Abstract In recent decades, unmanned surface vehicles (USVs) are attracting increasing attention due to their underlying capability in autonomously undertaking complex maritime tasks in constrained environments. However, the autonomy level of USVs is still limited, especially when being deployed to conduct multiple tasks simultaneously. This paper, therefore, aims to improve USVs autonomy level by investigating and developing an effective and efficient task management algorithm for multi-USV systems. To better deal with challenging requirements such as allocating vast tasks in cluttered environments, the task management has been de-composed into two submissions, i.e., task allocation and task execution. More specifically, unsupervised learning strategies have been used with an improved K-means algorithm proposed to first assign different tasks for a multi-USV system then a self-organising map (SOM) been implemented to deal with the task execution problem based upon the assigned tasks for each USV. Differing to other work, the communication problem that is crucial for USVs in a constrained environment has been specifically resolved by designing a new competition strategy for K-means algorithm. Key factors that will influence the communication capability in practical applications have been taken into account. A holistic task management architecture has been designed by integrating both the task allocation and task execution algorithms, and a number of simulations in both simulated and practical maritime environments have been carried out to validate the effectiveness of the proposed algorithms.

中文翻译：

---

基于无监督学习的无人水面车辆协调多任务分配

摘要 近几十年来，无人水面舰艇 (USV) 因其在受限环境中自主执行复杂海上任务的潜在能力而受到越来越多的关注。但是，USV 的自主性水平仍然有限，尤其是在部署以同时执行多项任务时。因此，本文旨在通过研究和开发一种适用于多 USV 系统的有效且高效的任务管理算法来提高 USV 的自主水平。为了更好地应对具有挑战性的需求，例如在杂乱环境中分配大量任务，任务管理已分解为两个提交，即任务分配和任务执行。进一步来说，无监督学习策略已与改进的 K-means 算法一起使用，该算法提出首先为多 USV 系统分配不同的任务，然后实施自组织映射 (SOM) 以根据为每个分配的任务处理任务执行问题。 USV。与其他工作不同，通过为 K-means 算法设计新的竞争策略，已经专门解决了在受限环境中对 USV 至关重要的通信问题。考虑了实际应用中影响通信能力的关键因素。通过集成任务分配和任务执行算法，设计了一个整体的任务管理架构，