Peter Ifju

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.

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

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.

Machining is among the most versatile material removal processes in the manufacturing industry. To better optimize the machining process, the knowledge of shear strains and shear strain rates within the primary shear zone (PSZ) during chip formation has been of great interest. The objective of this study is to study the strain and strain rate progression within the PSZ both in the chip flow direction and along the thickness direction during machining equal channel angular extrusion (ECAE) processed titanium (Ti). ECAE-processed ultrafine-grained Ti has been machined at cutting speeds of 0.1 and 0.5 m/s, and the shear strain and the shear strain rate have been determined using high speed imaging and digital image correlation (DIC). It is found that the chip morphology is saw-tooth at 0.1 m/s while continuous at 0.5 m/s. The cumulative shear strain and the incremental shear strain rate of the saw-tooth chip morphology can reach approximately 3.9 and 2.4 × 103 s−1, respectively, and those of the continuous chip morphology may be approximately 1.3 and 5.0 × 103 s−1, respectively. There is a distinct peak shift in the shear strain rate distribution during saw-tooth chip formation while there is a stable peak position of the strain rate distribution during continuous chip formation. The PSZ thickness during saw-tooth chip formation is more localized and smaller than that during continuous chip formation (28 versus 35 μm).

Purpose: This paper aims to present the methodology and results of the experimental characterization of three-dimensional (3D) printed acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) parts utilizing digital image correlation (DIC). Design/methodology/approach: Tensile and shear characterizations of ABS and PC 3D-printed parts were performed to determine the extent of anisotropy present in 3D-printed materials. Specimens were printed with varying raster ([+45/−45], [+30/−60], [+15/−75] and [0/90]) and build orientations (flat, on-edge and up-right) to determine the directional properties of the materials. Tensile and Iosipescu shear specimens were printed and loaded in a universal testing machine utilizing two-dimensional (2D) DIC to measure strain. The Poisson’s ratio, Young’s modulus, offset yield strength, tensile strength at yield, elongation at break, tensile stress at break and strain energy density were gathered for each tensile orientation combination. Shear modulus, offset yield strength and shear strength at yield values were collected for each shear combination. Findings: Results indicated that raster and build orientations had negligible effects on the Young’s modulus or Poisson’s ratio in ABS tensile specimens. Shear modulus and shear offset yield strength varied by up to 33 per cent in ABS specimens, signifying that tensile properties are not indicative of shear properties. Raster orientation in the flat build samples reveals anisotropic behavior in PC specimens as the moduli and strengths varied by up to 20 per cent. Similar variations were observed in shear for PC. Changing the build orientation of PC specimens appeared to reveal a similar magnitude of variation in material properties. Originality/value: This article tests tensile and shear specimens utilizing DIC, which has not been employed previously with 3D-printed specimens. The extensive shear testing conducted in this paper has not been previously attempted, and the results indicate the need for shear testing to understand the 3D-printed material behavior fully.