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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Analysis of Input from Wildfire Incident Experts to Identify Key Risks and Hazards in Wildfire Emergency Response

The United States Department of Agriculture (USDA) describes wildland fires as, “a force of nature that can be nearly as impossible to prevent, and as difficult to control, as hurricanes, tornadoes and floods.” Existing challenges in managing wildland fires often put first responders’ lives at risk. The emergence of drones and their capabilities to supplement human efforts could alleviate some, if not all, of those risks that first responders face during wildfire management efforts. However, the process of adding drones to wildfire response has come with its own challenges as well. NASA’s System-Wide Safety Project is working towards overcoming these challenges to enable routine transfer of risk from responders to aviation assets. The concept of operations and model-based systems engineering (MBSE) effort for this shift is underway. To inform and to validate the MBSE effort, we delivered a questionnaire to wildland firefighting experts on the hazards they currently face. This questionnaire has given us insight and a better understanding of the challenges related to the use of drones from a first responder’s point of view. We are using this information to better address responders’ concerns, develop a safety management system, and eliminate the roadblocks that prevent the use of drones in wildfire management.

Wildfire↗
NASA NTRS2023

Farming the Future: An Approach to Precision Agriculture through UAS-based Thermal Scanners

Food waste is a global issue, and it is extremely prevalent in the United States (U.S.) where one-third of the food produced goes to waste. The most common item found in landfills is food waste, which is why it is one of the most significant contributors to global warming with 8%. These negative environmental impacts also return to affect agriculture, as global warming changes climate patterns and adversely affects farming practices. In addition, the resources that were used to grow the produce are also wasted, including money, water, and time. To tackle this problem at its roots, this paper includes a design of an unmanned thermal drone that can be a farmer’s third eye. This drone will scan over farms and provide data to the farmers about the health, ripeness, and moisture content of their crops through mounted thermal cameras and artificial intelligence. The information provided via these drones will help farmers to make more informed decisions about their crops, decreasing the chances that the crops will go bad in the field. These drones will be one of the first steps in preventing food waste, which will, in turn, help to reduce global emissions that result from farming.

UAS supported agriculture↗
DOE OSTI2024

Low-Flow Marine Hydrokinetic Turbine for Small Autonomous Unmanned Mobile Recharge Stations

A prototype low-flow marine current turbine for deployment from a small unmanned mobile floating platform has been developed for autonomously seeking and harnessing tidal/coastal currents. The support platform is an unmanned surface vehicle (USV), in the form of a catamaran with two electric outboard motors and with capabilities for autonomous navigation. The USV utilized is a WAM-V 16 vehicle that has been developed separately with support from the Office of Naval Research (ONR) [1]. The marine current turbine is based on a freestream waterwheel (FSWW), also known as an undershot waterwheel (FSWW), mounted on the stern of the USV. The concept of operation involves the USV autonomously navigating to a designated marine current resource. Upon arrival, the USV anchors itself, aligns with the current, and deploys the FSWW turbine using a custom cable-lift mechanism. The turbine harnesses the local current, and an onboard power-take-off (PTO) device converts the mechanical energy into electricity, which is stored in an onboard battery bank. When energy harvesting is completed, the turbine and the anchor are retrieved and the USV navigates to a selected location. These unmanned at-sea platforms can provide power to other unmanned maritime systems. Specifically, in this project, the power generated onboard can be used to charge aerial drones via a custom flight deck that has been developed for the USV. The recharging capabilities offered by a fleet of such strategically placed recharging stations can significantly benefit aerial drones operating in the maritime domain by eliminating the need to travel back and forth to land or ship based charging stations. The project has resulted in the development of subcomponents, including the FSWW turbine, a novel PTO, an automated anchoring system for the USV, an automated turbine deployment system, and a flight deck with capabilities onboard the USV for landing, direct-contact charging and takeoff of aerial drones. The design and development of these subsystems have culminated in the overall prototype marine hydrokinetic platform (MHK Platform, Fig. 1). Comprehensive lab and field testing have been conducted to validate the functionality and performance of the platform and its components. The project demonstrates the potential for autonomous, unmanned systems to harness renewable energy from marine currents, and provide sustainable power solutions for maritime applications such as coastal surveillance and environmental monitoring; shoreline mapping; search and rescue; oceanographic research; inspection and maintenance of offshore energy installations like wind turbines and oil rigs; oil spill response; maritime disaster response; and aerial surveys, as well as facilitation of data transfer drones and shore stations.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Development of an Unmanned Mobile Current Turbine Platform: Preprint

