LLM-Driven Predictive-Adaptive Guidance for Autonomous Surface Vessels Under Environmental Disturbances

Abstract

Advances in AI are accelerating intelligent ship autonomy, yet robust trajectory tracking remains challenging under nonlinear dynamics and persistent environmental disturbances. Traditional model-based guidance becomes tuning-sensitive and loses robustness under strong disturbances, while data-driven approaches like reinforcement learning often suffer from poor generalization to unseen dynamics and brittleness in out-of-distribution conditions. To address these limitations, we propose a guidance architecture embedding a Large Language Model (LLM) directly within the closed-loop control system. Using in-context prompting with a structured Chain-of-Thought (CoT) template, the LLM generates adaptive k-step heading reference sequences conditioned on recent navigation history, without model parameter updates. A latency-aware temporal inference mechanism synchronizes the asynchronous LLM predictions with a downstream Model Predictive Control (MPC) module, ensuring dynamic feasibility and strict actuation constraints. In MMG-based simulations of the KVLCC2, our framework consistently outperforms conventional model-based baselines. Specifically, it demonstrates superior path-keeping accuracy, higher corridor compliance, and faster disturbance recovery, achieving these performance gains while maintaining comparable or reduced rudder usage. These results validate the feasibility of integrating LLMs as predictive components within physical control loops, establishing a foundation for knowledge-driven, context-aware maritime autonomy.