A comprehensive study on development of a small-sized self-propelled robot for bridge inspection

Akutsu, Ayako; Sasaki, Eiichi; Takeya, Koichi; Kobayashi, Yusuke; Suzuki, Keigo; Tamura, Hiroshi

doi:10.1080/15732479.2016.1236132

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…To enable robot movement along vertical surfaces, it is also necessary to ensure a robust adhesion system, i.e., a system that will enable adhering onto a vertical surface. Five main adhesion types have been described in the literature [24][25][26][27]:…”

Section: Challenges In the Development Of The Autonomous System Rokomentioning

confidence: 99%

Development of an autonomous structural integrity assessment and prediction system

2022

JCE

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Development of innovative solutions for the maintenance of transport infrastructure facilities is needed in order to ensure a more rational, planned and lower-cost maintenance of transport infrastructure, and to ultimately minimise the risk of catastrophic consequences. A system for an autonomous inspection of structures, based on advanced measuring methods integrated on a wall-climbing robot and an unmanned aerial vehicle, is currently developed in the scope of the ASAP project. The objective of this paper is to provide an overview and draw attention to disadvantages of conventional methods for testing materials and structures in order to assess their condition. This objective was the main motivation for forming a multidisciplinary team through the ASAP project. Possibilities and challenges in the development of an autonomous structural-assessment system are also presented in the paper, with the purpose of increasing the reliability and efficiency of systemic assessment of structures.

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Section: Challenges In the Development Of The Autonomous System Rokomentioning

confidence: 99%

Development of an autonomous structural integrity assessment and prediction system

2022

JCE

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“…In terms of the vision inspection method for surface defects of cables of a bridge, Akutsu et al [22,23] designed a semiautomatic robot to detect, measure, and annotate the defects of a bridge by combining data with different types of software such as computer aided design (CAD) routines. Li et al [24,25] developed a vision-based distributed detection system to detect surface damage to cables and proposed the use of the effective scale-invariant feature transform (SIFT) algorithm to realize the mosaicking of multiple images with partially overlapped areas in different defect images.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision

Fei¹,

Kalantari²,

Li³

et al. 2023

Computers, Materials &Amp; Continua

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The automatically defect detection method using vision inspection is a promising direction. In this paper, an efficient defect detection method for detecting surface damage to cables on a cable-stayed bridge automatically is developed. A mechanism design method for the protective layer of cables of a bridge based on vision inspection and diameter measurement is proposed by combining computer vision and diameter measurement techniques. A detection system for the surface damages of cables is de-signed. Images of cable surfaces are then enhanced and subjected to threshold segmentation by utilizing the improved local grey contrast enhancement method and the improved maximum correlation method. Afterwards, the data obtained through diameter measurement are mined by employing the moving average method. Image enhancement, threshold segmentation, and diameter measurement methods are separately validated experimentally. The experimental test results show that the system delivers recall ratios for type-I and II surface defects of cables reaching 80.4% and 85.2% respectively, which accurately detects bulges on cable surfaces.

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“…They can be classified based on the operating principle such as stress wave [9]- [14], electrical impedance/resistance [15], structural vibration [16], photographic techniques [17]- [19], strain gauges and fiber optics [20], [21]. Specifically for these problems, photographic techniques is more suitable because this could cover a large area with minimal permanently installed components and could produce a more direct result of the crack parameters (density and average width) [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Application of deep convolutional neural networks for automated and rapid identification and computation of crack statistics of thin cracks in strain hardening cementitious composites (SHCCs)

Das

Leung

Wan³

2021

Cement and Concrete Composites

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A comprehensive study on development of a small-sized self-propelled robot for bridge inspection

Cited by 12 publications

References 14 publications

Development of an autonomous structural integrity assessment and prediction system

Development of an autonomous structural integrity assessment and prediction system

Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision

Application of deep convolutional neural networks for automated and rapid identification and computation of crack statistics of thin cracks in strain hardening cementitious composites (SHCCs)

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