The increasing use and implementation of Autonomous Surface Vessels (ASVs) for various activities in maritime environments is expected to drive a rise in developments and research on their control. Particularly, the coordination of multiple ASVs presents novel challenges and opportunities, requiring interdisciplinary research efforts at the intersection of robotics, control theory, communication systems, and marine sciences. The wide variety of missions or objectives for which these vessels can be collectively used allows for the application and combination of different control techniques. This includes the exploration of machine learning to consider aspects previously deemed infeasible. This review provides a comprehensive exploration of coordinated ASV control while addressing critical gaps left by previous reviews. Unlike previous works, we adopt a systematic approach to ensure integrity and minimize bias in article selection. We delve into the complex world of sub-actuated ASVs with a focus on customized control strategies and the integration of machine learning techniques for increased autonomy. By synthesizing recent advances and identifying emerging trends, we offer insights that drive this field forward, providing both a comprehensive overview of state-of-the-art techniques and guidance for future research efforts.

翻译：随着自主水面航行器（ASVs）在海洋环境中各类活动的日益广泛应用与部署，其控制技术的发展与研究预计将迎来显著增长。特别是多ASV的协同控制提出了新的挑战与机遇，需要融合机器人学、控制理论、通信系统和海洋科学等多学科的交叉研究努力。这些航行器可集体执行的任务或目标种类繁多，使得不同控制技术的应用与融合成为可能，这包括探索利用机器学习来考虑以往被认为不可行的因素。本综述对ASV协同控制进行了全面探讨，同时弥补了以往综述遗留的关键空白。与既往研究不同，我们采用系统性方法以确保文献筛选的完整性与最小化偏差。我们深入探讨了欠驱动ASV的复杂控制领域，重点关注定制化控制策略以及集成机器学习技术以提升自主性。通过综合近期进展并识别新兴趋势，我们提出了推动该领域发展的见解，既全面概述了前沿技术，也为未来的研究工作提供了指导。