A Workload Balanced Algorithm for Task Assignment and Path Planning of Inhomogeneous Autonomous Underwater Vehicle System

Abstract

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.