Dynamic docking algorithm for UGV to UAV based on single planning under disturbed conditions

This paper investigates the initial dynamic docking problem to mobile and trajectory-disturbed targets for tracking and recovering drones by Unmanned Ground Vehicles (UGVs). First, the target status is estimated by employing the Extended Kalman Filter (EKF). Then, the drone's perturbation is mapped to a dynamic docking point, quantifying the target motion deviation. Within this point frame, an initial path planning algorithm based on Bezier curve with polar offset points is designed. By integrating the initial path and real-time target states into the control layer, a docking control algorithm within single planning is developed. This algorithm enables the UGV to dynamically dock to a trajectory-disturbed drone in position and angle, while also achieving target tracking after simplification. Finally, simulations and experiments have been conducted to demonstrate its effectiveness.