New Explorations in Autonomous Low-Altitude Area Defense Decision-Making and Control Methods of UAV Swarms

This paper explores recent advances in decision-making and control methods for autonomous Unmanned Aerial Vehicle (UAV) swarm-based defense in low-altitude airspace. The study categorizes existing approaches into differential game theory, multi-agent reinforcement learning (MARL), and bio-inspired swarm intelligence. While traditional differential games struggle with scalability and MARL approaches face stability and interpretability issues, bio-inspired frameworks offer promising adaptability by mimicking natural swarm defense behaviors. The paper highlights critical research challenges, including mapping biological behaviors to UAV rules, modeling maneuverability with environmental constraints, and inferring intent for mission planning. By integrating these elements, the work advocates for a unified framework that ensures real-time adaptability and formal control guarantees. This approach is particularly suited to many-vs-many defense scenarios where existing methods fall short, aiming to fill a critical gap in UAV swarm defense strategies under complex and dynamic conditions.