Autonomous robotic system for bridge deck data collection and analysis

La, Hung Manh; Gucunski, Nenad; Kee, Seong‐Hoon; Yi, Jingang; Senlet, Turgay; Nguyen, Luan Van

doi:10.1109/iros.2014.6942821

Cited by 49 publications

(37 citation statements)

References 10 publications

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“…1. The robot autonomously maneuvers on the bridge based on the advanced localization and navigation algorithm reported in our previous works [8]- [10]. The NDE data collection system is run on two Windows operating computers and communicate with the robot Linux operating computer through the serial communication protocols.…”

Section: The Bridge Robotic Inspection System a Data Collectionmentioning

confidence: 99%

Visual and Acoustic Data Analysis for the Bridge Deck Inspection Robotic System

La¹,

Gucunski

Kee

et al. 2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Bridge deck inspection is essential task to monitor the health of the bridges. This paper reports the data collection and analysis for bridge decks based on our novel robotic system which can autonomously and accurately navigate on the bridge. The developed robotic system can lessen the cost and time of the bridge deck data collection and risks of human inspections. The advanced software is developed to allow the robot to collect visual images and conduct nondestructive evaluation (NDE) measurements. The image stitching algorithm to build a whole bridge image from individual images is presented in detail. The impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the delamination and concrete elastic modulus maps of the deck.

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Section: The Bridge Robotic Inspection System a Data Collectionmentioning

confidence: 99%

Visual and Acoustic Data Analysis for the Bridge Deck Inspection Robotic System

La¹,

Gucunski

Kee

et al. 2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…References [6,7] were focused in finding sound features enabling differentiation between defective and non-defective spots as well as on the exploration of new methods to replace or aid the human operator holding the hammer in order to get more regular and reliable sound samples. References [8][9][10][11] were more focused on the data analysis part of the problem and used supervised learning to correctly distinguish sounds from non-defective areas and sounds from defective areas.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning approach to automation of hammering test using topological information

et al. 2017

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In this paper we present an online unsupervised method based on clustering to find defects in concrete structures using hammering. First, the initial dataset of sound samples is roughly clustered using the k-means algorithm with the k-means++ seeding procedure in order to find the cluster best representative of the structure. Then the regular model for the hammering sound, the centroid of this cluster, which is assumed to be the non-defective sound model, is established and finally used as a reference to conduct diagnosis on the whole dataset. During the model generation phase, topological information on the spatial distribution of samples is used to attribute varying importance to each sample and therefore take into account meticulous diagnosis of certain areas. The algorithm is fast and reliable enough to allow efficient diagnosis by running it each time a new sample is acquired. Tests on two commonly found types of defects, namely delamination and void type defects, were conducted on experimental test blocks and yielded satisfying results. This method also performed well in field environments.

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“…Ultrasonic nondestructive evaluation (NDE) has been widely used in quality assessment and failure analysis for critical structures or components in manufacturing, bridge structure, microelectronic packaging, and composite materials for aircraft structure [1][2][3][4]. The NDE system is commonly designed on microcontrollers and digital signal processors, which fall short of meeting the demands of high speed and requirements of adaptability.…”

Section: Introductionmentioning

confidence: 99%