Autonomous UAV Formation Adaptation Based on Link Quality Monitoring and Drift Detection

Unmanned Aerial Vehicles enable a wide range of applications, including search and rescue, environmental monitoring, and disaster response. These aerial platforms can form dynamic flying ad hoc networks to support both sensing and data communication. In particular, UAVs can establish a resilient communication backbone that adapts to varying propagation conditions and fluctuating traffic demands. However, maintaining reliable performance in such highly dynamic and unpredictable environments remains a critical challenge. This work investigates online link quality estimation by autonomous agents, with a focus on real-time detection of link model changes (e.g., due to mobility or interference) through model drift. Building on this, we propose and evaluate adaptive formation control strategies that adjust UAV placement to optimize the network’s Packet Delivery Ratio. Simulation results demonstrate that the proposed optimal placement strategy significantly outperforms baseline approaches for line-based UAV networks.