Multi-UAV Trajectory Optimization Under Dynamic Threats: An Enhanced GWO Algorithm Integrating a Priori and Real-Time Data

Abstract

Though the widespread use of multi-UAV systems offers significant tactical and operational advantages, achieving efficient and secure collaborative planning remains a critical challenge in dynamic threat environments. Traditional methods struggle to balance path optimization with threat avoidance, particularly in fluctuating environments where UAVs must adapt to changing threats. To address this, an enhanced Grey Wolf Optimization (GWO) algorithm is proposed for multi-UAV collaborative planning in dynamic threat zones. Our research integrates a priori knowledge of threat zone locations, speeds, and directions with real-time data on the UAVs position relative to the threat zones to effectively manage dynamic threat zones, allowing UAVs to dynamically decide whether to navigate around or through these zones, thus significantly reducing trajectory costs. To further improve search efficiency and solution quality, strategies such as greedy initialization and K-means clustering are incorporated, enhancing the algorithms multi-objective optimization capabilities. Experimental results demonstrate that the dynamic threat zone crossing strategy significantly reduces trajectory costs compared to the traditional bypass strategy. Furthermore, the enhanced GWO algorithm outperforms both the traditional GWO and MP-GWO algorithms in terms of trajectory cost and convergence accuracy. Our approach provides novel insights and methodologies for the advancement of multi-UAV collaborative trajectory planning, while extending the applicability of the GWO algorithm in complex environments