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A Novel Classified Self-Organising Map Applied to Task Assignment

Published online by Cambridge University Press:  27 May 2020

Yun Qu  and
Daqi Zhu Open the ORCID record for Daqi Zhu [Opens in a new window]
Yun Qu
Affiliation:
(Shanghai Engineering Research Center of Intelligent Maritime Search & Rescue and Underwater Vehicles, Shanghai)
Daqi Zhu*
Affiliation:
(Shanghai Engineering Research Center of Intelligent Maritime Search & Rescue and Underwater Vehicles, Shanghai)
*
(E-mail: [email protected])
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Abstract

With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given.

Keywords

Autonomous Underwater Vehicles (AUVs)Classified Self-Organising MapMatchedOrder
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 5 , September 2020 , pp. 1129 - 1145
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Copyright
Copyright © The Royal Institute of Navigation 2020

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