A multi-AUVs Bio-inspired Cooperative Hunting Algorithm for Environment with Ocean Current and Obstacles

Qingqin Liu, Bing Sun, Chunhua Gu, Dinghua Zhang, Daqi Zhu

Abstract: As a result of the complexity and indeterminacy of the underwater environment, multi-AUV cooperative hunting is a very challenging research area. In this paper, the focus is on the underwater environment with ocean currents and obstacles, and a novel multi-AUV bio-inspired cooperative hunting algorithm is presented. Firstly, a bio-inspired Neural Network model (BINN) is established to represent the AUV's underwater working environment. Each point in the grid map corresponds to the activity of a neural node in the BINN, and the next target point of the hunting AUV can be planned autonomously according to the neural activity of the neurons in the BINN. Secondly, the direction decision algorithm is embedded into the BINN hunting model for the ocean current environment, and the integrated algorithm proposed is compared with the hunting method without the direction decision. Finally, simulation studies are presented to demonstrate that the proposed algorithm is effective for cooperative hunting missions.

Submission Number: 68