Optimization of Net Deployment Performance in Two-Stage Ejection for UAV Capture

The warfare application of “low, slow, small” unmanned aerial vehicles (UAVs) has posed a significant threat to certain countries and regions. As a novel countermeasure, flexible nets have gradually attracted considerable research attention. However, a challenge encountered during the two-stage ejection of the net is the reduction in deployment area. To address this issue, this study employs the mass-spring method to establish a two-stage deployment model for the net and validates the correctness of the simulation model through ground deployment experiment. Without loss of generality, a square-shaped net is used as an example, where the mesh configuration and the mass ratio between the bullets and the net are taken as optimization variables. The maximum deployment area of the net is set as the optimization objective. And then the optimization model is established based on the genetic algorithm. Numerical simulations show that the optimized net performs better in adapting to two-stage ejection scenario. Its maximum deployment area increases by 20%. The research findings provide valuable insights for the successful implementation of net-based UAVs capture missions.