An Improved Ant Colony Algorithm Based Cluster Control Method for Hybrid Networking of UAV Communication
- DOI
- 10.2991/978-94-6463-108-1_48How to use a DOI?
- Keywords
- Hybrid networking; Improved ant colony algorithm; UAVs; Cluster control methods
- Abstract
The current UAV cluster control method based on multi-path planning achieves the planning of UAV flight routes through situational awareness technology, which leads to a long planning time due to the lack of defense against attack behaviors. In this regard, a cluster control method based on improved ant colony algorithm for UAV communication hybrid network is proposed. The constraint function of the UAV planning path is established, the attack behavior of the attack nodes is analyzed, the detection and processing method of the attack behavior is proposed, and the planning process of the UAV flight path is constructed. In the experiments, the proposed method is verified in terms of search time. The analysis of the experimental results shows that the UAV cluster control technique constructed by the proposed method possesses a low search time.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hanjie Yuan AU - Yong He AU - Yaohua Zheng AU - Limeng Dong AU - Jianan Yao AU - Yu Zhang AU - Lin Lu AU - Qi Tan AU - Tianhang Jiang AU - Haiao Tan PY - 2022 DA - 2022/12/30 TI - An Improved Ant Colony Algorithm Based Cluster Control Method for Hybrid Networking of UAV Communication BT - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022) PB - Atlantis Press SP - 421 EP - 428 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-108-1_48 DO - 10.2991/978-94-6463-108-1_48 ID - Yuan2022 ER -