Search NASA⌕ Search

SEARCH · Search NASA

Results for “Unmanned Aircraft System (UAS)”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

Unmanned Aircraft Systems (UAS) and Light Detection and Ranging (LiDAR)/Camera Technologies to Detect Avian Events and Other Environmental Measures at Utility- Scale Power Plants (Final Report)

The goal of this project was to develop and validate two complementary, cost-effective remote sensing technologies to monitor avian fatalities at utility-scale solar facilities: fixed platform (Animal Activity Monitoring-AAM) and aerial-based (Uncrewed Aircraft Systems-UAS). This project used these features with machine learning to automate the detection of avian carcasses and nests at solar facilities.

14 SOLAR ENERGY↗

Inspection and Mapping of Savannah River Site (SRS) Waste Tanks via Unmanned Aircraft System (UAS) – 25351

The CSTF at SRS contain 51 waste tanks with 8 closed waste tanks between FTF and HTF. SRMC is the LW contractor. The LW mission includes removing legacy nuclear waste from these tanks and treating it for final disposition. Once the bulk of the waste has been removed from a tank, it will undergo inspection and sampling to characterize the remaining waste in the tank prior to it being operationally closed. There are multiple points in the tank closure process where an inspection is performed, and there are multiple parts of a tank that get inspected. Waste tanks have a primary containment vessel (referred to as the “Primary”) and a secondary containment vessel (referred to as the “Annulus”) that surrounds the primary. Both of these sections of a tank receive multiple inspections throughout the closure process.

Murphy, Lucas D. [Savannah River Mission Completio↗

National Laboratories for Environmental Management and Stewardship (NNLEMS) National Lab Capabilities in Unmanned Aerial Systems (UAS) (Revision 1)

The Network of National Laboratories for Environmental Management and Stewardship (NNLEMS) formed an Unoccupied Aircraft Systems (UAS) topical team in spring 2025 for the purpose of documenting the capabilities of the National Laboratories relevant to the goals and needs of the Department of Energy (DOE) Office of Legacy Management (LM). The team was comprised of representatives from eight National Laboratories (Table 1), thereby bringing diverse skillsets from across the DOE complex. Recognizing that LM has extensive experience working with UAS contractors and using data collected from UAS, the topical team focused on the National Laboratories’ unique capabilities and types of scientific investigations that are not yet commercially available or easily contracted as services.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Measurements of TRACER pre-convective conditions and mesoscale circulations using small unmanned aircraft systems (sUAS)

Improved comprehension of the physical processes governing convective cloud formation and lifecycle are of critical importance for understanding and predicting future climate states. The influence of these clouds on the planetary energy budget, including on precipitation, is significant. Things are particularly complex in coastal regimes, where gradients in aerosol particle properties, localized circulations such as sea breezes, and large population centers are found. To date, numerical models struggle to accurately represent these critical clouds and are therefore challenged to provide a realistic view on the planetary energy budget. Through the proposed research, we deployed two uncrewed aircraft systems (UAS) equipped with a variety of instruments alongside sensors deployed by the US Department of Energy Atmospheric Radiation Measurement (ARM) program for the TRACER (Tracking Aerosol Convection Interactions Experiment) field campaign. The two small UAS platforms consisted of a CU RAAVEN fixed-wing airplane and an OU CopterSonde system, with the copter collecting frequent vertical profiles of thermodynamic and kinematic variables such as temperature, pressure, wind and humidity. At the same time, the fixed-wing captured horizontal gradients of these quantities and aerosol size distribution. These systems were deployed south of the Houston metro area, in an area that is impacted by the Gulf of Mexico sea breeze on a daily basis. These observations offer enhanced and complementary perspectives to those provided by the DOE ARM Mobile Facility (AMF) which is was deployed in southeast Houston, and an ancillary site in a more rural location west of the urban Houston area. Quality-controlled versions of the UAS data were collected and posted on the DOE ARM data archive after the conclusion of the campaign where they are accessible by the research community and general public. The UAS perspective offers revolutionary insight into key spatial and temporal effects that have not been evaluated previously.

54 ENVIRONMENTAL SCIENCES↗

Quantifying Operational Drivers of Multimodal Biometric Verification in Aerial Surveillance

Multimodal biometric verification is increasingly applied across operational contexts ranging from close-range security cameras and building-mounted surveillance to long-range ground sensors and unmanned aerial system (UAS) imagery. Variations in acquisition conditions—such as image resolution, viewing geometry, and motion artifacts—pose significant challenges for cross-domain algorithmic generalization. This study evaluates two independent multimodal biometric verification systems developed under the Intelligence Advanced Research Projects Activity (IARPA) Biometric Recognition and Identification at Altitude and Range (BRIAR) program, comparing performance on close-range and aerial datasets. Close-range video served as a baseline to quantify the decline in verification performance on aerial footage. The dataset included six UAS platforms, spanning small quadcopters at 10m altitude to medium-sized fixed-wing aircraft at 360m. Mixed-effects logistic regression identified image resolution (head and body pixel counts), head height, sensor characteristics, and algorithm selection as primary determinants of verification success, whereas demographic attributes and mission gait were not significant predictors. Activity type and collection site influenced performance in close-range data but had negligible impact on UAS imagery. These results clarify modality-specific strengths and limitations and highlight opportunities to enhance cross-domain biometric verification.

Peluso, Alina [ORNL] (ORCID:0000000328950406)↗