In order to minimize the costs, risks, and disruptions associated with structural inspections, robotic systems have increasingly been studied as an enhancement to current inspection practices. Combined with the increasing variety of commercially available robots, the last two decades have seen dramatic growth in the application of such systems. The use of these systems spans the breadth of civil infrastructure works, and the variety of implemented robotic systems is growing rapidly. However, the highly interdisciplinary nature of research in this field means that results are disseminated across a broad variety of publications. This review paper aggregates these studies in an effort to distill the state of the art in inspection robotics, as well as to assess outstanding challenges in the field and possibilities for the future. Overall, analysis of these studies illustrates that the design of inspection robots is often a case-specific compromise between competing needs for sophisticated inspection sensing and for flexible locomotion in challenging field environments. This review also points toward the growing use of robots as a platform to deploy advanced nondestructive evaluation (NDE) technologies, as well as the expanded use of commercially available robotic systems. Two key outstanding challenges for future researchers are suggested as well. The first is the need for more sophisticated, and inspection-driven, robot autonomy. The other is the need to process and manipulate the massive data sets that modern robots generate.

Unmanned aerial vehicles (UAV) are now a viable option for augmenting bridge inspections. Utilising an integrated combination of a UAV and computer vision can decrease costs, expedite inspections and facilitate bridge access. Any such inspection must consider the design of the UAV, the choice of cameras, data acquisition, geometrical resolution, safety regulations and pilot protocols. The Placer River Trail Bridge in Alaska recently served as a test bed for a UAV inspection methodology that integrates these considerations. The end goal was to produce a three-dimensional (3D) model of the bridge using UAV-captured images and a hierarchical Dense Structure-from-Motion algorithm. To maximise the quality of the model and its benefits to inspectors, this goal guided UAV design and mission planning. The resulting inspection methodology integrates UAV design, data capture and data analysis together to provide an optimised 3D model. This model provides inspection documentation while enabling the monitoring of defects. The developed methodology is presented herein, as well as analyses of the 3D models. The results are compared against models generated through laser scanning. The findings demonstrate that the UAV inspection methodology provided superior 3D models with the accuracy to resolve defects and support the needs of infrastructure managers.

Dams are a critical infrastructure system for many communities, but they are also one of the most challenging to inspect. Dams are typically very large and complex structures, and the result is that inspections are often time-intensive and require expensive, specialized equipment and training to provide inspectors with comprehensive access to the structure. The scale and nature of dam inspections also introduce additional safety risks to the inspectors. Unmanned aerial vehicles (UAV) have the potential to address many of these challenges, particularly when used as a data acquisition platform for photogrammetric three-dimensional (3D) reconstruction and analysis, though the nature of both UAV and modern photogrammetric methods necessitates careful planning and coordination for integration. This paper presents a case study on one such integration at the Brighton Dam, a large-scale concrete gravity dam in Maryland, USA. A combination of multiple UAV platforms and multi-scale photogrammetry was used to create two comprehensive and high-resolution 3D point clouds of the dam and surrounding environment at intervals. These models were then assessed for their overall quality, as well as their ability to resolve flaws and defects that were artificially applied to the structure between inspection intervals. The results indicate that the integrated process is capable of generating models that accurately render a variety of defect types with sub-millimeter accuracy. Recommendations for mission planning and imaging specifications are provided as well.