Unsupervised learning based coordinated multi-task allocation for unmanned surface vehicles

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.