In the field of autonomous surface vehicles (ASVs), devising decision-making and obstacle avoidance solutions that address maritime COLREGs (Collision Regulations), primarily defined for human operators, has long been a pressing challenge. Recent advancements in explainable Artificial Intelligence (AI) and machine learning have shown promise in enabling human-like decision-making. Notably, significant developments have occurred in the application of Large Language Models (LLMs) to the decision-making of complex systems, such as self-driving cars. The textual and somewhat ambiguous nature of COLREGs (from an algorithmic perspective), however, poses challenges that align well with the capabilities of LLMs, suggesting that LLMs may become increasingly suitable for this application soon. This paper presents and demonstrates the first application of LLM-based decision-making and control for ASVs. The proposed method establishes a high-level decision-maker that uses online collision risk indices and key measurements to make decisions for safe manoeuvres. A tailored design and runtime structure is developed to support training and real-time action generation on a realistic ASV model. Local planning and control algorithms are integrated to execute the commands for waypoint following and collision avoidance at a lower level. To the authors' knowledge, this study represents the first attempt to apply explainable AI to the dynamic control problem of maritime systems recognising the COLREGs rules, opening new avenues for research in this challenging area. Results obtained across multiple test scenarios demonstrate the system's ability to maintain online COLREGs compliance, accurate waypoint tracking, and feasible control, while providing human-interpretable reasoning for each decision.

翻译：在自主水面艇领域，设计符合《国际海上避碰规则》的决策与避障解决方案，始终是一项紧迫挑战。该规则主要面向人类操作者制定。近期可解释人工智能与机器学习的发展，为实现类人决策展现了潜力。值得注意的是，大语言模型在复杂系统（如自动驾驶汽车）决策中的应用已取得显著进展。然而，《国际海上避碰规则》的文本化特性及其（从算法视角看）存在的模糊性，恰恰与大语言模型的能力相契合，这表明大语言模型可能很快将愈发适用于此应用场景。本文提出并展示了首个基于大语言模型的自主水面艇决策与控制应用。所提方法建立了一个高层决策器，利用在线碰撞风险指数与关键测量数据，为安全机动做出决策。我们开发了定制化的设计与运行时结构，以支持在真实自主水面艇模型上进行训练和实时动作生成。同时集成了局部规划与控制算法，在底层执行航点跟踪与避碰指令。据作者所知，本研究首次尝试将可解释人工智能应用于识别《国际海上避碰规则》的海事系统动态控制问题，为这一挑战性领域的研究开辟了新途径。在多种测试场景中获得的结果表明，该系统能够保持在线合规于《国际海上避碰规则》、实现精确的航点跟踪与可行的控制，并为每个决策提供人类可理解的推理。