Abstract
This paper proposes a cooperative path planning algorithm for multiple UAVs based on aquila optimizer to meet the autonomous capability requirements of Close Air Support mission. The algorithm considers both the performance of individual trajectories in UAV path planning and the performance of space-time cooperation among multiple UAVs. We transform the cooperative path planning problem into an optimization problem and solve the multi-UAV trajectory with the features of multiple iterative methods of Aquila optimizer. According to the simulation results, the UAV trajectory generated by the algorithm can avoid enemy threats and terrain threats, maintain communication distance between team members, with no risk of crash, and can meet the requirements of reliability, safety, and environmental adaptability of UAVs in CAS missions with good performance.
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
Similar content being viewed by others
References
Huang, H., Luo, C., Han, B.: Prescribed performance fuzzy back-step** control of a flexible air-breathing hypersonic vehicle subject to input constraints. J. Intell. Manuf. 33, 853–866 (2020). https://doi.org/10.1007/s10845-020-01656-0
Ran, M., Li, J., **e, L.: A new extended state observer for uncertain nonlinear systems. Automatica 131, 1–10 (2021)
Zhao, X., Wang, Z., Huang, C., et al.: Mobile robot path planning based on an improved A* algorithm. Robot 40(6), 903–910 (2018)
Ye, Y., Wei, F., Cai, X.: Research on fast Dijkstra algorithm based on parallel computing. Comput. Eng. Appl. 56(6), 58–65 (2020)
Cheng, H., Chen, P., Shi, L., et al.: Fault detection and optimization for flight vehicles via deep reinforcement learning. In: 16th International Conference on Control & Automation (ICCA). IEEE, Hokkaido (2020)
Na, Y., Xu, J., Yan, L., et al.: Task optimization and scheduling of distributed cyber-physical system based on improved ant colony algorithm. Futur. Gener. Comput. Syst. 109, 134–148 (2020)
Tharwat, A., Elhoseny, M., Hassanien, A., et al.: Intelligent Bezier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm. Clust. Comput. 22(2), S4745–S4766 (2019)
Abualigah, L., Yousri, D., Abd Elaziz, M., et al.: Aquila Optimizer: a novel meta-heuristic optimization algorithm. Comput. Ind. Eng. 157, 107250 (2021)
Ran, M., **e, L.: Adaptive observation-based efficient reinforcement learning for uncertain systems. IEEE Trans. Neural Netw. Learn. Syst. 33, 5492–5503 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Bei**g HIWING Sci. and Tech. Info Inst
About this paper
Cite this paper
Huang, H., Li, H., Wang, M., Wu, Y., He, X. (2023). Multi-UAV Cooperative Path Planning Based on Aquila Optimizer. 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_186
Download citation
DOI: https://doi.org/10.1007/978-981-99-0479-2_186
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)