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Dynamic Task Assignment for Multi-AUV Cooperative Hunting

Xiang Cao1,2,3, Haichun Yu1,3, Hongbing Sun1,3
1 School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China;
2 School of Automation, Southeast University, Nanjin 210096, China;
3 Jiangsu Key Construction Laboratory of Modern Measurement Technology and Intelligent System, Huaian 223300, China.
* Corresponding Author: Xiang Cao,

Intelligent Automation & Soft Computing 2019, 25(1), 25-34. https://doi.org/10.31209/2018.100000038

Abstract

For cooperative hunting by a multi-AUV (multiple autonomous underwater vehicles) team, not only basic problems such as path planning and collision avoidance should be considered but also task assignments in a dynamic way. In this paper, an integrated algorithm is proposed by combining the self-organizing map (SOM) neural network and the Glasius Bio-Inspired Neural Network (GBNN) approach to improve the efficiency of multi-AUV cooperative hunting. With this integrated algorithm, the SOM neural network is adopted for dynamic allocation, while the GBNN is employed for path planning. It deals with various situations for single/multiple target(s) hunting in underwater environments with obstacles. The simulation results show that the proposed algorithm is capable of a cooperative hunting task with efficiency and adaptability.

Keywords

multiple autonomous underwater vehicles (Multi-AUV), cooperative hunting, dynamic task assignment, self-organizing map (SOM) neural network, Glasius Bio-Inspired Neural Network (GBNN).

Cite This Article

. , "Dynamic task assignment for multi-auv cooperative hunting," Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 25–34, 2019.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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