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Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

Published online by Cambridge University Press:  30 June 2017

Xiang Cao  and
A-long Yu
Xiang Cao*
Affiliation:
(School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China)
A-long Yu
Affiliation:
(School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China)
*
(E-mail: cxeffort@126.com)
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Abstract

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

Keywords

Cooperative target searchMulti-AUV. Self-organising mapGlasius bioinspired neural network
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1293 - 1311
Copyright
Copyright © The Royal Institute of Navigation 2017 

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