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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments [Slides]

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. The ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

14 SOLAR ENERGY↗
DOE OSTI2025

Unveiling the Hidden Evolution of Crystal Defects and Disorder in Energy Materials

Control of point defects and disorder in functional thin films and 2D materials is critical to realizing their full potential in applications ranging from energy storage to advanced electronics. However, these phenomena are often poorly understood, difficult to characterize, and challenging to direct with precision. This presentation explores emerging multi-modal computer vision to decipher and predict order in materials across multiple length scales in the electron microscope, from the atomic to the nanoscale. By fusing data from diverse sources, these powerful models provide unprecedented insights into materials' lifecycles, enabling the control of defects and their associated properties at a fundamental level. This capability promises to transform materials design and accelerate the development of next-generation technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

AI-Driven Accelerated Inclusion Analysis for Energy Efficient Steelmaking (Final CRADA Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and ArcelorMittal USA Research LLC (“ArcelorMittal” as the Participant), to use scanning electron microscopy (SEM) images, computer vision and machine learning methods, and high-performance computing to accelerate the inclusion analysis process of liquid steel so that new methods can be used for near-real time process control on the shop floor.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Vegetation Detection Using Deep Learning and Conventional Methods

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.

58 GEOSCIENCES↗
DOE OSTI2023

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections.

47 OTHER INSTRUMENTATION↗
NASA NTRS2002

Multi-sensor, high speed autonomous stair climbing

In this paper we present the design and implementation of a new set of estimation and control algorithms that increase the speed and effectiveness of stair climbing.

mobile robotics estimation control machine vision↗
NASA NTRS2000

TERA-OPS Processing for ATR

A three-dimensional microelectronic device (3DANN-R) capable of performing general image convolution at the speed of 10***sup12*** operations/second (ops) in a volume of less than 1.5 cubic centimeter has been successfully built under the BMDO/JPL VIGILANTE program.

Automatic↗
NASA NTRSDate not supplied

Mars 2020 – Landing a 1-ton rover and helicopter in an ancient Martian Lake

The Mars 2020 spacecraft launched in July 2020 and landed the Perseverance rover and Ingenuity helicopter successfully in Jezero crater on Feb. 18, 2021. Mars 2020 is the first stage of the Mars Sample Return campaign that will bring back the first samples from another planet to Earth. The entry, descent, and landing (EDL) sequence of the Mars 2020 spacecraft largely leveraged the previous Mars Science Laboratory (MSL) mission from 2012. Mars 2020 retained most of the EDL sequences of MSL, including active maneuvering during hypersonic flight to accurately target the landing site and use of the Skycrane descent stage that slowly lowered the rover while hovering above the ground. But Mars 2020 also added Terrain Relative Navigation, a machine vision-based system that allowed the spacecraft to navigate using an on-board camera that mapped ground landmarks to an on-board map, allowing the spacecraft to safely land in locations that were too hazardous for any previous Martian mission. Come hear about the “Seven Minutes of Terror” and the eight years of effort that went into the engineering behind the spacecraft.

Soumyo Dutta↗
NASA NTRSDate not supplied

Entry, Descent, and Landing GN&C System Evaluation via Cable-Driven Emulation Robotics

An innovative cable-driven parallel robot named the Six Degree-of-Freedom Tendon Actu- ated Robot (STAR), recently developed by researchers at NASA Johnson Space Center, was built to enable entry, descent, and landing (EDL) emulation experiments. Upon functional testing and system verification, STAR employed several simulated EDL trajectories to validate velocimeter LIDAR-based sensor models for cooperative terrain relative navigation applica- tions for its first testing campaign. The STAR lab also hosted researchers from Astrobotic to evaluate the real-time performance of a newly developed sensor payload, UltraNav, through hardware in-the-loop-testing (HWIL). This work introduces the STAR system along with its accompanying testing utilities and capabilities, and deliver the experimental methodology and results of test campaigns performed with the STAR facility to date.

navigation↗
NASA NTRSDate not supplied

Spacecraft Operation Emulation Via Six DOF Tendon-Actuated Robot

NASA Johnson Space Center’s (JSC) Simulation, Emulation, Navigation, Sensors and STAR (SENSS) Laboratory has developed a large-scale six degree of freedom tendon-actuated robot (STAR) for emulation of spacecraft proximity operations and Entry, Descent, and Landing approach trajectories to aid in the rapid development and testing of a wide range of guidance, navigation, and control algorithms at low cost.

navigation↗
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↗
NASA NTRSDate not supplied

Software Design for the Supervised Autonomous Assembly of a Tall Lunar Tower

Tall towers enable a wide-ranging set of capabilities on the lunar surface including communication, navigation, surveillance, power generation, and more. The Tall Lunar Tower project at NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of an engineering development unit to assemble a tall tower through supervised autonomous operations. In this paper, the software design for the supervised autonomous assembly of a tall lunar tower is presented. The paper includes a high-level description of the concept of operations, the agents, and an overview of the software architecture.

Moon↗