Two years of experiments with the AUV dagon - a versatile vehicle for high precision visual mapping and algorithm evaluation

Hildebrandt, Marc; Gaudig, Christopher; Christensen, Leif; Natarajan, Sankaranarayanan; Paranhos, Patrick Merz; Albiez, Jan

doi:10.1109/auv.2012.6380722

Cited by 20 publications

(17 citation statements)

References 7 publications

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“…The AUV Tri-Dog1 observed tubeworm colonies at the Tagiri vent field in Kagoshima Bay, Japan (Maki et al, 2011a). The AUV Dagon, which is equipped with a graphics processing unit for image processing, was tested in the Baltic Sea (Hildebrandt et al, 2012). Conventional AUVs follow a prespecified path and some parts of the target are often left unobserved because of occlusions, positioning errors, and so on.…”

Section: Introductionmentioning

confidence: 99%

Path Replanning Method for an AUV in Natural Hydrothermal Vent Fields: Toward 3D Imaging of a Hydrothermal Chimney

Sato¹,

Maki²,

Kume³

et al. 2014

mar technol soc j

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A B S T R A C TAutonomous underwater vehicles (AUVs) can operate without the need for human control or tether cables as long as there is sufficient energy. AUVs have recently been used for seafloor imaging. Visual observation by AUVs provides high-resolution color information of the seafloor. However, conventional observation techniques that follow a prespecified path offer limited coverage because it is impossible for operators to build a suitable path in unknown rough terrain. A flawed prespecified path will produce incomplete observation. If unobserved areas are found during postprocessing, another dive is necessary, which increases the total cost. To overcome this problem, the authors have proposed a path replanning method to realize high-coverage observation in one dive. With this method, the AUV evaluates unobserved areas after the first prespecified observation; if unobserved areas are found, the AUV recreates an appropriate path to cover what was missed. The validity of the proposed method was previously evaluated using an artificial target in a tank and in shallow seas at a depth of approximately 35 m. In this study, the feasibility of the method was validated in a more challenging setting: experimental data were taken from a hydrothermal vent field in Kagoshima Bay, Japan.

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Section: Introductionmentioning

confidence: 99%

Path Replanning Method for an AUV in Natural Hydrothermal Vent Fields: Toward 3D Imaging of a Hydrothermal Chimney

Sato¹,

Maki²,

Kume³

et al. 2014

mar technol soc j

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“…This paper addresses the problem of on-line learning of robot dynamic models, where we use an autonomous underwater vehicle (AUV) named "Dagon" as our test subject. This vehicle was developed in the CUSLAM project [7] and has been extensively modified and used in a number of subsequent research activities [8], [9]. This included the addition of a hydrodynamic hull around the pressure compartments, as well as the implementation of multiple additional sensor systems.…”

Section: Introductionmentioning

confidence: 99%

A Framework for On-line Learning of Underwater Vehicles Dynamic Models

Wehbe

Hildebrandt

Kirchner

2019

2019 International Conference on Robotics and Automation (ICRA)

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Learning the dynamics of robots from data can help achieve more accurate tracking controllers, or aid their navigation algorithms. However, when the actual dynamics of the robots change due to external conditions, on-line adaptation of their models is required to maintain high fidelity performance. In this work, a framework for on-line learning of robot dynamics is developed to adapt to such changes. The proposed framework employs an incremental support vector regression method to learn the model sequentially from data streams. In combination with the incremental learning, strategies for including and forgetting data are developed to obtain better generalization over the whole state space. The framework is tested in simulation and real experimental scenarios demonstrating its adaptation capabilities to changes in the robot's dynamics.

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“…Other inspection systems, especially for inspection inside tanks and ballast water tanks, were developed within the ROTIS II project [8]. Recently developed systems, such as the AUV Dagon [9], are usable for the underwater inspection of ship hulls under water and for the propeller section. For dry inspection of cargo holds, climbing robots are the most suitable systems, because they can transport sensors, such as cameras or sonar thickness measurements, close to the inspected area.…”

Section: Introductionmentioning

confidence: 99%

Design and Control of MIRA: A Lightweight Climbing Robot for Ship Inspection

Ahmed

Eich

Bernhard

2015

ILCPA

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ABSTRACT:The inspection of marine vessels is currently per-formed manually. Inspectors use tools (e.g. cameras and devices for non-destructive testing) to detect damaged areas, cracks, and corrosion in large cargo holds, tanks, and other parts of a ship. Due to the size and complex geometry of most ships, ship inspection is time-consuming and expensive. The EU-funded project INCASS develops concepts for a marine inspection robotic assistant system to improve and automate ship inspections. In this paper, we introduce our magnetic wall-climbing robot: Marine Inspection Robotic Assistant (MIRA). This semi-autonomous lightweight system is able to climb a vessels steel frame to deliver on-line visual inspection data. In addition, we describe the design of the robot and its building subsystems as well as its hardware and software components.

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Two years of experiments with the AUV dagon - a versatile vehicle for high precision visual mapping and algorithm evaluation

Cited by 20 publications

References 7 publications

Path Replanning Method for an AUV in Natural Hydrothermal Vent Fields: Toward 3D Imaging of a Hydrothermal Chimney

Path Replanning Method for an AUV in Natural Hydrothermal Vent Fields: Toward 3D Imaging of a Hydrothermal Chimney

A Framework for On-line Learning of Underwater Vehicles Dynamic Models

Design and Control of MIRA: A Lightweight Climbing Robot for Ship Inspection

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