Andrew E. Ortega

A unique drone-based system for underwater mapping (bathymetry) was developed at the University of Florida. The system, called the “Bathy-drone”, comprises a drone that drags, via a tether, a small vessel on the water surface in a raster pattern. The vessel is equipped with a recreational commercial off-the-shelf (COTS) sonar unit that has down-scan, side-scan, and chirp capabilities and logs GPS-referenced sonar data onboard or transmitted in real time with a telemetry link. Data can then be retrieved post mission and plotted in various ways. The system provides both isobaths and contours of bottom hardness. Extensive testing of the system was conducted on a 5 acre pond located at the University of Florida Plant Science and Education Unit in Citra, FL. Prior to performing scans of the pond, ground-truth data were acquired with an RTK GNSS unit on a pole to precisely measure the location of the bottom at over 300 locations. An assessment of the accuracy and resolution of the system was performed by comparison to the ground-truth data. The pond ground truth had an average depth of 2.30 m while the Bathy-drone measured an average 21.6 cm deeper than the ground truth, repeatable to within 2.6 cm. The results justify integration of RTK and IMU corrections. During testing, it was found that there are numerous advantages of the Bathy-drone system compared to conventional methods including ease of implementation and the ability to initiate surveys from the land by flying the system to the water or placing the platform in the water. The system is also inexpensive, lightweight, and low-volume, thus making transport convenient. The Bathy-drone can collect data at speeds of 0–24 km/h (0–15 mph) and, thus, can be used in waters with swift currents. Additionally, there are no propellers or control surfaces underwater; hence, the vessel does not tend to snag on floating vegetation and can be dragged over sandbars. An area of more than 10 acres was surveyed using the Bathy-drone in one battery charge and in less than 25 min.

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAE_VH = 12.6 cm and RMSE_VH = 17.5 cm; density: MAE_VD = 6.9 stems m⁻² and RMSE_VD = 9.4 stems m⁻²) and morphology (MAE_M = 4.2 cm; RMSE_M = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAE_VH = 31.1 cm; RMSE_VH = 38.1 cm; MAE_VD = 12.7 stems m⁻²; RMSE_VD = 16.6 stems m⁻²; MAE_M = 11.3 cm; RMSE_M = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAE_VH = 6.9 cm; RMSE_VH = 9.4 cm; MAE_VD = 10.0 stems m⁻²; RMSE_VD = 14.0 stems m⁻²). However, it improves when used together with the DAP-UAV point cloud (MAE_VH = 21.7 cm; RMSE_VH = 25.8 cm; MAE_VD = 15.2 stems m⁻²; RMSE_VD = 18.7 stems m⁻²). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

Bridge inspections are an important procedure for maintaining the infrastructure vital to our economy and well-being. The current methodology of utilizing specialized equipment such as snooper trucks and scaffolding to support manned-inspections poses a significant financial cost, disrupts traffic, and is dangerous to the inspectors and public. The advent of unmanned aerial systems (UAS), more commonly called drones, presents a practical solution that promises reduced cost, enhanced safety, and is significantly less intrusive than previous methodologies. Current limitations in the implementation of UAS include the reliance on a skilled operator and/or the requirement for a UAS to operate in a cluttered, GPS-denied environment. A solution to these challenges is presented in this paper by utilizing commercial off-the-shelf (COTS) hardware including laser rangefinders, optical flow sensors, and live video telemetry. Included in the system is the obstacle avoidance equipped drone and a ground station intended to be manned by a pilot and bridge inspector. The proposed custom-fabricated UAS was implemented during eight inspections of Florida Department of Transportation (FDOT) bridges. The UAS was able to navigate under GPS-denied and obstacle-laden bridge decks with position-hold performance comparable to, if not better than, a COTS unit in an unobstructed environment. The position hold capability maintained an altitude of ±12.8 cm with a horizontal hold of ±435 cm. Details of the hardware, algorithm development, and suggestions for future research are discussed in this paper.

Bridge inspections are important to proactively ensure that structures are safe for use by the traveling public. Accessibility challenges during inspections often necessitate the use of special equipment, such as under-bridge inspection vehicles, which may increase both cost and risk of inspections. Under-bridge inspection vehicles, however, cannot be used on all structures, such as load restricted structures. The objective of this research project is to develop and demonstrate a small unmanned aerial vehicle (sUAV) that can be used to perform bridge inspections that would typically require the use of under-bridge inspection vehicles. The proposed sUAV system builds off of previously conducted studies, which have faced challenges with insufficient GPS signal to fly underneath a bridge. By combining information from an optical flow sensor and a range-finding sensor, the proposed system offers a method of safely and efficiently operating underneath a bridge where a GPS signal may be denied. Additionally, the sUAV can capture images of the undersides of bridges using a top-mounted gimbal and a high-resolution camera. This paper provides an account of the methods and results of several demonstration sUAV bridge inspections. It then discusses the advantages and ongoing challenges associated with using sUAV systems to supplement bridge inspections. Finally, recommended applications for sUAV bridge inspections are provided, including periodically replacing the use of under-bridge inspection vehicles for inspections on bridges in good condition, and inspecting structures with load restrictions, especially post-impact, which may prohibit the use of under-bridge inspection vehicles.