DAPE-Form: A Decentralized Adaptive Position Estimation and Formation Control for Multi-Drone Systems

Abstract

Cooperative aerial multi-robot systems (MRS) are increasingly being considered for missions in uncertain and dynamic environments, where reliable coordination must be maintained despite sensor failures, external disturbances, and limited onboard computational resources. However, achieving resilient coordination while simultaneously maintaining tracking accuracy and computational efficiency remains challenging. To address these challenges jointly, we develop a hybrid architecture that combines adaptive value tracking (AVT)-based coordination and multilateration for position reconstruction, an extended state observer (ESO) for real-time disturbance estimation and compensation, and event-triggered control (ETC) to reduce unnecessary actuator updates. Theoretical analysis based on Lyapunov stability guarantees the convergence of the estimation and control errors. Simulation and real-world experiments using DJI/Tello quadrotors in a robot operating system (ROS)-based environment demonstrate improved robustness and formation-tracking accuracy relative to conventional periodic control, reducing control update frequency by 30% during trajectory-tracking in simulation and up to 70% during static formation regulation in physical flight tests. The multilateration scheme enables position reconstruction under sensor losses, while the ESO effectively compensates for wind disturbances. Overall, the results demonstrate that the proposed framework enables resilient cooperative operation under sensing failures and environmental disturbances while reducing control-update requirements.