Autonomous UAV-Quadruped Docking in Complex Terrains via Active Posture Alignment and Constraint-Aware Control

Abstract-- Autonomous docking between Unmanned Aerial V ehicles (UA Vs) and ground robots is essential for heterogeneous systems, yet most existing approaches target wheeled platforms whose limited mobility constrains exploration in complex terrains. Quadruped robots offer superior adaptability but undergo frequent posture variations, making it difficult to provide a stable landing surface for UA Vs. T o address these challenges, we propose an autonomous UA V-quadruped docking framework for GPS-denied environments. On the quadruped side, a Hybrid Internal Model with Horizontal Alignment (HIM-HA), learned via deep reinforcement learning, actively stabilizes the torso to provide a level platform. On the UA V side, a three-phase strategy is adopted, consisting of long-range acquisition with a median-filtered YOLOv8 detector, close-range tracking with a constraint-aware controller that integrates a Nonsingular Fast T erminal Sliding Mode Controller (NFTSMC) and a logarithmic Barrier Function (BF) to guarantee finite-time error convergence under field-of-view (FOV) constraints, and terminal descent guided by a Safety Period (SP) mechanism that jointly verifies tracking accuracy and platform stability. I. INTRODUCTION Heterogeneous cooperative systems that integrate Unmanned Aerial V ehicles (UA Vs) and Unmanned Ground V ehicles (UGVs) can expand operational scope and improve efficiency compared to single-domain platforms [1]. Autonomous docking is a key capability for many UA V-UGV collaborative tasks, yet most existing schemes focus on wheeled UGVs, whose mobility is restricted to flat terrain, limiting exploration in complex environments. Moreover, dynamic docking requires UA Vs to achieve precise localization and safe landing on moving platforms, imposing high demands on sensor fusion and robust control [2]. Quadruped robots, with their legged morphology, surpass wheeled and tracked UGVs in unstructured terrains and enable UA V collaboration in challenging environments such as mountains or tunnels.