Impact of UAV Hardware Options on Bridge Inspection Mission Capabilities

Abstract

Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements.

Autonomous Drone Racing: A Survey

“…Automating inspection tasks can save lives while being more productive than manual inspection. According to a recent survey on unmanned aerial vehicle (UAV) use in bridge inspection [5], most drones used for inspection tasks rely on GPS navigation with the biggest limiting factor on inspection efficiency being the drones endurance and mobility. Additionally, the authors note that the most popular drones used for surveying by several US Departments of Transportation are not fully autonomous and require expert human pilots [5].…”

“…According to a recent survey on unmanned aerial vehicle (UAV) use in bridge inspection [5], most drones used for inspection tasks rely on GPS navigation with the biggest limiting factor on inspection efficiency being the drones endurance and mobility. Additionally, the authors note that the most popular drones used for surveying by several US Departments of Transportation are not fully autonomous and require expert human pilots [5]. The commercial and safety advantages of highly agile drone systems is clear, however research into autonomous drone racing can also help us gain new understandings on how the visual processing and control by human pilots works, as demonstrated in [6].…”

“…Similar to the authors in [168], the authors of this dataset hope to push the state of the art in state estimation during aggressive motion and have created competitions to allow researchers to compete against one another on this agile flight benchmark. 5 Research on how expert human pilots focus on their targets during flying and provide a dataset that contains flight trajectories, videos, and data from the pilots is examined in [6] NeuroBEM [29] is a hybrid aerodynamic quadrotor model which combines blade-element-momentum-theory models with learned aerodynamic representations from highly aggressive maneuvers. While the model is fit to the specific quadrotor platform defined in [162], the approach can be used for any quadrotor platform and provides over 50% reduction in model prediction errors compared to traditional, exclusivelyfirst-principles approaches.…”

Review of artificial intelligence-based bridge damage detection

“…In addition, infrared acquisition instruments are easy to install, for example infrared cameras can be mounted on UAVs for quick and easy thermal image acquisition. 96,97 Pozzer et al 98 used deep learning to identify delamination, cracks, spalling and patches in conventional images and thermal imaging respectively. Data test results from a dam and two concrete bridge decks showed that MobileNetV2 performs well in identifying multiple types of damage in thermal images, identifying 79.7% of damage.…”

Stackelberg Game Approach for Service Selection in UAV Networks

“…A typical UAV is made up of a frame, motors, control unit, onboard sensors, communication system, and power supply. Furthermore, many UAVs have a dual tube substructure to allow for the installation of various payloads [7].…”