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Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers include autonomous navigation, knowledge extraction, motion control systems, sensing, multi-robot collaboration, safety implications, and data transmission. The findings of this study provide insight into the recent research and developments in the field of robotic inspection and monitoring of the built environment and will benefit researchers, and construction and facility managers, in developing and implementing new robotic solutions.
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Eye in the sky: condition monitoring of transportation infrastructure using drones
A study was undertaken into using unmanned aerial vehicles or drones to inspect the condition of a range of transport infrastructure. A road intersection, bridge and railway crossing in the USA were each inspected using two different types of drone. Machine-learning-based feature-identification techniques, developed in an earlier case study of a car parking lot, were then used to extract information automatically from the remotely captured photogrammetric data for each asset. The findings and analysis results will help to optimise future transportation infrastructure health monitoring using unmanned aerial vehicles.
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- Award ID(s):
- 2017796
- PAR ID:
- 10380409
- Date Published:
- Journal Name:
- Proceedings of the Institution of Civil Engineers - Civil Engineering
- ISSN:
- 0965-089X
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1680/jcien.22.00096
