On the use of robots and vision technologies for the inspection of vessels: A survey on recent advances

Bonnín-Pascual, Francisco; Ortiz, Alberto

doi:10.1016/j.oceaneng.2019.106420

Cited by 44 publications

(17 citation statements)

References 127 publications

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“…The aforementioned works consider, among others, power plant boilers and chimneys [4][5][6], dam walls and penstocks [7,8], bridges [9][10][11], power lines [12][13][14], wind turbines [15,16], mines and tunnels [17,18], petrochemical facilities [19,20], and large-tonnage vessels [21,22]. A survey on existing UAS for the inspection of varied infrastructures can be found in [23].…”

Section: Platform Requirements and Related Workmentioning

confidence: 99%

MUSSOL: A Micro-Uas to Survey Ship Cargo hOLds

Bonnín-Pascual¹,

Garcia-Fidalgo²,

Company-Corcoles³

et al. 2021

Remote Sensing

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Because of their high maneuverability and fast deployment times, aerial robots have recently gained popularity for automating inspection tasks. In this paper, we address the visual inspection of vessel cargo holds, aiming at safer, cost-efficient and more intensive visual inspections of ships by means of a multirotor-type platform. To this end, the vehicle is equipped with a sensor suite able to supply the surveyor with imagery from relevant areas, while the control software is supporting the operator during flight with enhanced functionalities and reliable autonomy. All this has been accomplished in the context of the supervised autonomy (SA) paradigm, by means of extensive use of behaviour-based high-level control (including obstacle detection and collision prevention), all specifically devised for visual inspection. The full system has been evaluated both in laboratory and in real environments, on-board two different vessels. Results show the vehicle effective for the referred application, in particular due to the inspection-oriented capabilities it has been fitted with.

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Section: Platform Requirements and Related Workmentioning

confidence: 99%

MUSSOL: A Micro-Uas to Survey Ship Cargo hOLds

Bonnín-Pascual¹,

Garcia-Fidalgo²,

Company-Corcoles³

et al. 2021

Remote Sensing

Self Cite

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“…• Permite la combinación de tareas parcialmente ordenadas mediante el uso de la palabra clave. El desarrollo de los servicios web implementados en la práctica se divide entre el robot de caracterización de los huecos y el robot encargado de reparar las vías [2], estos se explican a continuación:…”

Section: 4unclassified

Mantenimiento y reparación de vías usando un sistema robótico en la nube

et al. 2020

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La revolución del internet de las cosas - IoT, genera nuevos retos para el campo de la robótica; Para la construcción de las aplicaciones y comunicación de los sistemas robóticos, con la nube, fueron creados protocolos y conceptos, que facilitan y estandarizan la interconexión, algunos de estos conceptos son: servicios web, programación XML, sistema WS-BPEL, Arquitectura Orientada Servicio, Big data y computación en la nube. Este artículo, muestra una aplicación específica de la integración del concepto de IoT con la robótica, representado por un sistema de reparación y mantenimiento de vías autónomo, con un sistema robótico en la nube, el cual utiliza servicios web y protocolos de comunicación como XML y BPEL para la interconexión entre la información necesaria para la reparación y mantenimiento de las vías, usados en la toma de decisiones y tareas asignadas entre los robots. De esta manera, se demuestra, cómo es posible articular todos los conceptos, para lograr soluciones en un escenario real de reparación de vías.

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“…Corrosion has been widely investigated in many areas involving materials science, including maritime vessels [1], steel pipelines [2], and concrete and steel damage [3]. Recent studies estimated that the total cost of corrosion maintenance worldwide amounts to roughly 2.5 trillion US dollars per year [4,5].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Fuselage Corrosion Detection Using Artificial Intelligence

Brandoli

Geus

Souza

et al. 2021

Sensors

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Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.

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On the use of robots and vision technologies for the inspection of vessels: A survey on recent advances

Cited by 44 publications

References 127 publications

MUSSOL: A Micro-Uas to Survey Ship Cargo hOLds

MUSSOL: A Micro-Uas to Survey Ship Cargo hOLds

Mantenimiento y reparación de vías usando un sistema robótico en la nube

Aircraft Fuselage Corrosion Detection Using Artificial Intelligence

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