Abstract
In this paper, a 4D trajectory planning problem of a multiple unmanned combat aerial vehicle (multi-UCAV) cooperative formation based on given 4D navigation points is studied. The three degrees of freedom model of the unmanned combat aerial vehicle (UCAV) will be established first. Then, the development of current tau theory is introduced and an improved tau-j guidance strategy is proposed. The improved tau-j guidance strategy addresses the discontinuity of velocity and acceleration at the navigation point in the original tau-j theory. The speed and acceleration of the generated four-dimensional trajectory are controlled to meet the flight requirements of the UCAV. Finally, a cost function considering the length of the flight path, flight time, collision avoidance, maximum acceleration, minimum and maximum speed of UCAV is proposed. The Particle Swarm Optimization (PSO) algorithm is used to optimize the four-dimensional trajectory of the UCAV. The results of the mathematical simulation show that the four-dimensional trajectory planning algorithm of the multi-UCAV cooperative formation meets the requirements of the constraints, which proves the effectiveness of the trajectory planning algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LI Meng, et al., Aircraft trajectory optimization model under trajectory based on operation, Journal of Railway Science and Engineering, 1–10 (2021)
Guanghui, M.A., et al.: A 4D trajectory planning method based on historical radar tracks of air traffic. J. Transp. Inf. Safety 33(4), 106–112 (2015)
Murrieta-Mendoza, A., et al.: Four- and Three-dimensional aircraft reference trajectory optimization inspired by ant colony optimization. J. Aerosp. Inf. Syst. 14(11), 597–616 (2017)
Perez-Leon, H., Acevedo, J.J., Maza, I., Ollero, A.: Integration of a 4D-trajectory follower to improve Multi-UAV conflict management within the U-space context. J. Intell. Rob. Syst. 102(3), 1–13 (2021). https://doi.org/10.1007/s10846-021-01415-0
Zhao, W., et al.: Four-dimensional trajectory generation for UAVs based on multi-agent Q learning. J. Navig. 73(4), 874–891 (2020)
Yang, Z., Fang, Z., Li, P.: Decentralized 4D trajectory generation for UAVs based on improved intrinsic tau guidance strategy. Int. J. Adv. Robot. Syst. 13(3), 88 (2016). https://doi.org/10.5772/63431
Lee, D.N.: A theory of visual control of braking based on information about time-to-collision. Perception 5(4), 437–459 (1976)
Zhang, Z., Zhang, S., Xie, P., Ma, O.: Bioinspired 4D trajectory generation for a UAS rapid point-to-point movement. J. Bionic Eng. 11(1), 72–81 (2014). https://doi.org/10.1016/S1672-6529(14)60021-4
Qiang, D., et al.: Improved Tau-H strategy for four-dimensional cooperative route planning of multi-UAVs. J. Zhejiang Univ. (Engineering Science) 52 (7), 1398–1405+1422 (2018)
Acknowledgement
The author acknowledges funding received from the following science foundations: National Natural Science Foundation of China (No. 62176214, 61973253, 62101590), Natural Science Foundation of the Shaanxi Province, China (2021JQ-368).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Beijing HIWING Sci. and Tech. Info Inst
About this paper
Cite this paper
Wei, J., Fan, H., Wang, Q., Zhang, J., Yang, J. (2023). Research on Multi-UCAV Four-Dimensional Trajectory Planning Algorithm Based on Improved tau-J Guidance Strategy. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_247
Download citation
DOI: https://doi.org/10.1007/978-981-99-0479-2_247
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0478-5
Online ISBN: 978-981-99-0479-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)