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Multi-AUV Cooperative Hunting Control with Improved Glasius Bio-inspired Neural Network

Published online by Cambridge University Press:  05 November 2018

Mingzhi Chen  and
Daqi Zhu Open the ORCID record for Daqi Zhu [Opens in a new window]
Mingzhi Chen
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
Daqi Zhu*
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
*
(E-mail: [email protected])
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Abstract

Cooperative hunting with multiple Autonomous Underwater Vehicles (AUVs) not only needs the AUVs to cooperate, but also demands real-time path planning to catch up with evading targets. In this paper a time-based alliance mechanism to form efficient dynamic hunting alliances is proposed. After that, during the active hunting stage, an improved neural network model based on a Glasius Bio-inspired Neural Network (GBNN) is presented for path planning to immediately achieve tracking of an intelligent target. This study shows that the improved GBNN model has good performance in real-time hunting path planning. From the simulation studies as described in this paper, both the hunting alliance formation mechanism and the proposed real-time hunting path planning strategy show their advantages. The results show that the improved GBNN model proposed in this paper can work well in the control of multiple AUVs to hunt for intelligent evading targets in environments containing obstacles.

Keywords

Multi-AUV SystemHunting ControlImproved Glasius Bio-inspired Neural Network (GBNN)Time-based AllianceReal-time Path planning
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 759 - 776
Copyright
Copyright © The Royal Institute of Navigation 2018 

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