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At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Urban Air Mobility (UAM) Vision Concept of Operations (ConOps) UAM Maturity Level (UML)-4 Overview

The Vision ConOps is intended as a foundation to engage members of the UAM community and provide a consensus on the future vision of UAM operations. It provides a concept for more detailed discussion and a basis for the exploration of ideas using a common framework to inform the continued development and integration of UAM as part of the broader transportation system. Advanced Air Mobility (AAM) encompasses a range of innovative aviation technologies (small drones, electric aircraft, automated air traffic management, etc.) that are transforming aviation’s role in everyday life, including the movement of goods and people. Urban Air Mobility (UAM) represents one of the most exciting and complex AAM concepts with highly automated aircraft, providing commercial services to the public over densely populated cities.

Brian Hill↗

NASA NTRS2022

Developing a Vision for Heliophysics Infrastructure

The current infrastructure in heliophysics acts as a barrier towards progress due to the lack of intelligent connections between the many valuable resources in our field. Due to the large scale of this problem, the path to a productive infrastructure requires community collaboration towards a common vision. This white paper presents a broad overview of what that vision could be and a path forward.

Infrastructure↗

NASA NTRSDate not supplied

Visioning a Global Carbon Monitoring System That Can Quantify and Attribute Ocean Carbon Dioxide Removal: Where Are We Now and How Might We Get There?

As atmospheric greenhouse gas concentrations and the resulting social costs rise, it is increasingly important to conduct research on technical approaches to remove carbon dioxide from the atmosphere at the gigaton scale. One highly uncertain but perhaps plausible solution is ocean carbon dioxide (CO2) removal (CDR), which encompasses a suite of proposed techniques for increasing the net flux of CO2 from the atmosphere to the ocean. As reflected in a 2022 National Academy of Sciences report, interest in ocean CDR is rapidly growing, and new stakeholders from a wide range of fields want access to air-sea CO2 flux and ocean carbon information to understand the potential risks and benefits of ocean CDR. Moreover, there is an urgent need for research to develop a scientific basis to support these societal needs. At the same time, there is a growing need to vision a global carbon monitoring system that can quantify natural and anthropogenic perturbations in the air-sea CO2 flux to quantify and attribute the impacts of ocean CDR efforts on the ocean carbon sink. Here, we propose to present nascent efforts at the Ames Research Center to leverage NASA's unique remote sensing and computational capabilities and expertise in basic and applied Earth science to meet the needs of the ocean CDR stakeholder community as represented by the OceanVisions network. Specifically we aim to develop a global ocean CDR scenario explorer with two objectives: 1) to make quantitative estimates of the air-sea CO2 flux and its uncertainty more accessible to a wide range of users, and 2) to enable these diverse users to interactively explore the uncertain impacts on air-sea CO2 fluxes in a wide range of ocean CDR scenarios. The information would be provided to users via a public graphical user interface, and the results would be based on a new synthesis of scientific observations and knowledge in a global ocean mixed layer inverse model run on supercomputers. The proposed tool aims to fill a unique and valuable niche for users and the scientific community by balancing tradeoffs between user interests, scientific knowledge, and technical capabilities. We will conclude by visioning the potential implications for a global carbon monitoring system in a world with gigaton scale ocean CDR.

Global↗

NASA NTRS2022

Robust Controller Synthesis for Vision-based Spacecraft Guidance and Control

This work develops a method for Robust Controller for Vision-based Spacecraft (RCVS) guidance and control, integral to the robust autonomy framework for multi-spacecraft for- mation control and reconfiguration applications. The method is built around the use of a photo-realistic simulator, where a camera is deployed on a tracking spacecraft (ego) in order to observe an uncontrolled spacecraft (target) in a Low Earth Orbit (LEO). In this direction, the proposed approach performs the relative state (attitude and position) estimation of the target spacecraft using Convolutional Neural Network (CNN). The state estimation error is then modeled and the corresponding error-bounds are obtained around a nominal trajectory of the ego and target spacecraft. Next, this work proposes a linear matrix inequalities (LMIs) based approach to controller synthesis, guaranteed to be robust against both model uncertainties and measurement errors, resulting from vision-based estimation. This controller is comprised of two distinct components, one synthesized based on the nominal trajectory, while the “robust” component corrects for deviations from the nominal trajectory. Finally, a tracking scenario that directly utilize the image data for spacecraft guidance and control, is presented to showcase the performance of the proposed robust autonomy framework.

