Optimization Design of Flexible Net Capture System for Low, Slow, and Small Unmanned Aerial Vehicles Based on Improved Multi-Objective Wolf Pack Algorithm - Ran Xu; Qiang Peng; Husheng Wu (Drones)
In response to the increasing safety concerns posed by low, slow, and small unmanned aerial vehicles (UAVs), the use of flexible nets for interception emerges as a promising solution due to its high tolerance, minimal requirements, and cost-effectiveness. To enhance the effectiveness of the flexible net capture system for these types of UAVs, an optimization of the system’s parameters is conducted. A dynamic model of the flexible net capture system is developed, and its deployment process is simulated and analyzed through a combination of ABAQUS 2022/Explicit and MATLAB R2020b software. The coverage rate and hang time are proposed as the key performance indicators for quantitatively assessing the interception capabilities of the rope net. A mathematical model is formulated to optimize the capture system parameters, considering both spatial and temporal tolerances. The Multi-objective Wolf Pack Algorithm, which incorporates an Elite Leadership Strategy and a crowding distance-based population update mechanism, is utilized to optimize the design variables. This approach leads to the derivation of the optimized design parameters for the flexible net. Ultimately, the optimal parameter configuration for the flexible net capture system is achieved through the application of the Multi-objective Wolf Pack Algorithm to the design variables. This optimization ensures the system’s peak performance in intercepting low, slow, and small UAVs.