Methods of Autonomous Underwater Vehicles Positioning

Sigiel, Norbert

doi:10.2478/sjpna-2019-0003

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…It receives an initial GNSS position when the sensor is at the sea surface and, based on the motion of the platform, computes the position relative to the initial value for the duration the survey platform is submerged. More detailed information about underwater systems and their accuracies can be found in [26][27][28] [29].…”

Section: ) Positioning Systemsmentioning

confidence: 99%

Offshore Unexploded Ordnance Detection and Data Quality Control—A Guideline

Wehner¹,

Frey

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The detection of old submerged ammunition that is located on or below the seabed is important preparatory work for offshore projects. Multibeam echosounder, side scan sonar, subbottom profiler and magnetometers are the sensor types that are most commonly used for the task. Survey design decisions center around both detection campaign efficiency and sufficient data quality to find specific reference objects. The paper presents a comprehensive workflow for unexploded ordnance surveys in the sea and focuses on aspects of quantitative data quality control. For this purpose, data quality factors and corresponding threshold values were developed for each of the main sensor types. The authors designed and moderated a workshop-based stakeholder engagement process to establish expert consensus on suitable data quality factors and appropriate calculation of thresholds that define which data are sufficient and which are not. This approach was accompanied by a literature review of existing guidelines, standards, and survey recommendations. A quantitative description of data quality eases analyzing and comparing newly acquired and existing data. The paper presents the results of this process, thereby providing guidance for the planning, execution, and quality control of a technical investigation with the aim of finding unexploded ordnance in the sea. The defined data quality factors derived from the literature review and collective expert knowledge can be used in the suggested workflow.

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Section: ) Positioning Systemsmentioning

confidence: 99%

Offshore Unexploded Ordnance Detection and Data Quality Control—A Guideline

Wehner¹,

Frey

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Some other attempts involve cable laying on the seabed, 14,15 oceanographic research, 16,17 marine archeology, 18,19 and inspection of underwater structures. 20,21 The harsh environment in which they are designed to operate includes great difficulties in communication, [22][23][24] navigation, [25][26][27] material corrosion etc. Difficulties of these types demand high-cost solutions that contradict with the lowcost approach of the Synoris platform.…”

Section: Introductionmentioning

confidence: 99%

Autonomous underwater vehicle challenge: design and construction of a medium-sized, AI-enabled low-cost prototype

Paraschos

Papadakis

2021

Journal of Defense Modeling & Simulation

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The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities.

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Methods of Autonomous Underwater Vehicles Positioning

Abstract: Article presented below is a prelude to the process of autonomous underwater systems positioning. An article include description of positioning methods and guidelines for the operators of autonomous underwater vehicles (AUVs), concerning above mentioned process.

Cited by 2 publications

References 3 publications

Offshore Unexploded Ordnance Detection and Data Quality Control—A Guideline

Offshore Unexploded Ordnance Detection and Data Quality Control—A Guideline

Autonomous underwater vehicle challenge: design and construction of a medium-sized, AI-enabled low-cost prototype

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