Task assignment is an important research topic in multiple autonomous underwater vehicle (AUV) cooperative working system. However, many studies concentrate on minimizing total distance of AUVs serving targets at different locations, and mostly do not pay attention to workload balance among inhomogeneous AUVs. What is more, most of them do not think of the effect of ocean current while distributing tasks. To solve these problems, a novel dual competition strategy based on self-organizing map (SOM) neural network is put forward. An AUV makes use of surplus sailing distance to a target when it competes with others for engaging the target. In order to fulfill a balanced task assignment among AUVs, a task balance coefficient is also proposed. Meanwhile, a hybrid path planning approach is applied to guide AUVs to reach their targets safely. The good performance of the proposed algorithm for distributing tasks among AUVs is demonstrated through simulation studies. From the comparison study of SOM algorithm, Hungarian algorithm, $k$ -means algorithm, and the proposed dual competition strategy, it can be found that the task assignment with the proposed strategy is more rational and fair.

中文翻译：

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非均匀自主水下航行器系统任务分配和路径规划的工作负载均衡算法

任务分配是多自主水下航行器（AUV）协同工作系统中的一个重要研究课题。然而，许多研究专注于最小化 AUV 服务不同位置目标的总距离，而大多不关注非均匀 AUV 之间的工作负载平衡。更重要的是，他们中的大多数人在分配任务时都没有考虑到洋流的影响。针对这些问题，提出了一种基于自组织映射（SOM）神经网络的新型双竞争策略。当 AUV 与其他目标竞争时，它会利用到目标的剩余航行距离。为了实现AUV之间的平衡任务分配，还提出了任务平衡系数。同时，采用混合路径规划方法引导AUV安全到达目标。通过仿真研究证明了所提出的算法在 AUV 之间分配任务的良好性能。从SOM算法、匈牙利算法、$k$-means算法和提出的双重竞争策略的对比研究可以发现，提出策略的任务分配更加合理公平。