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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

A Multi-Tier Autonomous Aerial Architecture for Wildfire Detection, Characterization, and Communication in Infrastructure-Denied Environments

Wildfire response depends on how fast an ignition can be confirmed and located, especially in remote regions where ground-based communication and monitoring may be limited. Geostationary sensors provide frequent observations but at kilometer-scale resolution, which is too coarse to resolve small fires in remote terrain. Ground camera networks require sightlines and infrastructure that back-country areas lack. To address these limitations, this work proposes a Multi-Tier Autonomous Wildfire Intelligence System that combines wide-area monitoring with targeted, high-resolution sensing. A solar-powered high-altitude long endurance (HALE) platform operating at approximately 60,000 ft provides persistent wide-area thermal and optical surveillance, running onboard edge inference to screen candidate ignitions and reduce false positives and downlink bandwidth. When a candidate ignition is detected, low-altitude uncrewed aircraft systems (UAS) can be deployed to conduct localized observations, including high-resolution imaging and atmospheric measurements such as wind and plume observation. By combining persistent detection with local sensing, the proposed architecture is designed to provide first responders with timely, high-resolution information about fire location and behavior to aid in emergency decision making.

Wildfire management, UAS, drones↗
DOE OSTI2021

Characterizing the performance of a POPS miniaturized optical particle counter when operated on a quadcopter drone

We first validate the performance of the Portable Optical Particle Spectrometer (POPS), a small light-weight and high sensitivity optical particle counter, against a reference scanning mobility particle sizer (SMPS) for a month-long deployment in an environment dominated by biomass burning aerosols. Subsequently, we examine any biases introduced by operating the POPS on a quadcopter drone, a DJI Matrice 200 V2. We report the root mean square difference (RMSD) and mean absolute difference (MAD) in particle number concentrations (PNCs) when mounted on the UAV and operating on the ground and when hovering at 10 m. When wind speeds are low (less than 2.6 m s –1 ), we find only modest differences in the RMSDs and MADs of 5 % and 3 % when operating at 10 m altitude. When wind speeds are between 2.6 and 7.7 m s –1 the RMSDs and MADs increase to 26.2 % and 19.1 %, respectively, when operating at 10m altitude. No statistical difference in PNCs was detected when operating on the UAV in either ascent or descent. We also find size distributions of aerosols in the accumulation mode (defined by diameter, d, where 0.1 ≤ d ≤ 1 µm) are relatively consistent between measurements at the surface and measurements at 10 m altitude, while differences in the coarse mode (here defined by d > 1 µm) are universally larger. Our results suggest that the impact of the UAV rotors on the POPS PNCs are small at low wind speeds, but when operating under a higher wind speed of up to 7.6 m s –1 , larger discrepancies occur. In addition, it appears that the POPS measures sub-micron aerosol particles more accurately than super-micron aerosol particles when airborne on the UAV. These measurements lay the foundations for determining the magnitude of potential errors that might be introduced into measured aerosol particle size distributions and concentrations owing to the turbulence created by the rotors on the UAV.

47 OTHER INSTRUMENTATION↗
NASA NTRS2006

Towards Determination of Visual Requirements for Augmented Reality Displays and Virtual Environments for the Airport Tower

The visual requirements for augmented reality or virtual environments displays that might be used in real or virtual towers are reviewed wi th respect to similar displays already used in aircraft. As an example of the type of human performance studies needed to determine the use ful specifications of augmented reality displays, an optical see-thro ugh display was used in an ATC Tower simulation. Three different binocular fields of view (14 deg, 28 deg, and 47 deg) were examined to det ermine their effect on subjects# ability to detect aircraft maneuveri ng and landing. The results suggest that binocular fields of view much greater than 47 deg are unlikely to dramatically improve search perf ormance and that partial binocular overlap is a feasible display tech nique for augmented reality Tower applications.

