Learning Resilient Formation Control of Drones with Graph Attention Network

TL;DR

This paper introduces a learning-based formation control method for drones using graph attention networks, significantly enhancing robustness and resilience in adversarial and dynamic environments through adaptive internode relationship modeling.

Contribution

The paper presents a novel GAT-based formation control approach that improves drone system resilience and adaptability under communication failures and cyberattacks.

Findings

Enhanced robustness against DoS attacks

Superior formation performance in simulations

Validated effectiveness in real-world drone flights

Abstract

The rapid advancement of drone technology has significantly impacted various sectors, including search and rescue, environmental surveillance, and industrial inspection. Multidrone systems offer notable advantages such as enhanced efficiency, scalability, and redundancy over single-drone operations. Despite these benefits, ensuring resilient formation control in dynamic and adversarial environments, such as under communication loss or cyberattacks, remains a significant challenge. Classical approaches to resilient formation control, while effective in certain scenarios, often struggle with complex modeling and the curse of dimensionality, particularly as the number of agents increases. This paper proposes a novel, learning-based formation control for enhancing the adaptability and resilience of multidrone formations using graph attention networks (GATs). By leveraging GAT's dynamic capabilities to extract internode relationships based on the attention mechanism, this GAT-based formation controller significantly improves the robustness of drone formations against various threats, such as Denial of Service (DoS) attacks. Our approach not only improves formation performance in normal conditions but also ensures the resilience of multidrone systems in variable and adversarial environments. Extensive simulation results demonstrate the superior performance of our method over baseline formation controllers. Furthermore, the physical experiments validate the effectiveness of the trained control policy in real-world flights.