Rahmani, Amir↗

NASA NTRSDate not supplied

Ground-Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility

Advanced Air Mobility (AAM) Air Mobility and Urban Air Mobility (UAM) require aircraft surveillance and monitoring for safety and security. Persistent tracking of flying objects provides Air Traffic Control (ATC) and Air Traffic Management (ATM) continuous coverage and knowledge of the national airspace (NAS). Since there are numerous more AAM and UAM aircraft than commercial aircraft, it will be challenging to utilize the same ATC/ATM architectures. A first step in creating a similar ATC/ATM architecture for AAM/UAM will require ground-based and airborne-based sensors to provide monitoring, which will be difficult in urban environments due to GPS degradation. This paper proposes a vision-based tracking method with static cameras by utilizing image subtraction and blob detection, which avoids adding additional electromagnetic interferences in the environment with sensors such as radar. The ground-based vision tracker (GBVT) outputs the detected objects' azimuth and elevation angles from unmanned aerial system (UAS) flight tests. Future and ongoing work includes sending the detected objects' azimuth and elevation angles as inputs for an extended Kalman filter (EKF) to estimate the position and velocity of the detected object.

distributed sensing↗

NASA NTRSDate not supplied

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembly engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗

NASA NTRSDate not supplied

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembled engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗

NASA NTRS2023

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

Unreal Engine↗

NASA NTRSDate not supplied

Ground-Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility

Advanced Air Mobility (AAM) Air Mobility and Urban Air Mobility (UAM) require aircraft surveillance and monitoring for safety and security. Persistent tracking of flying objects provides Air Traffic Control (ATC) and Air Traffic Management (ATM) continuous coverage and knowledge of the national airspace system (NAS). Given the significant disparity in the number of AAM and UAM aircraft compared to commercial aircraft in the NAS, coupled with the dense AAM/UAM operations in urban environments, employing the existing ATC/ATM architectures poses considerable challenges. A first step in creating a similar ATC/ATM architecture for AAM/UAM will require ground-based and airborne-based sensors to provide monitoring, which will be difficult in urban environments due to GPS degradation. This paper proposes a vision-based tracking method with static cameras by utilizing image subtraction and blob detection, which avoids adding additional electromagnetic interferences in the environment with sensors such as radar. The ground-based vision tracker (GBVT) outputs the detected objects' azimuth and elevation angles from unmanned aerial system (UAS) flight tests. Future and ongoing work includes sending the detected objects' azimuth and elevation angles as inputs for an extended Kalman filter (EKF) to estimate the position and velocity of the detected object.

distributed sensing↗

DOE OSTI2017

System and method for controlling a vision guided robot assembly

A method includes the following steps: actuating a robotic arm to perform an action at a start position; moving the robotic arm from the start position toward a first position; determining from a vision process method if a first part from the first position will be ready to be subjected to a first action by the robotic arm once the robotic arm reaches the first position; commencing the execution of the visual processing method for determining the position deviation of the second part from the second position and the readiness of the second part to be subjected to a second action by the robotic arm once the robotic arm reaches the second position; and performing a first action on the first part using the robotic arm with the position deviation of the first part from the first position predetermined by the vision process method.

42 ENGINEERING↗

DOE OSTI2018

Enabling an Interactive Grid: A Strategic Vision for Interoperability

This whitepaper proposes that ecosystems of organizations integrating smart technology use such a vision to align their directions and transcend immediate challenge. It then offers a framework for developing a stakeholder roadmap for these integration ecosystems to achieve their vision, including tools to measure the state of interoperability, and a process to identify gaps and prioritize technologies, policies, and timelines.

der integration↗

DOE OSTI2018

Enabling an Interactive Grid: A Strategic Vision for Interoperability

interoperability, , grid architecture, der integra↗

DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗

DOE OSTI2021

Progress Towards Goals Outlined in the 2014 Decadal Vision

The Atmospheric Radiation Measurement (ARM) user facility published its first decadal vision document in 2014 (U.S. DOE 2014a). The overarching vision described in that document was to develop an integrated observation-modeling framework to accelerate the application of ARM observations for process studies and model applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Elementary Particle Physics Vision for EPP2024

In the fall of 2022, the decadal survey committee on Elementary Particle Physics of the US National Academies requested 2000 word Vision Papers, giving personal interpretations of the results of the Snowmass 2021 study and the future of the field. This is my contribution, which emphasizes the central role of the Higgs boson and its associated mysteries. I encourage the authors of other Vision Papers to make them widely available on the arXiv.

