Multi-UAV Dynamic Communication Coverage Algorithm with Low Interception Probability

Unmanned Aerial Vehicles (UAVs) are widely used as communication relays for ground search units in complex and remote areas. However, the avoidance of risky areas, such as no-fly zones, and the evasion of radar interception remain critical challenges. A Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3)-based dynamic UAV deployment algorithm for multi-UAV-assisted communication coverage with low interception risk (MATD3-RC2) is proposed. A centralized training and decentralized execution framework is adopted, eliminating the need for global environmental awareness. The communication coverage problem is formulated as a multi-objective optimization task, aiming to maximize coverage effectiveness, minimize radar interception risk, and ensure robust UAV connectivity. The simulation results demonstrate that MATD3-RC2 outperforms both the Multi-Agent Deep Reinforcement Learning-based energy-efficient control (MADRL-E) and the Distributed Virtual Force Motion Control (DVFMC) algorithms in terms of the target performance metrics.