NATO FURNISHED↗
DOE OSTI2022

Scaling Building Energy Audits through Machine Learning Methods on Novel Drone Image Data

Building energy audits are time-consuming and labor-intensive. This paper describes a new method using machine learning (ML) techniques on novel data sources (drone images) to improve the identification of building characteristics and retrofit opportunities, and thereby reduce the effort for audits. The new ML method includes: (1) Building footprint extraction using line extraction, polygonization, and polygon-merging, (2) Building envelope extraction using PIX4d modeling software to reconstruct a building 3D model, (3) Visualization tool for viewing images from the 3D model, (4) Window-to-wall ratio (WWR) using state-of-art deep neural network semantic segmentation, (5) Envelope thermal anomaly detection using an unsupervised machine learning clustering algorithm, and (6) Rooftop energy equipment detection based on an object detection algorithm. The testing of this method involved a comparison of additional ML-generated information overlaid on current ‘state-of-practice’ audit and remote assessment baselines using evaluation metrics: labor time and associated cost, marginal benefits of using ML-generated information in workflows for audits and remote assessments, integration potential with existing processes and tools, and replicability/scalability of the method. In two test buildings in California that had comprehensive drawings and meter data available, the ML method effectively generated a building footprint, envelope, rooftop equipment, WWR, and locations of envelope thermal anomalies. Projected target segments of the ML method are sites with minimal drawings and energy data, and underserved sectors such as multistoried housing, disadvantaged communities, and schools for which the ML method can enable identification of building asset characteristics and prioritization of envelope retrofits and decentralized energy equipment retrofits.

Singh, Reshma↗
DOE OSTI2022

How High to Fly? Mapping Evapotranspiration from Remotely Piloted Aircrafts at Different Elevations

Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. The goal of this work was to create, compare, and quantify uncertainty associated with evapotranspiration (ET) maps generated from different conditions and image capture elevations. We collected optical and thermal data from a commercially irrigated potato (Solanum tuberosum) field in the Wisconsin Central Sands using a quadcopter RPA system and combined multispectral/thermal camera. We conducted eight mission sets (24 total missions) during the 2019 growing season. Each mission set included flights at 90, 60, and 30 m above ground level. Ground reference measurements of surface temperature and soil moisture were collected throughout the domain within 15 min of each RPA mission set. Evapotranspiration values were modeled from the flight data using the High-Resolution Mapping of Evapotranspiration (HRMET) model. We compared HRMET-derived ET estimates to an Eddy Covariance system within the flight domain. Additionally, we assessed uncertainty for each flight using a Monte Carlo approach. Results indicate that the primary source of uncertainty in ET estimates was the optical and thermal data. Despite some additional detectable features at low elevation, we conclude that the tradeoff in resources and computation does not currently justify low elevation flights for annual vegetable crop management in the Midwest USA.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Development of a Compact Lidar Sensor for Terrain Relative Navigation and Terrain Hazard Avoidance

A Lidar sensor utilizing linear-mode flash lidar technology and a novel Super-Resolution technique has been developed for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard landing vehicles. Processing algorithms for precision navigation and safe landing location identification take advantage of the uniform fixed pixels property of generated high resolution Digital Elevation Maps to achieve high reliability operation in near real-time. This paper describes the results of drone and helicopter flight tests of a breadboard system, explains the design and capabilities of a recently built compact prototype unit, and proposes a concepts of operation for future landing missions.

3-D Imaging↗
DOE OSTI2024

A new aerial approach for quantifying and attributing methane emissions: implementation and validation

Methane (CH 4 ) is a powerful greenhouse gas that is produced by a diverse set of natural and anthropogenic emission sources. Biogenic methane sources generally involve anaerobic decay processes such as those occurring in wetlands, melting permafrost, or the digestion of organic matter in the guts of ruminant animals. Thermogenic CH 4 sources originate from the breakdown of organic material at high temperatures and pressure within the Earth's crust, a process which also produces more complex trace hydrocarbons such as ethane (C 2 H 6 ). Here, we present the development and deployment of an uncrewed aerial system (UAS) that employs a fast (1 Hz) and sensitive (1–0.5 ppb s -1 ) CH 4 and C 2 H 6 sensor and ultrasonic anemometer. The UAS platform is a vertical-takeoff, hexarotor drone (DJI Matrice 600 Pro, M600P) capable of vertical profiling to 120 m altitude and plume sampling across scales up to 1 km. Simultaneous measurements of CH 4 and C 2 H 6 concentrations, vector winds, and positional data allow for source classification (biogenic versus thermogenic), differentiation, and emission rates without the need for modeling or a priori assumptions about winds, vertical mixing, or other environmental conditions. The system has been used for direct quantification of methane point sources, such as orphan wells, and distributed emitters, such as landfills and wastewater treatment facilities. With detectable source rates as low as 0.04 and up to ~1500 kg h -1 , this UAS offers a direct and repeatable method of horizontal and vertical profiling of emission plumes at scales that are complementary to regional aerial surveys and localized ground-based monitoring.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Design and Technology Maturation of the Stratospheric Projectile Experiment of Entry Dynamics

