A Review of Risk Analysis Research for the Operations of Autonomous Underwater Vehicles

https://doi.org/10.1016/j.ress.2021.108011Get rights and content

Highlights

  • A systematic review of risk analysis for autonomous underwater vehicles is provided.

  • Risk factors and causal relationships of AUV operations are identified and analyzed.

  • A comparative study for evolving methods/models of AUV risk analysis is performed.

  • Four research directions for future works are highlighted to bridge existing gaps.

Abstract

Risk analysis for autonomous underwater vehicles (AUVs) is essential to assist decision making for safer operations. This study aims to provide a systematic review of risk analysis research to enhance the safety performance of AUVs. Forty-two domain articles were retrieved and analyzed. Critical risk factors and causal relationships of AUV operations were identified. A comparative analysis of evolving methods and models was performed by categorizing them as qualitative, semi-quantitative, and quantitative. Future trends of research in this field were also outlined. The study observes that as AUV technologies gradually mature, environmental factors, human factors, and their interactive impacts are gathering more attention. Quantitative risk analysis methods have recently played a key role in improving the accuracy and handling the uncertainties of risk estimation. The study recommends devoting efforts to dynamic risk analysis, addressing limited historical data, intelligent risk analysis, and multi-vehicles risk analysis for future works. This study is expected to help AUV stakeholders gain comprehensive insights into fundamental concepts and evolving methods for risk analysis of AUVs. At the same time, it is expected to highlight future directions to bridge existing gaps.

Introduction

Autonomous underwater vehicles (AUVs) are effective platforms for navigating underwater or under ice to provide automated measurements without human intervention [1, 2]. The high level of autonomy of AUVs makes them an ideal tool for multiple data-gathering applications in scientific [3], [4], [5], commercial [6], military [7], and geopolitical areas [8]. In recent research, AUVs are increasingly deployed in harsh environments such as under sea ice or ice shelves in the Antarctic [4, 9, 10] and the Arctic regions [3, 11, 12]. Operating in such extreme conditions, including thick ice cover, permafrost, fragile material integrity, unpredictable climatic changes, and poor visibility, will inevitably pose a higher risk to both the physical vehicle and the onsite AUV supervisors compared to open water missions [13]. Hence, it is essential to conduct effective risk analysis before a mission to ensure the safe deployment of AUVs.

Table 1, which is adapted from a former study [14], summarizes potential accident types of AUV operations and their severity according to the level of damage to the vehicle itself, where AUV loss could be regarded as the most severe accident. AUV loss usually refers to the complete loss of the physical vehicle or an AUV being damaged and unrepairable for future missions. It is not only financially costly due to the higher insurance premium and acquisition costs of the vehicle [15]. Furthermore, it may also cause time delays or even the termination of research projects, lead to the loss of valuable gathered data, and potentially harm fragile polar environments [16, 17].

Over the years, there have been a number of formally reported accidents of AUV losses during deployment, as shown in Fig. 1. For example, the AUV Autosub2 was lost under the Fimbulisen ice shelf in Antarctica in February 2005. A formal accident inquiry concluded that this accident was equally likely to have been caused by an abort command or a loss of power. These technical failures was most likely introduced during the manufacturing and assembly phases [18]. Another lost vehicle, SeaBED, which was designed to scan the seafloor below overhanging sea ice, became trapped under the Antarctic ice during a mission and was almost crushed by an iceberg before it was rescued [19]. The Autonomous Benthic Explorer (ABE) was lost in March 2010, during its 222nd research dive off the coast of Chile. Researchers believed that the loss of the ABE was also caused by a technical failure. More specifically, the ABE may have suffered a catastrophic implosion of a glass sphere used for providing buoyancy, causing instant destruction of the on-board systems. Consequently, the ABE failed to send fail-safe commands for helping itself float to the surface for recovery [20]. An underwater glider, Seaglider SG522, lost communication in the Antarctic in February 2012 after having completed 156 dives. The inquiry panel identified that the root cause was an erroneous command, which resulted in this glider continuously diving and eventually being lost [21]. In April 2014, the Autosub Long Range AUV lost communication during a mission near the Irish coast. Luckily, it re-transmitted its position signal and was recovered after three months. More recently, a Hugin AUV was lost during its first under ice mission in the Antarctic in January 2019, and it was recovered four days later. Pre-dive checks had been reviewed for this vehicle without any irregularities. Technicians believed the vehicle was trapped below an ice floe, causing the Iridium signal for the AUV position failing to be received [22].

From the overview of historical accidents of AUV loss, it can be observed that the potential causes of historical accidents show a wide variety, which confirms the unpredictable and uncertain features of AUV related accidents. This non-uniform accidental pattern and relatively severe consequences imply the vulnerability of AUV operations and reinforce the necessity of implementing effective risk analysis before an AUV mission.

