Abstract:In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy's Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE) with a flying height of 344 m above ground level (AGL).

Remote characterization of high radiation environments is a pressing application area where robots can provide benefits in terms of time, cost, safety and quality of data. However, the DOE roadmap for Robotics and Intelligent Machines states that 'usability' may well prove to be the most challenging and yet crucial component of robotic systems for remote characterization and handling of radioactive and hazardous materials. In 2001, the INEEL successfully deployed a teleoperated robotic system coupled with a Gamma Locating and Isotopic Identification Device (RGL&IID) to characterize an area that had been closed to human entry for many years. This paper examines the human-robot dynamic of this teleoperated task and the limitations inherent to the master-slave strategy employed. Next, the paper outlines an innovative, mixed-initiative command and control architecture developed to address these limitations. The resulting, mixed-initiative control architecture retains the human in the loop, but interleaves multiple levels of human intervention into the functioning of a robotic system that can, in turn, scale its own level of initiative to meet whatever level of input is handed down.

The height and shape of shrub canopies are critical measurements for characterizing shrub steppe rangelands. Remote sensing technologies might provide an efficient method to acquire these measurements across large areas. This study compared point-cloud and rasterized lidar data to field-measured sagebrush height and shape to quantify the correlation between field-based and lidar-derived estimates. The results demonstrated that discrete return, small-footprint lidar with high point density (9.46 points/m 2) can provide strong predictions of true sagebrush height (R 2 of 0.84 to 0.86), but with a consistent underestimation of approximately 30 percent. Our results provided the first successful lidar-based descriptors of sagebrush shape with R 2 values of 0.65, 0.74, and 0.78 for respective predictions of shortest canopy diameter, longest canopy diameter, and canopy area. Future studies can extend lidar-derived shrub height and shape measurements to canopy volume, cover, and biomass estimates.

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We study a space-time finite element approach for the nonhomogeneous wave equation using a continuous time Galerkin method. We present fully implicit examples in 1+1, 2+1, and 3+1 dimensions using linear quadrilateral, hexahedral, and tesseractic elements. Krylov solvers with additive Schwarz preconditioning are used for solving the linear system. We introduce a time decomposition strategy in preconditioning which significantly improves performance when compared with unpreconditioned cases.

Aircraft that can launch ballistically and convert to autonomous, free-flying drones have applications in many areas such as emergency response, defense, and space exploration, where they can gather critical situational data using onboard sensors. This paper presents a ballistically-launched, autonomously-stabilizing multirotor prototype (SQUID-Streamlined Quick Unfolding Investigation Drone) with an onboard sensor suite, autonomy pipeline, and passive aerodynamic stability. We demonstrate autonomous transition from passive to vision-based, active stabilization, confirming the multirotor's ability to autonomously stabilize after a ballistic launch in a GPS-denied environment.

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification.The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels.In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and "feathering" areas of flightline overlap. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus).

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