A Bayesian approach to risk modeling of autonomous subsea intervention operations

Hegde, Jeevith; Utne, Ingrid Bouwer; Schjølberg, Ingrid; Thorkildsen, Brede

doi:10.1016/j.ress.2018.03.019

Cited by 33 publications

(25 citation statements)

References 44 publications

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“…One way is to have a workshop with experts in the field, such as experienced captains. This workshop can be similar to risk analysis workshops such as [23] and [24]. Another option is to study captains during operation as done in [3].…”

Section: Priorsmentioning

confidence: 99%

Intention modelling and inference for autonomous collision avoidance at sea

Rothmund¹,

Tengesdal²,

Brekke³

et al. 2022

Preprint

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The open wording of the traffic rules of the sea, COLREGS, and the existence of unwritten rules, make it essential for an autonomous ship to understand the intentions of meeting traffic. This article uses a dynamic Bayesian network (DBN) to model and infer the intentions of other ships based on their observed real-time behavior. Multiple intention nodes are included to describe the different ways a ship can interpret and conflict with the behavioral rules outlined in CORLEGS. The prior distributions of the intention nodes are adapted to the current situation based on observable characteristics such as location and relative ship size. When a new observation is made, the probability distributions of the intention variables are updated by excluding all combinations of intention states that conflict with the observed behavior. This way of modeling makes the intention probabilities independent of how often observations are made. The resulting model is able to identify situations that are prone to cause misunderstandings and infer the state of multiple intention variables that describe the behavior. Different collision avoidance algorithms can use the resulting intention information to better know if, when, and how to act.

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Section: Priorsmentioning

confidence: 99%

Intention modelling and inference for autonomous collision avoidance at sea

Rothmund¹,

Tengesdal²,

Brekke³

et al. 2022

Preprint

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“…Using associative reasoning, risk analysts can inform decision‐makers about how the probability of an outcome changes for a particular event; this occurs by performing evidence propagation. For example, Hegde, Utne, Schjølberg, and Thorkildsen ( 2018 ) developed a BN model to help supervisors to determine when an autonomous underwater vehicle mission should be aborted. This model connects 41 technical, organizational, and operational causal factors to a target node that represents the probability of aborting a mission.…”

Section: A Primer On Causality and Interventions In Engineering Risk ...mentioning

confidence: 99%

Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions

2021

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In the last decade, Bayesian networks (BNs) have been widely used in engineering risk assessment due to the benefits that they provide over other methods. Among these, the most significant is the ability to model systems, causal factors, and their dependencies in a probabilistic manner. This capability has enabled the community to do causal reasoning through associations, which answers questions such as: "How does new evidence x about the occurrence of event X change my belief about the occurrence of event Y ?" Associative reasoning has helped risk analysts to identify relevant risk-contributing factors and perform scenario analysis by evidence propagation. However, engineering risk assessment has yet to explore other features of BNs, such as the ability to reason through interventions, which enables the BN model to support answering questions of the form "How does doing X = x change my belief about the occurrence of event Y ?" In this article, we propose to expand the scope of use of BN models in engineering risk assessment to support intervention reasoning. This will provide more robust risk-informed decision support by enabling the modeling of policies and actions before being implemented. To do this, we provide the formal mathematical background and tools to model interventions in BNs and propose a framework that enables its use in engineering risk assessment. This is demonstrated in an illustrative case study on third-party damage of natural gas pipelines, showing how BNs can be used to inform decision-makers about the effect that new actions/policies can have on a system.

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“…They are also applied in medical contexts [54] and robotics [55]. For the latter, one should mention that in the underwater environment risk assessment with Bayesian networks is a current topic [56,57].…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

Determination of Flow Parameters of a Water Flow Around an AUV Body

Hoth

Kowalczyk

2019

Robotics

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Autonomous underwater vehicles (AUVs) have changed the way marine environment is surveyed, monitored and mapped. Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings. AUVs not only perform a given task but also adapt to changes in the environment, e.g., sudden side currents, downdrafts, and other effects which are extremely unpredictable. To navigate properly and allow simultaneous localisation and mapping (SLAM) algorithms to be used, these effects need to be detected. With current navigation systems, these disturbances in the water flow are not measured directly. Only the indirect effects are observed. It is proposed to detect the disturbances directly by placing pressure sensors on the surface of the AUV and processing the pressure data obtained. Within this study, the applicability of different learning methods for determining flow parameters of a surrounding fluid from pressure on an AUV body are tested. This is based on CFD simulations using pressure data from specified points on the surface of the AUV. It is shown that support vector machines are most suitable for the given task and yield excellent results.

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A Bayesian approach to risk modeling of autonomous subsea intervention operations

Cited by 33 publications

References 44 publications

Intention modelling and inference for autonomous collision avoidance at sea

Intention modelling and inference for autonomous collision avoidance at sea

Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions

Determination of Flow Parameters of a Water Flow Around an AUV Body

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