The supersonic and transonic dynamic stability of blunt-body reentry vehicles currently poses large risks in all of NASA’s ongoing entry missions (MSR SRL, MSR EES, and Dragonfly). These projects have allocated millions of dollars to testing and modeling efforts to buy down risk by using the current state-of-the-art (SoA) facilities at NASA’s disposal. While these facilities have heritage in supplying dynamics data to reentry missions, their availability is severely limited – particularly with the high number of concur-rent projects requesting simultaneous testing– and are costly when considering the science density per dollar. None of the current SoA facility methodologies allow the test model to have the dynamics fully develop through a flight relevant free-stream profile and as such require extrapolations with resultant high uncertainties in order to relate the test dynamics to flight expectations. SPEED is a NASA Ames Center Innovation Fund (CIF) project that is developing a highly tailorable and cost-effective test methodology to better assess the dynamic stability of blunt-body reentry vehicles via a stratospheric balloon flight. This is accomplished by dropping a suite of instrumented capsules from a stratospheric balloon to gain a statistically relevant dataset of scaled reentry vehicles in mission relevant free-flight conditions. This presentation will walk through how the test methodology is being implemented specifically for the Mars Sample Return (MSR) Earth Entry System (EES) geometry in an awarded Flight Opportunities Program (FOP) test flight in early CY24. SPEED Application to MSR: SPEED consists of three main mechanical systems: the Drop Platform, the Projectile, and the test Capsule. SPEED is being developed as a set of guidelines and recommendations for how to test with the proposed Concept of Operations (Conops) since the specific design parameters will vary depending on the specific project’s reference trajectory and entry vehicle design. As such, this presentation will walk through the development time-line as shown in Fig. 2. This is meant to serve as a blueprint for further missions as desired. Mechanical and Avionics Design. The SPEED test platform designed for the MSR-EES capsule geometry with nominal entry parameters has the ability to carry 10 Capsules to altitude instrumented with: 1. 3-Axis Accelerometer 2. IMU 3. Gyroscope 4. Magnetometer 5. Pressure Transducer cruciform 6. Uplook and Horizon Cameras To package the avionics/instrumentation suite, the capsule is approximately 1’ in diameter with the Outer Mold Line (OML) centroid-scaled from the full EES design. The internal volume is gutted and custom-shaped to fit the desired instrumentation suite, as well as to allow for the positioning of ballast mass such that the Center of Gravity is analogous to the flight vehicle. All structural components in the Capsule and Projectile are 3D printed, which significantly reduces the cost of each flight unit to around $1500 including all instrumentation, avionics, and structural components. Flight Conops. The test Capsule is accelerated to the desired altitude and Mach number while stowed in the Projectile, a missile-like vehicle consisting of steel ballast in the nose, a low-drag OML, and an Ejection Mechanism to reliably release the Capsule into the free-flow supersonic conditions. For the MSR-EES design, the capsule employs ~3kg of ballast mass at the nose to accelerate the 1.25kg test Capsule to ~Mach 1.7 at 23km altitude. This requires an initial release altitude of 40km, the quoted limit of a 80kg payload by the FOP-contracted balloon provider. Once the Ejection Mechanism avionics detect the proper conditions, the spring-loaded Ejection Mechanism will release and – guided by the sabot – expose the test Capsule to the desired test conditions for ~5 seconds of free-flight in the supersonic/transonic regimes. Dynamics in the subsonic regime will also be captured with the instrumentation suite with post-flight recovery operations aimed at recovering the high-G-load capable SD cards after the planned hard impact landings. Testing and Development: In the few months the SPEED project has worked the development of MSR-EES flight test, the team has performed lab and drone based testing which this presentation will overview. After the first design phase, the team fabricated Engineering Demonstration Units (EDUs) of all subsystems to perform validation testing shown in Fig. 5. After validation was completed on the subsystem level, a drone-drop test was performed at the recreational flight ceiling of 400ft altitude to assess the SPEED systems in a flight environment. Parameters such as in-flight stability, hard impact landing performance, and avionics performance were quantified and qualified. The FY23 CIF will culminate in a helicopter drop test aboard an Air National Guard Blackhawk. This will prepare the team for the CY24 FOP stratospheric balloon flight that should provide the final verification to begin offering the test platform for mission support. Focus of Presentation: This presentation will outline the technology maturation path of the SPEED implementation to the MSR-EES capsule baseline as well as the details regarding the mechanical system, avionics and instrumentation, and flight operations. Note that a complementary presentation is being submitted for a methodology overview of the SPEED test platform, introducing the testing technique and benefits as well as the full application space of the technology.

