An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting

Abstract

Cooperative hunting by a multi-AUV system in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To conduct this task, each AUV needs to move quickly without obstacle collisions and cooperate with other AUVs considering the overall interests. In this paper, the heterogeneous AUVs cooperative hunting problem is studied, including two main tasks, namely the search and pursuit of targets, and a novel spinal neural system-based approach is proposed. In the search stage, a partition and column parallel search strategy is used in this paper, and a search formation control algorithm based on an improved spinal neural system is proposed. The presented search algorithm not only accomplishes the search task but also maintains a stable formation without obstacle collisions. In the cooperative pursuit stage, a dynamic alliance method based on bidirectional negotiation strategy and a pursuit direction assignment method based on improved genetic algorithm are presented, which can realize the pursuit task efficiently. Finally, some simulations are conducted and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the hunting tasks in unknown 3D underwater environment efficiently.