Name
Abstract | ||
|---|---|---|
With the rapid development of artificial intelligence technology, the multi-UAV cooperative search has wide applications in the field of Internet of Things, such as resource exploration, emergency rescue, intelligent transportation, etc. However, the communication network in an unknown environment may be inaccessible, and the real-time information sharing among UAVs cannot be guaranteed, resulting in the failure of cooperative search. Aiming at this issue, this article is devoted to the design of the multi-UAV flight strategy to improve the cooperative search capability in an uncertain communication environment. Specifically, a new cooperative architecture oriented to a local communication network is devised to control the observation locations of multiple UAVs in the search process, and some local communication networks are established based on the distance among UAVs to meet the requirements of the search task. On this foundation, we develop a multi-UAV cooperative search model (MCSM) with communication cost and formation benefit as an optimization function to ensure the effectiveness of multi-UAV search. Moreover, in the process of model solving, an improved sparrow search algorithm (ISSA) is presented with some different search strategies to enhance the optimization capability. To verify the superiority of the proposed method, we designed several groups of simulation experiments to analyze the performance of MCSM. Experimental results illustrate that our method can not only maintain high cooperative search accuracy but also has high stability and convergence speed. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JIOT.2022.3165278 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Communication cost,formation benefit,improved sparrow search algorithm (ISSA),local communication network,multi-UAV cooperative search | Journal | 9 |
Issue | ISSN | Citations |
19 | 2327-4662 | 0 |
PageRank | References | Authors |
0.34 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bowen Fei | 1 | 0 | 1.01 |
| Weidong Bao | 2 | 23 | 6.49 |
| Xiaomin Zhu | 3 | 0 | 0.34 |
| Daqian Liu | 4 | 0 | 1.01 |
| Tong Men | 5 | 0 | 0.68 |
| Zhenliang Xiao | 6 | 0 | 0.34 |