pitch damping coefficient↗
NASA NTRS2017

Parallels in Communication and Navigation Technology and Natural Phenomenon

The premise is more than art imitates life, or technology imitates nature it is a nascent step to see how we might be unwittingly inspired and influenced. An example that might immediately come to mind is a starling murmuration (a phenomenon called scale-free correlation) and Intels recent Coachella music festival drone performance. Superconductivity is a macroscopic manifestation of a quantum phenomenon - choreographed electrons (i.e. an electron murmuration) that enable astonishing devices. There is indeed an intimate connectedness between biology and electromagnetism. Our brains are complex neural circuits generating magnetic fields with a magnitude around 100 femtoTesla (roughly one billion times weaker than a typical magnet used to tack notes to a refrigerator door). Migratory birds navigate by orienteering with respect to the Earth's magnetic field. Electromagnetic field therapy is used in orthopedics to aid in bone repair. The electric eel generates a large electric field for self-defense. Sharks apparently detect extremely weak electric fields for finding prey. And so on. There are similarities between the way a field of wheat responds to a breeze and the natural restoring forces of a semiconductor crystal. And waves in a slowly moving river can lap backwards against a peninsular shoreline mimicking a diffraction effect. Getting back to the introductory sentence and mysterious links over cosmic distances, in August 2016, China launched the Quantum Experiments at Space Scale (QUESS) satellite. The technology is based on a non-linear crystal that produces pairs of entangled photons whose attributes apparently remain entwined regardless of how far apart they are separated. This paper will, no doubt superficially, attempt to enumerate and examine these types of connections and parallelisms.

Romanofsky, Robert↗
NASA NTRS2019

Towards Resilient Autonomous Navigation of Drones

Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptually degraded and without good GNSS, it is critical to have a reliable and robust state estimation solution. The main body of literature in robot state estimation focuses on developing complex algorithms favoring accuracy. Typically, these approaches rely on a strong underlying assumption: the main estimation engine will not fail during operation. In contrast, we propose an architecture that pursues robustness in state estimation by considering redundancy and heterogeneity in both sensing and estimation algorithms. The architecture is designed to expect and detect failures and adapt the behavior of the system to ensure safety. To this end, we present HeRO (Heterogeneous Redundant Odometry): a stack of estimation algorithms running in parallel supervised by a resiliency logic. This logic carries out three main functions: a) perform confidence tests both in data quality and algorithm health; b) re-initialize those algorithms that might be malfunctioning; c) generate a smooth state estimate by multiplexing the inputs based on their quality. The state and quality estimates are used by the guidance and control modules to adapt the mobility behaviors of the system. The validation and utility of the approach are shown with real experiments on a ying robot for the use case of autonomous exploration of subterranean environments, with particular results from the STIX event of the DARPA Subterranean Challenge.

Agha-mohammadi, Ali-akbar↗
NASA NTRS2021

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗
NASA NTRSDate not supplied

The LuNaMaps Project: Advancing Capabilities for Developing and Validating Digital Elevation Models of Rocky Surfaces from Orbital Data

