A Mobile Robot for Automated Civil Infrastructure Inspection and Evaluation

Nguyen, Luan Van; Gibb, Spencer; Pham, Huy Xuan; La, Hung Manh

doi:10.1109/ssrr.2018.8468642

Cited by 6 publications

(9 citation statements)

References 16 publications

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“…This particular robotic platform has been equipped with state-of-the-art sensor technologies (e.g., impact echo, ultrasonic surface waves, electrical resistivity and GPR), which enable the classification of some of the most common defects in bridge decks, such as concrete degradation, delamination and rebar corrosion [12,47]. The ARA Lab Robot is also a wheel-based robotic platform that has been recently developed for bridge deck inspection and maintenance [37,50,90,91]. With a similar array of sensors, another autonomous platform for infrastructural inspection was developed by La et al [8], which provided a wide array of different functionalities related to the automated monitoring of civil infrastructure, using on-surface crack detection and bridge evaluation for signs of deterioration within the metal rebar and concrete slabs.…”

Section: Ground Robotsmentioning

confidence: 99%

“…A half-cell potential sensor was used by the ETH (Eidgenössische Technische Hochschule) Zurich autonomous robot for potential mapping to detect a level of corrosion within the concrete structures (e.g., bridge deck underside and parking lots) [59]. Electrical Resistivity (ER) probes have been one of the most widely used electrical sensors, which have been incorporated within two of the most widely discussed ground robots for the SHM of bridge decks in the recent past, namely the ARA Lab Robot [37,50,90] and RABIT platforms [8,91,100,101]. The purpose of ER probes is to examine the level of sub-surface corrosion within bridge decks and other infrastructures [79].…”

Section: Single Sensor Systemsmentioning

confidence: 99%

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Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

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Section: Ground Robotsmentioning

confidence: 99%

Section: Single Sensor Systemsmentioning

confidence: 99%

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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“…Crack detection in the frequency domain using the Gaussian filter resulted in the median number of TPs (40) and the second lowest FP (12%). The accuracy (84%) was also near the median value, but the precision (87%) was the second highest.…”

Section: Frequency Domain Gaussian Filtermentioning

confidence: 99%

“…Crack detection in the frequency domain using the Butterworth filter resulted in the median number of TPs (40) and the median FP (7). The accuracy (83%) and precision (85%) were also near the median of the six methods evaluated.…”

Section: Frequency Domain Butterworth Filtermentioning

confidence: 99%

“…Crack detection algorithms can emphasize edges by applying filters in either the spatial or frequency domain. Even though use of edge detectors for crack detection goes back to the early 2000s [29], these methods have been used in the past and are still being used in recent studies because of their simplicity and pixel-based detection of cracks [15,28,[30][31][32][33][34][35][36][37][38][39][40][41][42]. Even in emerging applications of supervised machine learning methods, edge detectors are still considered in practice since they do not require expensive annotated training datasets.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

2019

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This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

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A Climbing Robot for Steel Bridge Inspection

Nguyen

2021

J Intell Robot Syst

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A Mobile Robot for Automated Civil Infrastructure Inspection and Evaluation

Cited by 6 publications

References 16 publications

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

A Climbing Robot for Steel Bridge Inspection

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