The design and development of a prototype unmanned mobile floating platform, equipped with a custom low-flow marine current turbine for autonomously seeking and harnessing tidal/coastal currents is described. The platform is an unmanned surface vehicle in the form of a catamaran with two electric outboard motors and capabilities for autonomous navigation. An undershot water wheel, aided by a custom flow concentrator, has been selected as the basic design for the marine current turbine, which is mounted on the stern of the unmanned surface vehicle platform. The concept of operation is that the platform would navigate to a designated marine current resource, autonomously anchor at the location, align itself in the current and deploy the current turbine using a custom cable-lift deployment mechanism. As the turbine harnesses the local current, an onboard power-take-off device converts the harnessed mechanical energy to electricity which is stored in onboard batteries. Considerations of deployment in tidal and coastal waters required obtaining the necessary environmental permits for conducting in-water testing; developing required mitigation measures in protecting local wildlife and their habitats; and identifying potential in-water test sites and surveying them for their suitability in terms of the current resource, bottom type, water depth, and local boat traffic. The design and development of the turbine and the results of initial in-water testing are discussed. The potential for scaling up the system for extended capacity is presented.

ENGINEERING,TIDAL AND WAVE POWER↗
NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. There exists a gamut of approaches to solving merging and intersection crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to solving these problems that is based on distributed cooperation between the UAVs/UASs and the infrastructure. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing UAVs/UASs to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them.

Distributed↗
DOE OSTI2022

Autonomous aerial flight path inspection using advanced manufacturing techniques

Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structural components.

42 ENGINEERING↗
DOE OSTI2020

Unmanned Aerial Vehicle Path Generation for Image Collection to Assist Heliostat Field Optical Characterization

This paper focuses on applications of unmanned aerial vehicles (UAVs) for measuring optical error of heliostats in concentrating solar power (CSP) plants. In CSP, there is a need to measure solar-field optical errors, which is critical for future production improvement as well as for operations and maintenance of a heliostat field. This latter need is particularly challenging because of the large number of heliostats (over 10,000 for a utility-scale power plant) that individually track the sun in the field. To address this issue, a camera-equipped UAV, with an optimized drone flight path developed and uploaded to it, collects images of a precise reflection of the tower on each heliostat to evaluate optical error sources without interrupting plant operation. Generation of the drone path for capturing the reflected images is affected by a number technical and realistic constraints, which include the camera angle used to capture the image, the blocking of the camera view due to surrounding heliostats, the location of the camera in reference to the target heliostat, and the target heliostat position with reference to the tower. The effect of these constraints on calculating the camera position will be discussed in detail in this article. An effective drone-path algorithm is generated to fulfil the need of image collection under various constraints.

concentrated solar power↗
NASA NTRS2000

A Plan for Measuring Climatic Scale Global Precipitation Variability: The Global Precipitation Mission

The outstanding success of the Tropical Rainfall Measuring Mission (TRMM) stemmed from a near flawless launch and deployment, a highly successful measurement campaign, achievement of all original scientific objectives before the mission life had ended, and the accomplishment of a number of unanticipated but important additional scientific advances. This success and the realization that satellite rainfall datasets are now a foremost tool in the understanding of decadal climate variability has helped motivate a comprehensive global rainfall measuring mission, called 'The Global Precipitation Mission' (GPM). The intent of this mission is to address looming scientific questions arising in the context of global climate-water cycle interactions, hydrometeorology, weather prediction, the global carbon budget, and atmosphere-biosphere-cryosphere chemistry. This paper addresses the status of that mission currently planed for launch in the early 2007 time frame. The GPM design involves a nine-member satellite constellation, one of which will be an advanced TRMM-like 'core' satellite carrying a dual-frequency Ku-Ka band radar (df-PR) and a TMI-like radiometer. The other eight members of the constellation can be considered drones to the core satellite, each carrying some type of passive microwave radiometer measuring across the 10.7-85 GHz frequency range, likely based on both real and synthetic aperture antenna technology and to include a combination of new lightweight dedicated GPM drones and both co-existing operational and experimental satellites carrying passive microwave radiometers (i.e., SSM/l, AMSR, etc.). The constellation is designed to provide a minimum of three-hour sampling at any spot on the globe using sun-synchronous orbit architecture, with the core satellite providing relevant measurements on internal cloud precipitation microphysical processes. The core satellite also enables 'training' and 'calibration' of the drone retrieval process. Additional information is contained in the original extended abstract.