Risk analysis is a proactive approach for hazard identification, consequence analysis, and risk estimation for potential accidents [23]. There is a long history of the development of risk analysis techniques that have been applied in multiple fields, including nuclear power, chemical process, aerospace, and offshore oil and gas industries [24], [25], [26], [27]. Currently, with the booming development of the maritime industry, applications of risk analysis methods are also stimulated in this area [28], [29], [30], [31], [32]. Since marine systems are becoming more autonomous, using the AUV is an ongoing trend in the maritime industry for ocean research, ocean monitoring, military and commercial data-gathering, and so on [2, 29]. As AUV technologies have gradually matured, risk analysis for AUVs has rapidly become essential to ensure safer operations and assist decision making. A number of past efforts regarding risk analysis have been undertaken to improve the safety performance of AUVs. However, to the best knowledge of the authors, a systematic review and analysis of past studies has not yet been done. As a thorough review will enable domain researchers to gain a better understanding of AUV risk analysis and benefit future development, the authors believe that a critical review article is timely.

In light of the above, the objective of this article is to provide a structured review of risk analysis research regarding AUV operations. It aims to answer four key questions arising from historical developments and to highlight future trends in this domain. The four key questions as the focus of this review are listed in Table 2, which shows the overall process of this literature review from analyses of past studies. The main contribution of this study is to help researchers and AUV stakeholders obtain comprehensive insights about fundamental concepts and evolving methods for the risk analysis of AUVs. Meanwhile, it is expected to indicate directions for future research to bridge existing gaps.

The scope of this study is restricted to risk analysis for AUV operations. According to the objective and scope of this review, the literature retrieval was performed based on keywords searching including AUVs with the combination of risk identification, risk analysis, risk assessment, risk management, risk mitigation, risk modeling, and safety measures. A total of forty-two articles with significant relevance to the research purpose and scope were retrieved. In addition, to better answer the research questions and facilitate further statistical analysis, the selected publications were classified into various aspects, including the type of identified risk factors, the type of adopted risk analysis methods, the type of mission forms, the area of operations, and the type of potential consequences. The dataset of selected literature is classified and summarized in the Appendix.

The article is structured as follows. In section 2, critical risk factors of AUV operations are analyzed by categorizing them into technical factors, environmental factors, and human factors. Section 3 compares the evolving methods or models applied for AUV risk analysis by classifying them as three types: qualitative methods, semi-quantitative methods, and quantitative methods. Section 4 outlines current research gaps and future directions. The summary and conclusion of this study are given in Section 5.

Section snippets

Analysis of risk factors of AUV operations

Risk factors identification is defined as the process of identifying potential risk factors, which is the first step of the risk analysis phases [23]. Based on past studies, risk factors related to AUV operations are identified and analyzed in this section by categorizing them into technical factors, human factors, and environmental factors. Fig. 2 presents the number and distribution of publications of risk analysis of AUVs regarding three types of risk factors. As mentioned in the

Risk analysis methods of AUV operations

This section provides an overview of existing methods for risk analysis of AUV operations. It aims to outline the evolution of the developed methods and models, critically analyze the progress and limitations of past research, and highlight future research trends in this domain. This section is expected to help researchers gain a better understanding of historical developments for AUV risk analysis methods and bridge the existing research gaps in future work. In this section, the reviewed

Future challenges for risk analysis of AUV operations

Based on the above analysis of past progress, section 4 identifies current research gaps and discusses future challenges in the domain of AUV risk analysis.

Summary and conclusion

The main objective of this study is to provide a systematic review of past progress of risk analysis research for AUV operations. This review answers key questions including fundamental concepts and evolving methods in the domain of risk analysis for AUVs, and it highlights future research trends to bridge existing gaps. The scope of this article is restricted to the research questions. Based on the aim and scope of this study, a total of forty-two articles with significant relevance to

CRediT authorship contribution statement

Xi Chen: Conceptualization, Methodology, Visualization, Writing – original draft. Neil Bose: Conceptualization, Supervision, Validation, Funding acquisition, Writing – review & editing. Mario Brito: Supervision, Validation, Visualization, Writing – review & editing. Faisal Khan: Conceptualization, Supervision, Methodology, Funding acquisition, Writing – review & editing. Bo Thanyamanta: Resources, Validation, Writing – review & editing. Ting Zou: Supervision, Validation, Writing – review &

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is funded by Fisheries and Oceans Canada through the Multi-partner Oil Spill Research Initiative (MPRI) 1.03: Oil Spill Reconnaissance and Delineation through Robotic Autonomous Underwater Vehicle Technology in Open and Iced Waters. Coauthor, Faisal Khan, wishes to acknowledge the financial support provided by the Canada Research Chair (Tier 1) program on Offshore Safety and Risk Engineering.

References (123)

  • A Alvarez et al.

    Fòlaga: A low-cost autonomous underwater vehicle combining glider and AUV capabilities

    Ocean Engineering

    (2009)
  • E Locorotondo et al.

    Development of a battery real-time state of health diagnosis based on fast impedance measurements

    Journal of Energy Storage

    (2021)
  • L Pugi et al.

    Redundant and reconfigurable propulsion systems to improve motion capability of underwater vehicles

    Ocean Engineering

    (2018)
  • K Wróbel et al.

