Precision Docking of a Foldable Quadrotor on a Wheel-Legged Robot via CFNTSM with GFA-FEO and FiLM-SAC Deep Reinforcement Learning

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot (300×180 mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at 2.5 Hz, (iii) continuous platform translation at 0.3–0.8 m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight λ(t); and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3∘ attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm).