Applications of Autonomous Underwater Vehicles in Offshore Petroleum Industry Environmental Effects Monitoring

Niu, Haibo; Adams, Shane Michael; Husain, Tahir; Bose, N; Lee, K

doi:10.2118/2007-116

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Abyssal topographic information is crucial for marine scientific research and environmental protection [1][2]. Currently, ship-borne multi-beam survey is the main method to obtain abyssal bathymetry information, but is with low precision and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Accurate registration of abyssal terrain data from shipborne and AUV platforms

Liu,

Li,

Zhang

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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Abyssal topographic information is critical for abyssal scientific research. The ship-borne multi-beam system efficiently measures deep-water seafloor but has low precision and resolution. By contrast, a muti-beam system mounted on AUV can obtain precise depth data and effectively supplement ship-borne multi-beam data. When combining datasets from these two platforms, improved spatiotemporal coverage can characterize the abyssal seafloor change. However, the AUV platform fixes its position using acoustic methods, which introduces a significant bias in its positioning reference. Consequently, the depth variation can be misinterpreted when combining depth data from ship-borne and AUV platforms. In this paper, the robust terrain matching method is used to register the data of different platforms. The method was tested on the Juan de Fuca Ridge datasets. Computational results demonstrate that the proposed method eliminates the positioning reference bias effectively and thus improves the accuracy of the interpretation of the abyssal seafloor change.

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Section: Introductionmentioning

confidence: 99%

Accurate registration of abyssal terrain data from shipborne and AUV platforms

Liu,

Li,

Zhang

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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“…In the future, AUV will be increasingly operated together with other autonomous systems, for example, autonomous aerial vehicles and surface vessels, for example, for joint monitoring of the environment. 16,17 In order to carry out such operations satisfactorily, AUVs need to be highly reliable. AUVs are expensive assets, often purpose built with a specific payload.…”

Section: Introductionmentioning

confidence: 99%

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

Thieme

Utne

2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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Autonomous Marine Systems, such as autonomous ships and autonomous underwater vehicles (AUVs), gain increased interest in industry and academia. Expected benefits of AMS in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. AUVs are applied in scientific, commercial and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. AUVs are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with AUVs, in order to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian Belief Network (BBN) to assess the Human Autonomy Collaboration Performance (HAC), as part of a risk model for AUV operation. HAC represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the HAC can be improved in two ways; (i) through better training and inclusion of experienced operators, and (ii) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the HAC BBN can improve AUV design and AUV operations by clarifying relationships between technical, human and organizational factors and their influence on mission risk. The article focuses on AUV, but the results should be applicable to other types of AMS.

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“…AUVs are mostly cigar shaped untethered vehicles which follow a preprogrammed mission path through the ocean. Future operations of AUVs will be combined with other unmanned systems, such as drones and autonomous surface vessels, e.g., for ice monitoring in arctic areas (Haugen et al, 2011) or environmental impact studies (Niu et al, 2009). For these operations, a high reliability is required to perform satisfactory and deliver the required data without loss of the involved vehicles.…”

Section: Introductionmentioning

confidence: 99%

Risk modeling of autonomous underwater vehicle operation focusing on the human operator

Thieme¹,

Utne²,

Schjølberg³

2015

Safety and Reliability of Complex Engineered Systems

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Applications of Autonomous Underwater Vehicles in Offshore Petroleum Industry Environmental Effects Monitoring

Abstract: Environmental Effects Monitoring (EEM) is an important tool in assisting Environmental

Cited by 6 publications

References 2 publications

Accurate registration of abyssal terrain data from shipborne and AUV platforms

Accurate registration of abyssal terrain data from shipborne and AUV platforms

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

Risk modeling of autonomous underwater vehicle operation focusing on the human operator

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