Influence Mechanism and Evaluation of Communication Models for Unmanned Swarm Cooperation

To solve the problem of performance degradation of unmanned aerial vehicle (UAV) swarm cooperative control under practical communication constraints, we deeply investigate the influence mechanism and performance differences of communication models on swarm intelligence algorithms. By constructing a high-fidelity communication simulation environment that integrates a complete network protocol stack, a communication performance model capable of accurately characterizing the distance-dependent dynamic characteristics of time delay and packet loss is established. Based on this model, the performance of five typical swarm intelligence algorithms under different communication qualities, swarm sizes, and environmental complexities is systematically analyzed, and a quantitative evaluation index system covering multiple dimensions such as connectivity, convergence, and task execution performance is established. The results reveal the internal mechanism of swarm cooperative behavior failure caused by time delay and packet loss, namely, communication constraints lead to decision information delay and neighborhood information loss, and different algorithms show significant differences in sensitivity to these constraints. This study deepens the understanding of the coupling effects between communication and control, and provides a reference for algorithm selection in practical engineering applications based on the quantitative evaluation results.