Autonomous UAV�Quadruped Docking in Complex Terrains Via Active Posture Alignment and Constraint-Aware Control
Haozhe Xu, Cheng Cheng, Hongrui Sang, Zhipeng Wang, Qiyong He, Xiuxian Li, Bin He
AI summary
Problem
Existing docking systems target wheeled robots limited to flat terrain, while quadruped platforms exhibit unstable torso postures that prevent safe UAV landing in complex, GPS-denied environments.
Approach
The method combines a deep reinforcement learning policy that actively flattens the quadruped's torso during docking with a three-phase UAV controller that uses vision-based detection and constraint-aware sliding mode control to safely track and land on the moving platform.
Key results
- HIM-HA reinforcement learning policy actively stabilizes quadruped torso posture for landing
- Three-phase UAV docking strategy with FOV-constrained NFTSMC-BF controller
- Real-world validation achieving successful docking on 17 cm stairs and 30-degree slopes
- First demonstration of autonomous UAV-quadruped docking in unstructured 3D terrains
Why it matters
Enables robust aerial-ground collaboration for search, rescue, and exploration in GPS-denied, rugged environments where wheeled platforms cannot operate.
Abstract
Autonomous docking between Unmanned Aerial Vehicles (UAVs) and ground robots is essential for heteroge- neous systems, yet most existing approaches target wheeled plat- forms whose limited mobility constrains exploration in complex terrains. Quadruped robots offer superior adaptability but un- dergo frequent posture variations, making it difficult to provide a stable landing surface for UAVs. To address these challenges, we propose an autonomous UAV–quadruped docking frame- work 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 UAV 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 Terminal 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. The proposed framework is validated in both simulation and real-world scenarios, successfully achieving docking on outdoor staircases higher than 17 cm and rough slopes steeper than 30 degrees. Supplementary materials and videos are available at: https://uav-quadruped-docking.github.io.