Smith, Eric A.↗
NASA NTRS2016

Computational Study of Flow Interactions in Coaxial Rotors

Although the first idea of coaxial rotors appeared more than 150 years ago, most helicopters have used single main-rotor/tail-rotor combination. Since reactive moments of coaxial rotors are canceled by contra-rotation, no tail rotor is required to counter the torque generated by the main rotor. Unlike the single main rotor design that distributes power to both main and tail rotors, all of the power for coaxial rotors is used for vertical thrust. Thus, no power is wasted for anti-torque or directional control. The saved power helps coaxial rotors reach a higher hover ceiling than single rotor helicopters. Another advantage of coaxial rotors is that the overall rotor diameter can be reduced for a given vehicle gross weight because each rotor provides a maximum contribution to vertical thrust to overcome vehicle weight. However, increased mechanical complexity of the hub has been one of the challenges for manufacturing coaxial rotorcraft. Only the Kamov Design Bureau of Russia had been notably successful in production of coaxial helicopters until Sikorsky built X2, an experimental compound helicopter. Recent developments in unmanned aircraft systems and high-speed rotorcraft have renewed interest in the coaxial configuration. Multi-rotors are frequently used for small electric unmanned rotorcraft partly due to mechanical simplicity. The use of multiple motors provides redundancy as well as cost-efficiency. The multi-rotor concept has rarely been used until recently because of its inherent stability and control problems. However, advances in inexpensive electronic flight control systems have opened the floodgates for small drones using multirotors. Coaxial rotors have started to appear in some multi-rotor configurations. Small coaxial rotors have often been designed using a hundred year old approach that is "sketch, build, fly, and iterate." In that approach, there is no systematic way to explore trade-offs or determine logical next steps. It is neither possible to account for multiple real-world constraints up front in design nor possible to know what performance is possible with a given design. Since unmanned vehicles are sized and optimized for the particular mission, a modern low-fidelity conceptual design and sizing tool that has been used for the design of large helicopters can be used for design of small coaxial rotorcraft. However, unlike most helicopters with single main rotor, the interactions between the upper and lower rotors emerge as an important factor to consider in design because an increase in performance of a multi-rotor system is not proportional to the number of rotors. Interference losses and differences in thrusts between the upper and lower rotors were investigated by theoretical methods as well as a computational fluid dynamics (CFD) method using the Reynolds-Averaged Navier-Stokes (RANS) equations. In this work, hybrid turbulence models are used to investigate the physics of interactions between coaxial rotors and a fuselage that are not well understood. Present study covers not only small-scale drones but also large-scale coaxial rotors for heavy-lifting missions. Considering the recently proposed FAA drone rules that require the flight only in visual line-of-sight, a large multirotor might be used as an airborne carrier for launch and recovery of unmanned aircraft systems with a human operator onboard. For applications to civil operations, their aerodynamic performance and noise levels need to be assessed. Noise is one of the largest limiting factors to rotorcraft operations in urban area. Since the high-frequency noise of multi-rotors may increase the annoyance, noise may turn out to be a key issue that must be addressed for market acceptability. One of the objectives of the present work is to study the effects of inter-rotor spacing and collectives on the performance, efficiency, and acoustics of coaxial rotor systems.

Study↗
NASA NTRS2017

A Multi-Modality Mobility Concept for a Small Package Delivery UAV

This paper will discuss a different approach to the typical notional small package delivery drone concept. Most delivery drone concepts employ a point-to-point aerial delivery CONOPS (Concept of Operations) from a warehouse directly to the front or back yards of a customers residence or a commercial office space. Instead, the proposed approach is somewhat analogous to current postal deliveries: a small aerial vehicle flies from a warehouse to designated neighborhood VTOL (Vertical Take-Off and Landing) landing spots where the aerial vehicle then converts to a "roadable" (ground-mobility) vehicle that then transits on sidewalks and/or bicycle paths till it arrives to the residence/office drop-off points. This concept and associated platform or vehicle will be referred in this paper as MICHAEL (Multimodal Intra-City Hauling and Aerial-Effected Logistics) concept. It is suggested that the MICHAEL concept potentially results in a more community friendly "delivery drone" approach.

