Risk and reliability modelling for multi-vehicle marine domains

Abstract

Abstract-It is well-known that autonomous underwater vehicle (AUV) missions are a challenging, high-risk robotics application. With many parallels to Mars rovers, AUV missions involve operating a vehicle in an inherently uncertain environment of which our prior knowledge is often sparse or low-resolution. The lack of an accurate prior, coupled with poor situational awareness and potentially significant sensor noise, presents substantial engineering challenges in navigation, localisation, state estimation and control. When constructing missions and operating AUVs, it is important to consider the risks involved. Stakeholders need to be reassured that risks of vehicle loss or damage have been minimised where possible, and scientists need to be confident that the mission is likely to produce sufficient high-quality data to meet the aims of the deployment. In this paper, we consider the challenges associated with risk analysis methods and representations for multi-vehicle missions, reviewing the relevant literature and proposing a methodology.

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

“…Qualitative Safety layer method (Ortiz et al, 1999) Semiquantitative Risk management process (Griffiths and Trembanis, 2007;Brito et al, 2010;Griffiths and Brito, 2011;Thieme et al, 2015a) Fault tree analysis (Bian et al, 2009a, b;Hu et al, 2013;Xu et al, 2013;Aslansefat et al, 2014;Thieme et al, 2015a;Brito, 2016;Harris et al, 2016;Xiang et al, 2017;Brito and Chang, 2018) Event tree analysis (Thieme et al, 2015a; Failure Mode and Effects Analysis (Hu et al, 2013;Harris et al, 2016) Bow-tie model (Yu et al, 2017) Kaplan-Meier survival model (Brito et al, 2010;Brito et al, 2014a;Brito and Griffiths, 2016) Fuzzy set theory (Loh et al, 2019;Loh et al, 2020a;Loh et al, 2020b;Xu et al, 2020) Quantitative Bayesian belief network (Griffiths and Brito, 2008;Thieme et al, 2015b;Brito and Griffiths, 2016;Hegde et al, 2018;Bremnes et al, 2019) Markov chain (Brito and Griffiths, 2011;Griffiths and Brito, 2011) System dynamics Loh et al, 2020a;Loh et al, 2020c, b;Xu et al, 2020) Fig. 7.…”

Review of mission planning for autonomous marine vehicle fleets

“…Ingrand and Ghallab () presented a well‐distilled survey of the enormous amount of literature addressing deliberative planning for autonomous robots with small discussion on deliberation for multirobot systems, the take‐home messages of which we apply specifically to the AMV fleet domain. Finally, Harris, Phillips, Dopico‐Gonzalez, and Brito () reviewed the risk and reliability models used for multi‐AMV operations, the results of which we bring to the discussion of automated planning. To the extent of the authors’ knowledge, there are no surveys that have comprehensively covered planning for AMV fleets.…”

A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration

“…18 Therefore, risk models related to mission success (or correspondingly mission failure) are needed for decision support to the human operator. 19…”

“…Regarding the qualitative risk assessment, they identify human error in remote operation and maintenance, foundering in heavy weather, and security issues as the main hazards. Some risk models for autonomous vessels address heavy weather conditions, such as Ono et al 37 and Li et al 38 Harris et al 19 review models for risk assessment of AUV and similar systems. They assess the applicability of these models to multi-vehicle operations and conclude that a bottom-up approach to risk assessment is most suitable.…”

“…18 Therefore, risk F o r P e e r R e v i e w 3 models related to mission success (or correspondingly mission failure) are needed for decision support to the human operator. 19 "Autonomous" does not mean that no personnel will operate them. Autonomy is a system's ability to change its pre-programmed plan of action to achieve its goal.…”