Recover a moving rotor UAV without ground–air communications: System and control of a dual-stage tracking device

Recovering a moving rotor unmanned aerial vehicle (UAV) using a single-stage dynamic tracking device poses a significant challenge, particularly without real-time communication between the two systems. This study presents a dual-stage tracking system comprising an unmanned ground vehicle (UGV) and a Stewart platform, aimed at dynamically tracking and recovering the UAV. Firstly, an observation algorithm combining Kalman filtering (KF) and curve fitting is designed to estimate and complete the drone's states and predict its trajectory. Subsequently, a decoupled dual-stage tracking control structure is introduced, integrating two independent controlled subsystems. Specifically, in the UGV controller, the model predictive control (MPC) is employed to enhance dynamic tracking capabilities using absolute kinematics. A motion tracking algorithm based on relative kinematics was developed for the Stewart recovery platform to compensate for UGV tracking errors and improve tracking accuracy. Dynamic recovery simulations and experiments have been conducted to validate the feasibility and effectiveness of the proposed dual-stage tracking system. The results demonstrate the system's capability to dynamically track and recover the drone without real-time communication in complex environments characterized by detection noise and target trajectory disturbances.