UAV↗
NASA NTRS2018

UAS Autonomous Hazard Mitigation through Assured Compliance with Conformance Criteria

The behavior of a drone depends on the integrity of the data it uses and the reliability of the avionics systems that process that data to affect the operation of the aircraft. Commercial unmanned aircraft systems frequently rely on commercial-off-the-shelf and open source avionics components and data sources whose reliability and integrity are not easily assured. To mitigate failure events for aircraft that do not comply with conventional aviation safety standards, operational limitations are typically prescribed by regulators. Part 107 of the Federal Aviation Regulations serves as a good example of operational limitations that mitigate risk for small unmanned aircraft systems. These limitations, however, restrict growth possibilities for the industry. Any reasonable path toward achieving routine operation of all types of drones will have to address the need for assurance of avionics systems, especially their software. This paper discusses the possibility of strategically using assured systems as a stepping stone to routine operation of drones. A specimen system for assured geofencing, called Safeguard, is described as an example of such a stepping stone.

Dill, Evan, T.↗
NASA NTRSDate not supplied

The Influence of Viability, Independence, and Self-Governance on Trust and Public Acceptance of Uncrewed Air Vehicle Operations

Trust is expected to be a critical construct that drives successful use of advanced air mobility (AAM) technologies. As yet, though, the role of trust in human-autonomy interaction is underexplored. Kaber (2018) argues that autonomy requires the highest level of three independent dimensions -- viability, independence, and self-governance. The present study examined whether trust varies across the three dimensions of autonomy under varying levels of risk. Participants in the high-risk group read a series of vignettes on a drone that delivers medical supplies over a city where the current study was conducted. Participants in the low-risk group read a series of vignettes on a drone that delivers fast food over a fictitious city. Each vignette described a drone that is either autonomous (i.e., possesses all dimensions) or automated (i.e., one of the dimensions is compromised). Results imply that the three dimensions of autonomy do not equally influence human-technology trust and behavior.

Advanced Air Mobility↗
NASA NTRS2023

Automated sUAS Inspection Capability for NASA’s Mission Critical Testing Facilities

The wind tunnels at Ames are crucial to NASA and industry but pose unique inspection challenges. Current inspection processes are highly manual, and as such are labor and schedule intensive. These needs can be better met with emerging technology such as sUAS (drones), computer vision, and machine learning. This work seeks to bring the drone based inspection workflow into production-ready status and integrate into existing facility inspections. This includes operation of a drone platform, establishing a photogrammetry pipeline, development of procedures and streamlining flight approval processes, and establishing a base of experience at Ames for this work. We have worked with the NFAC, unitary, and Arcjet facilities to identify use cases and have performed test flights at Ames (both indoors and outdoors). The system is being readied for incorporation into routine inspection operations.

David Daisuke Murakami↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Fast Data Processing for Hyperspectral Sensors on Small Platforms

Hyperspectral imaging is a very promising technology for nuclear proliferation detection. However, due to size and weight restrictions, small hyperspectral platforms such as satellites and small drones lack the on-board computing resources for accurate, real-time analysis of the enormous flow of data that a continuously operating hyperspectral sensor generates. This severely limits satellite systems, which can collect far more data than what they can telemeter, and hinders the ability of all platforms to adapt their missions on the fly in response to observations. This program addresses the hyperspectral data processing challenge through development of new, fast and accurate algorithms that produce data products in real time. The algorithms circumvent the major computational bottlenecks in existing processing streams, and would be incorporated in lightweight, power-efficient single-board computer systems. The toolkit of fast algorithms will be immediately useful in current and future hyperspectral systems being built by the Government and by private industry, including drone-based systems and satellite constellations that acquire timely global imagery.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Polarimetry-Enhanced Imaging towards Autonomous Solar Field and Receiver Inspections

During the typical operation of a Concentrating Solar Power (CSP) plant, a large portion of the energy (~45%) can be lost due to various imperfect conditions, such as blocking, shading, mirror soiling, tracking and canting errors, etc. It is necessary to develop efficient and effective field inspection technology to optically survey and characterize a CSP field, which can be used as an input for autonomous control, maintenance scheduling and spot repair whenever necessary to maximize the overall efficiency of the plant. In this project, We aim to apply polarimetric imaging for CSP collector and receiver inspection and develop polarimetric drone cameras (via integrating polarimetric imagers onto drones) for autonomous field inspection in CSP plants.

14 SOLAR ENERGY↗