Learning Resilient Formation Control of Drones With Graph Attention Network

Jiaping Xiao, Xu Fang, Qianlei Jia, Mir Feroskhan

Abstract: Multidrone systems offer notable advantages in various missions, such as search and rescue, environmental surveillance, and industrial inspection, providing enhanced efficiency and redundancy over single-drone operations. However, ensuring resilient multidrone formation in dynamic and adversarial environments, such as during communication loss or cyberattacks, remains a significant challenge. Traditional approaches often struggle with complex modeling requirements and scalability issues. Among them, leader-follower methods rely heavily on predefined hierarchies, making them vulnerable to single-point failures, while distributed methods incur high communication costs and lack efficient mechanisms to dynamically adapt to changing environments. This article proposes a novel learning-based formation control method to enhance the scalability and resilience of multidrone formations. First, a graph attention network (GAT) is leveraged to dynamically model interagent relationships and prioritize critical interactions among variable neighbors via attention mechanisms with bounded communication overhead. Second, a dual-mode control strategy is designed, integrating leader-follower and distributed control approaches to optimize communication costs while maintaining formation performance. Third, deep reinforcement learning is utilized to train the GAT-based controller, achieving objectives, such as maintaining formation tightness, avoiding collisions, and ensuring resilience against Denial-of-Service (DoS) attacks. Extensive simulations demonstrate superior performance of our method over baseline controllers under normal and adversarial conditions. Furthermore, real-world flight experiments validate the effectiveness and generalizability of the trained policy.

External IDs:doi:10.1109/jiot.2025.3554098