AUV Terrain-Following Method Based on B-Spline Planning and Meta-Learning LTV-MPC

To address the difficulty of simultaneously satisfying tracking accuracy, bottom-clearance safety and actuator constraints for autonomous underwater vehicle(AUV) navigating close to complex seabed terrain, this paper proposes a terrain-following method based on sliding-window B-spline planning and meta-learning-based adaptive linear time-varying model predictive control(LTV-MPC). The planning layer employs cubic B-spline under slope constraints to generate a reference path that maintains a prescribed altitude margin, combined with a sliding-window mechanism to achieve a balance between local optimization and global continuity. The control layer implements LTV-MPC with a meta-learning network that adaptively adjusts cost weights based on terrain curvature, depth and pitch errors to adapt to varying tracking difficulty under different terrain conditions. The meta-network parameters are trained offline via Bayesian optimization on closed-loop simulation data from multiple synthetic terrains, learning the mapping relationship between terrain features and optimal weights, requiring only forward inference online with high computational efficiency. In the specified simulation scenarios, validations on synthetic and real seabed terrain data demonstrate that, compared with LOS-PID, the proposed method reduces root mean square(RMS) tracking error by 42.5% and 31.3% under the two terrain scenarios respectively, compared with standard LTV-MPC, RMS tracking error is reduced by 4.5% and 6.1% respectively, while satisfying pitch angle and rudder angle constraints, effectively solving the balance problem between tracking accuracy and safety under complex terrain conditions.