Field Validation of an Advanced Autonomous Method of Exterior Dam Inspection Using Unmanned Aerial Vehicles

Field Validation of an Advanced Autonomous Method of Exterior Dam Inspection Using Unmanned Aerial Vehicles Benjamin Joseph Barrett Department of Civil and Environmental Engineering, BYU Master of Science The maintenance of infrastructure is critical to the well-being of society. This work focuses on a novel method for inspecting the exterior of dams using unmanned aerial vehicles (UAVs) in an automated fashion. The UAVs are equipped with optical sensors capturing still images. The resulting images are used to generate three-dimensional (3D) models using Structure from Motion (SfM) computer software. The SfM models are then used to inspect the exterior of the dam. As typical dam inspections entail completing a checklist of inspection items with varied degrees of precision (e.g. a concrete spillway may be finely inspected for cracking or joint deterioration while the general stability and water-tightness of a large embankment may be observed from a distance), a targeted inspection is also needed for the UAV method. In conjunction with the work presented in this thesis, a novel algorithm was developed which uses camera view planning across multiple proximity levels to generate a set of camera poses (positions and orientations) which can be collected in an autonomous UAV flight that facilitates generation of SfM models having tiered model quality for targeted inspection of infrastructure features. In this thesis, this novel algorithm and accompanying mobile application (referred to together as the novel advanced autonomous method) were field validated at Tibble Fork Dam, UT. The advanced autonomous method was compared to two other common image acquisition methods—basic autonomous and manual piloted—based on the SfM models produced from the collected image sets. The advanced autonomous method was found to produce models having tiered quality needed for efficient targeted inspection (25% and 50% higher resolution in medium and high priority target areas). The advanced autonomous method was found to produce models having on average 38% higher precise point accuracy (1.3cm) and 53% tighter surface reproducibility (for repeat inspections) (1.9cm) than basic autonomous and manual piloted image acquisition methods. The advanced autonomous method required on average 167% longer flight time and 38% fewer images than the other two methods, resulting in increased field time but decreased processing load. Additionally, viability of the advanced autonomous method for practical dam inspection was assessed through a case study inspection of Tibble Fork Dam using the collected SfM model and corresponding still images. The SfM model and corresponding images were found fully adequate for performing 94% of the inspection tasks and partially adequate for the remaining tasks. In consideration of this and other practical implementation factors such as time and safety, the method appears highly viable as an alternate to or supplement with traditional on-foot visual exterior inspection of dams such as Tibble Fork Dam. Suggestions for future work include adjustments to the optimization framework to improve field efficiency, development of a framework for cooperative inspection using UAV swarms, and development of a more automated workflow that would allow fully-remote dam inspections.