Peskin, Michael↗

DOE OSTI2018

Achieving the Vision: An Update on Cleanup, Closure, and Reindustrialization of the East Tennessee Technology Park - 18401

URS-CH2M Oak Ridge LLC (UCOR) is responsible for cleanup of the U.S. Department of Energy's (DOE) East Tennessee Technology Park (ETTP) in Oak Ridge, Tennessee. Formerly known as the K-25 site, the facility enriched uranium for major defense nd energy programs for more than 40 years. The site's industrial processes - largely the gaseous diffusion method used for enriching uranium - left behind radioactive, chemical and industrial contaminants in soils and structures. Cleanup of the environmental legacies enables DOE's goal for converting ETTP into a private sector industrial park. Return of the restored site to the community will attract new industries to the region, creating jobs and growing the economy. The central elements to achieve DOE's vision for the site are 1) Cleanup, 2) Closure, and 3) Reindustrialization. Cleanup entails completion of all demolition activities and remedial actions consistent with the Comprehensive Environmental Response, Compensation and Liability Act of 1980 (CERCLA) agreements. Closure leaves facilities and infrastructure in a condition that enables transfer of the site to non-federal entities and reduces DOE's long-term stewardship costs. Closure activities include: - Infrastructure stabilization (site utilities, roads, stormwater, etc.); - Disposal or transfer of government property; - Relocation of most DOE contractors and support functions offsite; - Transfer of the majority of the site property to non-federal entities for redevelopment. Reindustrialization enables DOE's vision for the site as a multi-use private sector industrial park, reducing DOE's costs and enhancing the local economy. Transfer to the private sector will be accomplished through the Community Reuse Organization of East Tennessee (CROET), which will serve as the Park Manager. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Robotic Non-destructive Test of Concrete Structures with GPR, Impact Echo and 3D Vision - 19540

Structural members come across with various internal defects that cannot be evaluated visually. Instead, hand-held nondestructive sensors are used to detect such defects. In many situations, the hand-held NDT is not possible in high-risk structures because of limited access or safety issues. Therefore, Robotic NDT plays major role in assessing such structures. Robot Operating Systems (ROS) is a powerful open source tool, which is used to program and control robots and other devices enabling one to use sensors, devices and new functionalities such as autonomous navigation, visual perception. For the research presented, ROS is used to program the Sawyer robot arm manufactured by Rethink Robotics. The control system for the robot ensures the ability to adapt to different weights of integrated measuring devices and measured force applied against the wall with a wheeled measuring device while making a remote sweep. Twelve reinforced concrete (RC) beams built with characterized defects are evaluated with the robotic arm integrated with the measuring devices. The RC beams are characterized by voids, corrosion and debonding problems of rebars. The Impact Echo and Ground Penetrating Radar (GPR) devices attached to the robot arm are used to measure the material properties of the concrete beams. The robot arm ensures consistency and uniformity of measurement even in obscure locations. Infrared cameras are used to obtain 3D images of the surfaces of the concrete structures. Roughness is analyzed using computer vision techniques prior to the use of other sensors that are in touch with the surfaces. The computer vision is also used to handle the images collected from GPR sensor. Hand-held NDE is also performed to verify the results obtained by robotic examination. (authors)

42 ENGINEERING↗

DOE OSTI2026

Selective Amnesia using Contrastive Subnet Erasure for Class Level Unlearning in Vision Models

We study concept-level forgetting in pretrained vision models: removing an entire semantic category so the system no longer recognizes that object in unseen images and contexts, rather than merely forgetting specific training examples. Prior work either applies blunt global projections or fine-tunes parameters, which can introduce collateral damage to unrelated features, add compute, and become unstable as forgetting strength increases. We introduce Contrastive Subnet Erasure (CSE), a training-free, encoder-centric edit that targets a compact set of channels most responsible for the class and attenuates them in a calibrated manner. The modification is algebraically folded into the subsequent layer, yielding no inference-time overhead and leaving task heads unchanged. To evaluate whether forgetting generalizes beyond the data used to specify the class, we introduce a cross dataset protocol in which the class is defined on a source dataset and performance is measured on a disjoint target dataset drawn from a different distribution with no shared images. This setup tests whether the model still fails to recognize the object when it looks different or appears in new scenes, and it helps avoid overfitting to patterns in the source dataset. Across CIFAR 10, CIFAR 100, and ImageNet under this protocol, CSE achieves stronger forgetting of the target class while better preserving non target utility than existing baselines in both single class and multi class settings. Overall, CSE provides a simple, stable, and deployment-ready mechanism for class-level unlearning in vision.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