Research on many-to-many target assignment for unmanned aerial vehicle swarm in three-dimensional scenarios. (May 2021)

• Record Type: Journal Article
• Authors: Hua, Xiang; Wang, Zhao; Yao, Hongjuan; Li, Baohua; Shi, Chenglong; Zuo, Jiaxian
• Is Part Of: Computers & electrical engineering. Volume 91(2021)
• Publication Date: 2021-05
• Subjects: Unmanned aerial vehicle -- Target assignment -- Swarm intelligence optimization algorithm -- Three-dimensional scenario; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107067
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The model effectively solves the multi-target assignment of swarm in three-dimension. The dual objective function describes the comprehensive effect of system performance. The multi-population strategy improves the efficiency of the assignment algorithm. The optimization algorithm outperforms other methods in stability and convergence. Abstract: Target assignment for unmanned aerial vehicle (UAV) swarm has been a research hotspot in academic and industry communities. The current methods mainly focus on multiple targets assignment in planes or few targets assignment in solids. However, they ignore three-dimensional scenarios for UAV swarm and multiple targets characteristics for assignment problem. To solve these issues, we propose a multi-target intelligent assignment model. Firstly, we introduce damage cost and time cost to evaluate the system performance of swarm and time performance in three-dimensional scenarios. Then, we design a bio-inspired swarm intelligence optimization algorithm to find the optimal multiple targets assignment and to balance the two costs and multiple constraints simultaneously. This algorithm regards UAVs as several parallel biological sub-populations, which adopts the multi-layered optimization strategy to select the suitable assignment sequence. The simulation results demonstrate that the proposed method is effective for multi-target assignment in three-dimensional scenarios. Graphical abstract: Image, graphical abstract