High-Precision Landing on a Moving Platform Based on Drone Vision Using YOLO Algorithm

High-precision landing is a key technical problem that Unmanned Aerial Vehicles (UAVs) will encounter in all application fields, especially for the landing of moving targets. This paper focuses on developing a landing system designed to achieve real-time precise navigation by integrating the Global Navigation Satellite System (GNSS) with the quadcopter’s vision data. To overcome the challenge of the flight altitude being too high to detect the landing target, this paper first detects large-volume targets, followed by the precise identification of smaller targets, achieving enhanced recognition accuracy and speed through an improved YOLOv8 OBB algorithm. To maintain the UAV’s safety and stability throughout the landing process, this paper applies a position control approach using a reference model-based sliding mode controller (RMSMC). The quadcopter’s position is then controlled by the RMSMC throughout the entire landing procedure. The reference value of each state is determined by the reference model, which improves the stability and safety of the entire position control system. During the final experiment, the results demonstrate that the enhanced YOLOv8 OBB identification model increases the mAP0.5:0.95 index for landing target point detection by 2.22 percentage points compared to the original YOLOv8 OBB model, running at 53 FPS on Nvidia AGX. Through multiple actual flights, the proposed landing system consistently achieves an average position error of just 0.07 m.