Keywords: Fixed-wing UAV; RRT∗ algorithm; Path planning; B-spline curve


REFERENCES

  1. [1] Z. Zhang, T. Shen, X. L. Tang, F. P. Yu, and R. M. Zhang, (2022) “Summary of research on penetration path planning method of combat aircraft" Aviation Weapon: 1–11. DOI: http://kns.cnki.net/kcms/detail/41.1228.TJ.20220224.1654.001.html.
  2. [2] L. Chen, H. Jiang, and Y. J. Zheng, (2019) “Summary of Rapidly-Exploring Random Tree Algorithm in Robot Path Planning" Computer Engineering and Application 55(16): 10–17. DOI: 10.3778/j.issn.1002-8331.1905-0061.
  3. [3] C.Wang, H. L. Dong, X. P. Gu, J. H. Li, and J. L. Chen, (2019) “Improved particle swarm optimization algorithm and its application in track planning" Control Engineering 26(08): 1466–1471. DOI: 10.14107/j.cnki.kzgc.171049.
  4. [4] D. Zhang, Y. Xian, J. Li, G. Lei, and Y. Chang. “UAV Path Planning Based on Chaos Ant Colony Algorithm”. In: Cited by: 26. 2015, 81–85. DOI: 10.1109/CSMA.2015.23.
  5. [5] X. Wang and X. Meng. “UAV online path planning based on improved genetic algorithm”. In: 2019-July. Cited by: 1. 2019, 4101–4106. DOI: 10.23919/ChiCC.2019.8866205.
  6. [6] S. Wang, T. Long, Z. Wang, and Q. Cai, (2018) “Dynamic path planning using anytime repairing sparse A∗ algorithm" Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 40(12): 2714–2721. DOI: 10.3969/j.issn.1001-506X.2018.12.14.
  7. [7] P. K. Selvam, G. Raja, V. Rajagopal, K. Dev, and S. Knorr. “Collision-free Path Planning for UAVs using Efficient Artificial Potential Field Algorithm”. In: 2021-April. Cited by: 12. 2021. DOI: 10.1109/VTC2021-Spring51267.2021.9448937.
  8. [8] X. Jiang and Y. Deng, (2021) “UAV track planning of electric tower pole inspection based on improved artificial potential field method" Journal of Applied Science and Engineering (Taiwan) 24(2): 123–132. DOI: 10.6180/jase.202104_24(2).0001.
  9. [9] S. Gao, J. L. Ai, and Z. H.Wang, (2020) “Mixed population RRT algorithm for UAV path planning" Systems Engineering and Electronic Technology 42(01): 101–107. DOI: 10.3969/j.issn.1001-506X.2020.01.14.
  10. [10] G. Y. Yin, S. L. Zhou, and Q. P. Wu, (2017) “UAV track planning based on improved RRT algorithm" ACTA ELECTRONICA SINICA 45(7): 1764–1769. DOI: 10.3969/j.issn.0372-2112.2017.07.029.
  11. [11] D. Lee, H. Song, and D. H. Shim. “Optimal path planning based on spline-RRT∗ for fixed-wing UAVs operating in three-dimensional environments”. In: Cited by: 20. 2014, 835–839. DOI: 10.1109/ICCAS.2014.6987895.
  12. [12] J. Ge, L. Liu, X. Dong, andW. Tian. “Trajectory Planning of Fixed-wing UAV Using Kinodynamic RRTAlgorithm”. In: Cited by: 2. 2020, 44–49. DOI: 10.1109/ICIST49303.2020.9202213.
  13. [13] Y. F. Huang, L. K. Hu, and W. C. Xue, (2021) “Path planning of mobile robot based on improved RRT - connect algorithm" Computer Engineering 47(8): 22–28. DOI: 10.19678/j.issn.1000-3428.0060192.
  14. [14] S. Karaman and E. Frazzoli. “Optimal kinodynamic motion planning using incremental sampling-based methods”. In: Cited by: 273; All Open Access, Green Open Access. 2010, 7681–7687. DOI: 10.1109/CDC.2010.5717430.

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