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本文提出一种两阶段新框架，将迭代 K-means 聚类与蚁群优化相结合，面向续航受限 AUV 的大尺度海底测量任务求解能耗最优路径规划问题，并通过系统仿真验证其有效性。

This paper proposes a novel two-stage framework combining iterative K-means clustering and ant colony optimization to solve energy-optimal path planning for large-scale seabed survey missions using endurance-constrained AUVs, validated through comprehensive simulations.

本文从“性能视角”对无人帆船设计进行了系统综述，围绕结构耐久性、能源自持能力、抗倾覆能力与航速性能四个关键指标，梳理当前研究与工程实践的主要局限，并提出面向任务场景的自适应机构与基于仿真的优化设计等未来方向，以提升无人帆船在海洋科学观测中的应用价值。

This comprehensive review analyzes autonomous sailboat design through the critical performance lens of structural durability, energy self-sufficiency, overturning resistance, and sailing speed, identifying current limitations and proposing future directions with adaptive mechanisms and simulation-based optimization to enhance their role in marine science.

本文面向无人单体帆船的早期设计评估需求，构建通用速度预报程序（VPP）框架，并通过遗传算法求解器与基于邻域信息的优化策略（NIBO）增强求解效率与稳定性，实现对多种非常规船-帆-龙骨方案的快速性能对比与定量评估。

This study develops a generalized velocity prediction program (VPP) enhanced by a genetic algorithm solver and neighbourhood information-based optimization (NIBO) strategy, enabling efficient performance evaluation of autonomous sailing monohulls in early design stages.

本文提出一种结合贝叶斯优化与知识迁移的双代理建模框架，用于翼帆无人单体帆船总体参数优化设计。在显著提升航速性能的同时，可将计算成本降低约 40–60%，并在“Seagull”原型平台上得到验证。

This study proposes a Bayesian optimisation and knowledge transfer framework that reduces computational costs by 40-60% while significantly enhancing the speed performance of wing-sailed autonomous sailboats, validated on the “Seagull” prototype.

本研究围绕无人帆船“航速性能提升”这一核心目标，针对航速与抗倾覆能力之间的矛盾，提出并系统构建了多种设计评估与优化方法，包括基于 CFD 的性能评估、结合知识迁移的贝叶斯优化设计框架，以及用于提高稳性与间接增速的被动水翼稳性方案。

This research develops novel design evaluation and optimization methods to enhance the speed performance of autonomous sailboats, specifically addressing the conflict between speed and capsize resistance through CFD-based evaluation, Bayesian optimization with knowledge transfer, and passive hydrofoil stabilizers.

本文针对浮式海上风电机组整体化安装对接（mating）过程中关键对接点相对运动难以预测的问题，提出一种数据驱动的人工神经网络模型用于预测极值相对位移与速度，从而为对接安全预警与人员决策提供支持。

This study proposes a data-driven artificial neural network model to predict extreme relative motions between mating points during floating offshore wind turbine installation, enabling safer mating operations and informed crew decision-making.

本文提出一种考虑运动学约束的分布式协同覆盖方案，用于多无人水面艇（USV）在动态扩展水域中的水污染监测，实现实时覆盖优化与冗余抑制。

This paper develops a novel kinematic-aware distributed scheme for coordinated water pollution monitoring using multiple Unmanned Surface Vehicles (USVs), achieving real-time coverage optimization in dynamically expanding aquatic environments.

本文提出首个面向翼帆无人帆船的高保真 MATLAB/Simulink 过程对象（process plant）模型，将多体动力学与基于 CFD 的气/水动力建模相融合，并通过静态与动态试验完成验证。

This paper presents the first high-fidelity MATLAB/Simulink process plant model for wing-sail unmanned sailboats, integrating multi-body dynamics with CFD-informed aerodynamics/hydrodynamics and validating it through static-dynamic tests.

本文针对传统波浪浮标类比（WBA）方法在演化海况中因“刚性平稳性假设”而引入的误差问题，提出一种基于小波的自适应分段算法，将波浪视为半平稳过程，并可从船舶运动数据中动态捕捉随时间变化的海况特征。数值仿真表明，该方法能够抑制在病态反演估计系统中易被放大的谱误差，从而显著提升实时监测的可靠性。

This study addresses the inaccuracies of traditional wave buoy analogy (WBA) methods caused by rigid stationary assumptions in evolving oceans. We propose a wavelet-based adaptive segmentation algorithm that treats waves as semi-stationary processes, dynamically capturing time-dependent variations in ship motion data. Simulations demonstrate that this approach significantly enhances the reliability of real-time monitoring by mitigating spectral errors that are otherwise amplified in ill-posed estimation systems.

本文提出首个面向“非均匀且时变海洋环境”的无人帆船时空覆盖路径规划（CPP）专用框架。该方法将拓扑形态约束与面向预报的前瞻规划相结合，有效应对动态流场导致的性能各向异性与局部不可达问题。仿真结果表明，在传统策略失效的复杂海洋环境中，该框架仍能生成高效且可行的覆盖路径。

This study presents the first dedicated spatiotemporal coverage path planning (CPP) framework for autonomous sailboats in inhomogeneous and time-varying oceans. By integrating topological morphological constraints with forecast-aware look-ahead planning, the method overcomes performance anisotropy and partial accessibility induced by dynamic flows. Simulations show that the framework generates efficient, feasible paths in complex marine environments where traditional strategies fail.

本文从“性能视角”综合梳理了竞技帆船与自主机器人帆船之间长期存在的学科隔阂。通过追溯两者的历史分化并引入结构化的五指标评估框架，系统评估了方法论在四个层级维度（部件性能、船体建模、运动控制与高层航路规划）之间的可迁移性，识别关键协同点与共同挑战，为全球风力推进领域的协同发展提供路线图。

This review provides a perspective-driven synthesis of the disciplinary divide between competitive racing and autonomous robotic sailing. By tracing their historical divergence and applying a structured five-metric framework, the study evaluates the transferability of methodologies across four hierarchical dimensions: component performance, vessel modeling, motion control, and high-level routing. It identifies critical synergies and shared challenges, offering a strategic roadmap for mutual advancement within the global wind-propulsion community.

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