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A fuzzy‐based risk assessment framework for autonomous underwater vehicle under‐ice missions


Loh, TY and Brito, MP and Bose, N and Xu, J and Tenekedjiev, K, A fuzzy‐based risk assessment framework for autonomous underwater vehicle under‐ice missions, Risk Analysis, 39, (12) pp. 2744-2765. ISSN 0272-4332 (2019) [Refereed Article]

Copyright Statement

Copyright 2019 Society for Risk Analysis

DOI: doi:10.1111/risa.13376


The use of autonomous underwater vehicles (AUVs) for various scientific, commercial, and military applications has become more common with maturing technology and improved accessibility. One relatively new development lies in the use of AUVs for under‐ice marine science research in the Antarctic. The extreme environment, ice cover, and inaccessibility as compared to open‐water missions can result in a higher risk of loss. Therefore, having an effective assessment of risks before undertaking any Antarctic under‐ice missions is crucial to ensure an AUV's survival. Existing risk assessment approaches predominantly focused on the use of historical fault log data of an AUV and elicitation of expertsí opinions for probabilistic quantification. However, an AUV program in its early phases lacks historical data and any assessment of risk may be vague and ambiguous. In this article, a fuzzy‐based risk assessment framework is proposed for quantifying the risk of AUV loss under ice. The framework uses the knowledge, prior experience of available subject matter experts, and the widely used semiquantitative risk assessment matrix, albeit in a new form. A well‐developed example based on an upcoming mission by an ISE‐explorer class AUV is presented to demonstrate the application and effectiveness of the proposed framework. The example demonstrates that the proposed fuzzy‐based risk assessment framework is pragmatically useful for future under‐ice AUV deployments. Sensitivity analysis demonstrates the validity of the proposed method.

Item Details

Item Type:Refereed Article
Keywords:autonomous underwater vehicle, fuzzy set theory, risk assessment, under ice
Research Division:Engineering
Research Group:Control engineering, mechatronics and robotics
Research Field:Autonomous vehicle systems
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Loh, TY (Mr Tzu Yang Loh)
UTAS Author:Bose, N (Professor Neil Bose)
UTAS Author:Tenekedjiev, K (Professor Kiril Tenekedjiev)
ID Code:136003
Year Published:2019
Web of Science® Times Cited:6
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2019-11-25
Last Modified:2020-04-06

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