Bio-inspired neighbor selection mechanism and performance evaluation for UAV Swarm: An extended visual-attention model
Jun Zhang, Liang Zhang · IFAC-PapersOnLine · 2025
Multi-unmanned aerial vehicle (UAV) swarms exhibit significant potential in collaborative tasks but face challenges in communication efficiency and neighbor selection, particularly in complex environments where global coordination is impractical. Inspired by biological flocking behaviors, this paper proposes an extended visual attention mechanism-based model for UAV swarm control, optimizing neighbor selection and maintaining balanced formations. By integrating bio-inspired communication strategies and evolutionary algorithms, our method reduces resource consumption while achieving robust swarm cooperation. Simulation results demonstrate that the proposed approach significantly improves energy efficiency, neighbor management, and formation stability, enabling effective task completion with minimized communication overhead. This study offers new insights into scalable and adaptive cooperative control for UAV swarms in diverse and dynamic environments.