Both navigation and surface science can benefit from the ability to generate high resolution and accurate maps of the surface of the Moon and other solar system bodies. The primary way these maps are generated is through the use of orbital imagery and ranging data. Traditionally, the process of using orbital imagery and ranging data is tedious and labor-intensive. Additionally, once maps have been built, there has generally been limited effort in developing standards by which to verify the accuracy and quality of the generated maps. The Lunar Navigation Maps (LuNaMaps) project is a NASA Game Changing Development (GCD) project which over the last 4 years has aimed to address these issues both for the Moon and for other rocky solar system bodies. This has been accomplished through development of new and existing capabilities including: a suite of methods and tools to combine all sources of orbital imagery; a benchmark data set as well as basic requirements for high-fidelity simulations of precision landing functions; tools to synthetically enhance map products with lander-scale features for use in the development and testing of hazard detection systems; methods and tools to evaluate the accuracy of developed digital elevation maps (DEMs) and their quality for use in terrain relative navigation scenarios; and tools to realistically render image and lidar data. In this work, we provide an overview of the capabilities developed through LuNaMaps, demonstrating its use for processing existing lunar data, and describing how it can be applied to other use cases. We additionally provide preliminary results showing the application of the developed tools and processes to the generation of elevation maps of the Lunar Surface Proving Grounds (LSPG) lunar analog at Astrobotic’s Mojave testing facility using “orbital imagery” captured by a drone. In this terrestrial demonstration, we have the benefit of being able to compare the results to a ground truth model of the LSPG. We finally describe plans to use the newly created maps in a terrestrial terrain relative navigation demonstration over the LSPG in early 2025.

optical navigation↗
NASA NTRSDate not supplied

High Resolution Terrain Sensing Lidar for Precision Navigation and Safe Landing of Space and Aerial Vehicles

A 3-D imaging flash lidar sensor employing a resolution enhancement algorithm is being developed at NASA Langley Research Center for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard spacecraft landing on the Moon, Mars, and other planetary bodies. This lidar sensor, we refer to as Terrain Sensing Lidar (TSL), is a solution for future missions that require landing at pre-designated sites near high value resources or at areas of high scientific value, while avoiding hazardous terrain features, such as escarpments, craters, slopes, and rocks, or pre-deployed assets. TSL can also benefit terrestrial applications such as autonomous aerial vehicles without reliance on signals from Global Positioning System (GPS). The feasibility of the TSL concept has been shown through a series of drone, fixed-wing aircraft, and helicopter flight tests. A prototype version of the TSL has been recently assembled for conducting another set of flight tests to demonstrate its readiness for upcoming landing missions. This paper describes the TSL, provides its performance parameters, and explains its operational concepts for landing missions.

Precision Navigation↗
NASA NTRS2024

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m 3 . Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m 3 and a total area of 0.025 km 2 . ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m 3 and 1,160,000 ± 85,000 m 3 , highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Chunli Dai↗
NASA NTRS2021

GA-ASI Final Report and Program Wrap-up for NASA System Integration and Operationalization (SIO)

The NASA SIO demonstration flight of April 3, 2020 represents a successful culmination of 18 months of coordinated effort between GA-ASI, NASA, the FAA, Collins Aerospace, and Honeywell Aerospace to operate a Medium Altitude, Long Endurance (MALE) Unmanned Aircraft System (UAS) safely in the National Airspace System (NAS) using industry leading prototype technologies. The GA-ASI team consisted of technical experts, program managers, engineers, mechanics, flight technicians, flight crews, and numerous other subject matter experts. This final report describes the most significant and potentially impactful aspects of the planning, integration, test, and flight aspects of this effort. GA-ASI successfully integrated the key technologies needed for UAS to fly in the NAS onto our prototype SkyGuardian UAS, which was designed to meet the most stringent airworthiness standards applicable to an aircraft of its size category. A proven Detect and Avoid (DAA) system, developed by GA-ASI and utilizing Honeywell Aerospace technology was integrated onto SkyGuardian for the first time, along with datalink radios from Collins Aerospace that meet the new civil standard for Control and Non-Payload Communication (CNPC) links. GA-ASI also obtained approvals from the FAA and FCC to operate the reconfigured UAS. The SIO demonstration flight represented a commercial aerial surveying operation conducted at medium altitude (>10,000ft above mean sea level). The aircraft’s onboard sensors were used to capture photographic, infrared and radar imagery of public and commercial infrastructure and land, and to subsequently produce the types of data products that would provide business value to potential customers. These survey services would supplement or replace services currently provided by manned airplanes and helicopters, small drones or satellites. A “virtual” mission was also planned, to show what additional survey data could have been captured during the flight if additional sensors had been installed on the aircraft’s external hardpoints. This revision of the final report focuses on information of value to the wider UAS community, and avoids propriety data to facilitate broad dissemination. It also includes a description of GA-ASI’s engagement with the FAA following the SIO flight, which led to the award of an updated Special Airworthiness Certificate in the Experimental Category (SAC-EC) and a Certificate of Waiver or Authorization (COA) allowing operation of the SkyGuardian UAS using its DAA system to satisfy right-of-way rules, instead of a chase plane. The associated operational limitations are described to illustrate the further steps that would be needed to remove those limitations for unhindered commercial operations.

