Fusing Adaptive Game Theory and Deep Reinforcement Learning for Multi-UAV Swarm Navigation

To address issues such as inadequate robustness in dynamic obstacle avoidance, instability in formation morphology, severe resource conflicts in multi-task scenarios, and challenges in global path planning optimization for unmanned aerial vehicles (UAVs) operating in complex airspace environments, this paper examines the advantages and limitations of conventional UAV formation cooperative control theories. A multi-UAV cooperative control strategy is proposed, integrating adaptive game theory and deep reinforcement learning within a unified framework. By employing a three-layer information fusion architecture—comprising the physical layer, intent layer, and game-theoretic layer—the approach establishes models for multi-modal perception fusion, game-theoretic threat assessment, and dynamic aggregation-reconstruction. This optimizes obstacle avoidance algorithms, facilitates interaction and task coupling among formation members, and significantly improves the intelligence, resilience, and coordination of formation-wide cooperative control. The proposed solution effectively addresses the challenges associated with cooperative control of UAV formations in complex traffic environments.