    Towards the assessment of potential impact of unmanned vessels on maritime transportation safety

    Reliability Engineering & System Safety

    (2017)
  • MJ Akhtar et al.

    Human fatigue's effect on the risk of maritime groundings–A Bayesian Network modeling approach

    Safety science

    (2014)
  • MJ Doble et al.

    Through-ice AUV deployment: Operational and technical experience from two seasons of Arctic fieldwork

    Cold Regions Science Technology

    (2009)
  • X Xiang et al.

    On intelligent risk analysis and critical decision of underwater robotic vehicle

    Ocean Engineering

    (2017)
  • N Khakzad et al.

    Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

    Reliability Engineering & System Safety

    (2011)
  • C-T Lin et al.

    Hybrid fault tree analysis using fuzzy sets

    Reliability Engineering & System Safety

    (1997)
  • M Čepin et al.

    A dynamic fault tree

    Reliability Engineering & System Safety

    (2002)
  • M Ghadhab et al.

    Safety analysis for vehicle guidance systems with dynamic fault trees

    Reliability Engineering & System Safety

    (2019)
  • NUI Hossain et al.

    A Bayesian network based approach for modeling and assessing resilience: A case study of a full service deep water port

    Reliability Engineering & System Safety

    (2019)
  • G Song et al.

    Dynamic occupational risk model for offshore operations in harsh environments

    Reliability Engineering & System Safety

    (2016)
  • H Xu et al.

    Reliability analysis of an autonomous underwater vehicle using fault tree

  • Dowdeswell JA, Evans J, Mugford R, Griffiths G, McPhail S, Millard N, et al. Autonomous underwater vehicles (AUVs) and...
  • A Jenkins et al.

    Observations beneath Pine Island Glacier in West Antarctica and implications for its retreat

    Nature Geoscience

    (2010)
  • A Kleiner et al.

    Ice class AUV development. OTC

  • D Rothrock et al.

    The accuracy of sea ice drafts measured from US Navy submarines

    Journal of Atmospheric Oceanic Technology

    (2007)
  • M Brito et al.

    A behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments

    Journal of Atmospheric Oceanic Technology

    (2012)
  • G Williams et al.

    Thick and deformed Antarctic sea ice mapped with autonomous underwater vehicles

    Nature Geoscience

    (2015)
  • DE Gwyther et al.

    Cold ocean cavity and weak basal melting of the Sørsdal ice shelf revealed by surveys using autonomous platforms

    Journal of Geophysical Research: Oceans

    (2020)
  • P Wadhams et al.

    A new view of the underside of Arctic sea ice

    Geophysical Research Letters

    (2006)
  • G Salavasidis et al.

    Terrain Aided Navigation for Long Range AUV operations at arctic latitudes

    2016 IEEE/OES Autonomous Underwater Vehicles (AUV)

    (2016)
  • TY Loh et al.

    Human Error in Autonomous Underwater Vehicle Deployment: A System Dynamics Approach

    Risk Analysis

    (2020)
  • Z Hu et al.

    Failure analysis for the mechanical system of autonomous underwater vehicles

  • Griffiths G, Bose N, Ferguson J, Blidberg D. Insurance for autonomous underwater vehicles. Underwater Technology. 2007;...
  • G Griffiths et al.

    Masterclass in AUV technology for Polar science: collaborative Autosub science in extreme environments

  • MP Brito et al.

    Risk analysis for autonomous underwater vehicle operations in extreme environments

    Risk Analysis: An International Journal

    (2010)
  • J. Strutt

    Report of the inquiry into the loss of Autosub2 under the Fimbulisen

    National Oceanography Centre Southampton

    (2006)
  • Waters H. The robots that dare to explore Antarctica's frozen ocean. Retrieved from...
  • L. Lippsett

    RIPABE: the pioneering Autonomous Benthic Explorer is lost at sea

    Oceanus

    (2010)
  • Bound M. Expedition blogs. Retrieved from https://oceanwide-expeditions.com/....
  • M. Rausand

    Risk assessment: theory, methods, and applications

    (2013)
  • E Paté-Cornell et al.

    Probabilistic risk analysis for the NASA space shuttle: a brief history and current work

    Reliability Engineering & System Safety

    (2001)
  • Wróbel K, Montewka J, Kujala P. Towards the development of a system-theoretic model for safety assessment of autonomous...
  • M Rausand et al.

    System reliability theory: models, statistical methods, and applications

    (2003)
  • Hegde J, Utne IB, Schjølberg I, Thorkildsen B. A Bayesian approach to risk modeling of autonomous subsea intervention...
  • L Paull et al.

    AUV Navigation and Localization: A Review

    IEEE Journal of Oceanic Engineering

    (2014)
  • Bao J, Li D, Qiao X, Rauschenbach T. Integrated navigation for autonomous underwater vehicles in aquaculture: A review....
  • G Griffiths et al.

    Undersea gliders

    Journal of Ocean Technology

    (2007)
  • Cited by (0)

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