Flight demonstration↗
DOE OSTI2024

Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks

Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

Gonzalez, Emmanuel M.↗
DOE OSTI2021

An Automated Method for Mapping Giant Kelp Canopy Dynamics from UAV

Satellite and aerial imagery have been used extensively for mapping the abundance and distribution of giant kelp ( Macrocystis pyrifera ) in southern California. There is now great potential for using unoccupied aerial vehicles (UAVs) to map kelp canopy at very high resolutions. However, tides and currents have been shown to affect the amount of floating kelp canopy on the water surface, and the impacts of these processes on remotely sensed kelp estimates in this region have not been fully quantified. UAVs were used to map fine-scale changes in canopy area due to tidal height and current speed at kelp forests off the coast of Palos Verdes, CA and Santa Barbara, CA. An automated method for detecting kelp canopy was developed that was 67% accurate using red-green-blue (RGB) UAV imagery and 93% accurate using multispectral UAV imagery across a range of weather, ocean, and illumination conditions. Increases in tidal height of 1 m reduced the amount of floating kelp canopy by 15% in Santa Barbara and by over 30% in Palos Verdes. The effect of current speed on visible kelp canopy was inconclusive, but there was a trend towards lower canopy area with increased current speed. Therefore, while tidal height and current speed can introduce significant variability to estimates of kelp abundance, the magnitude of this variability is site specific. Still, UAVs are a valuable tool for mapping of kelp canopy and can provide greater spatial resolution and temporal coverage than is possible from many satellite sensors. This data can provide insight into the patterns and drivers of high frequency fluctuations in kelp abundance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Drones for Decommissioning

The U.S. Nuclear Regulatory Commission has responsibility for regulating the safe decommissioning of facilities and sites to meet the License Termination Rule in 10 Code of Federal Regulations (CFR) Part 20, Standards for Protection Against Radiation, Subpart E “Radiological Criteria for License Termination.” Decommissioning is performed in accordance with 10 CFR Part 50, Domestic Licensing of Production and Utilization Facilities, as part of license termination (§50.82) and release of the facility or site for unrestricted use (§50.83). The guidance currently demonstrates the minimum requirements and necessary conditions for conducting radiological surveys by a person carrying a radiation detector(s). The Pacific Northwest National Laboratory (PNNL) evaluated the use of an unoccupied aerial vehicle (UAV) to conduct radiological surveys that could be used in decommissioning to potentially reduce time, cost, and worker safety compared to current survey methods. The objective of this project was to evaluate the performance and limitations of a UAV to support a decommissioning radiological survey and compare it to a radiological survey conducted by a human. The primary research questions of interest evaluated were: 1. Did observed UAV paths differ from human paths and, if so, how much? 2. Did survey path deviation affect survey results and, if so, how? 3. Were radiological measurements from human and UAV surveys significantly different? To answer these research questions, an experimental field was set up at PNNL’s 3440 test track, and it included radiological sources commonly surveyed during decommissioning: cobalt-60 (Co-60), cesium-137 (Cs-137), and americium-241 (Am-241). Nine check sources (three each of Am-241, Cs-137, and Co-60) with activities ranging from 3.54 µCi to 39.34 µCi were set over a path that also included an area for measuring background radiation. An Aurelia X6 UAV coupled with a GPS and lidar unit was used to conduct the radiological surveys. UAV and human surveys were conducted using two different NaI(Tl) scintillation radiation detectors (2 in. × 2 in. Ludlum, Inc. and 2 in. × 0.04 in. Alpha Spectra, Inc.) at a travel velocity of approximately 0.2 m/s at a low (15–40 cm median altitude) or high (87–105 cm median altitude) survey altitude. Since the survey velocity and altitude parameters were atypical for normal UAV operations, testing was done prior to conducting the radiological surveys to establish airworthiness, evaluate the navigation system, and establish flight control. Human and UAV surveys were paired according to the detector type and altitude regime to compare the survey data. The results of this proof-of-concept research determined that the UAV and human surveys followed similar survey paths and detected the radiological sources with no significant statistical difference (in 33 out of 36 surveys). However, further research is needed prior to deploying UAVs for decommissioning surveys.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