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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Using Virtual Reality and Motion Capture as tools for Human Factors Engineering at NASA Marshall Space Flight Center

NASA Marshall Space Flight Center (MSFC) Human Factors Engineering (HFE) Team is implementing virtual reality (VR) and motion capture (MoCap) into HFE analyses of various projects through its Virtual Environments Lab (VEL). VR allows for multiple analyses early in the design process and more opportunities to give design feedback. This tool can be used by engineers in most disciplines to compare design alternatives and is particularly valuable to HFE to give early input during these evaluations. These techniques are being implemented for concept development of Deep Space Habitats (DSH), and work is being done to implement VR for design aspects of the Space Launch System (SLS). VR utilization in the VEL will push the design to be better formulated before mockups are constructed, saving budget and time. The MSFC VEL will continue forward leaning implementation with VR technologies in these and other projects for better models earlier in the design process.

Andrews, Tanya↗
NASA NTRS2019

Using Virtual Reality and Motion Capture as tools for Human Factors Engineering at NASA Marshall Space Flight Center

NASA Marshall Space Flight Center (MSFC) Human Factors Engineering (HFE) Team is implementing virtual reality (VR) and motion capture (MoCap) into HFE analyses of various projects through its Virtual Environments Lab (VEL). These techniques are being implemented for concept of development of Deep Space Habitats (DSH) and design and analyses for NASA’s Space Launch System (SLS). VR utilization in the VEL will push the design to be better formulated before mockups are constructed, saving budget and time.

Andrews, Tanya↗
NASA NTRS2019

Using Virtual Reality for Worksite Analysis

NASA Marshall Space Flight Center (MSFC) Human Factors Engineering (HFE) Team is implementing virtual reality (VR) and motion capture (MoCap) into HFE analyses of various projects through its Virtual Environments Lab (VEL). This complements the long history of analyses completed using mockups. These techniques are being implemented for; (1) Concept of development of Deep Space Habitats (DSH), (2) Design and analyses for NASA’s Space Launch System (SLS). VR utilization in the VEL will push the design to be better formulated before mockups are constructed, saving budget and time.

Andrews, Tanya↗
NASA NTRS2019

Underwater Virtual Reality System for Neutral Buoyancy Training: Development and Evaluation

During terrestrial activities, sensation of pressure on the skin and tension in muscles and joints provides information about how the body is oriented relative to gravity and how the body is moving relative to the surrounding environment. In contrast, in aquatic environments when suspended in a state of neutral buoyancy, the weight of the body and limbs is offloaded, rendering these cues uninformative. It is not yet known how this altered sensory environment impacts virtual reality experiences. To investigate this question, we converted a full-face SCUBA mask into an underwater head-mounted display and developed software to simulate jetpack locomotion outside the International Space Station. Our goal was to emulate conditions experienced by astronauts during training at NASA's Neutral Buoyancy Lab. A user study was conducted to evaluate both sickness and presence when using virtual reality in this altered sensory environment. We observed an increase in nausea related symptoms underwater, but we cannot conclude that this is due to VR use. Other measures of sickness and presence underwater were comparable to measures taken above water. We conclude with suggestions for improved underwater VR systems and improved methods for evaluation of these systems based on our experience.

Simulation↗
NASA NTRS2019

Forming Aggregations using Virtual Sharding: Lessons Learned from Simple Scalable Storage (S3)

Data aggregation is the ability to combine separate datasets to form a single new logical dataset provides users with a powerful abstraction. The advantage of an aggregate dataset is that the users are freed from having to understand, and incorporate into their workflow, knowledge about the (ad hoc) organization of the constituent datasets. However, aggregating large numbers of files can be computationally complex with data server systems performing many repetitive operations. As part of the authors work on subsetting data stored on Amazon Web Service (AWS) Simple Storage Service (S3), we developed technology to read portions of otherwise monolithic data files. This enables the formation of virtual shards for user in subsetting data stored in HDF5 (hierarchical data format, version 5) files. This same tool can be used to form aggregations that combine data stored in many HDF5 files when those files are stored on S3. The nature of the virtual sharding and the algorithm that exploits it for subsetting is such that it can also be used for aggregation with the need for many of the repetitive operations required by the per file aggregation techniques. We will present timing information that demonstrates the flexibility of this approach. However, the lessons learned is that while this is a useful result in and of itself, these very same techniques can be applied in other contexts where data are stored in services and on media other than S3. For example, this same technique can be applied to data stored on spinning disk. Pushing the envelope for S3 forced a reexamination of our data access techniques which lead to unexpected positive benefits.

Gallagher, James↗
NASA NTRSDate not supplied

VTXO: the Virtual Telescope for X-ray Observations

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with 1 km focal length and with nearly 50 milli-arcsecond angular resolution. Laboratory characterization of PFLs have demonstrated near diffraction-limited angular resolution in the X-ray band, but they require long focal lengths to achieve this quality of imaging. VTXO is formed by using precision formation flying of two SmallSats: a smaller, 6U OpticsSat that houses the PFLs and navigation beacons while a larger, ESPA-class DetectorSat contains an X-ray camera, a charged-particle radiation monitor, a precision star tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly-elliptical supersynchronous geostationary transfer orbit to allow the inertial formation to form and hold around the 90,000 km apogee for 10 hours of the 32.5-hour orbit with nearly a year mission lifetime. The guidance, navigation, and control (GN&C) for the formation flying uses standard CubeSat avionics packages, a precision star tracker, imaging beacons on the Optics Sat, and a radio ranging system that also serves as an inter-satellite communication link. VTXO’s fine angular resolution enables measuring the environments nearly an order of magnitude closer to the central engines of bright compact X-ray sources compared to the current state of the art. This X-ray imaging capability allows for the study of the effects of dust scattering nearer to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure nearer to the compact object in X-ray novae such as Cyg X-1and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The In this paper, the VTXO science performance, SmallSat and instrument designs,and mission description is be described. The VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions.

John Krizmanic↗
NASA NTRSDate not supplied

Building Capacity for Policy-makers in a Virtual Setting: Providing Tools to Analyze Wildfire Smoke Plumes and Their Impacts

The NASA DEVELOP Program conducted 10-week long feasibility projects in a remote work setting, including partnering with The Nature Conservancy’s Washington Chapter and the Puget Sound Clean Air Agency to investigate wildfire smoke from 2000 - 2020 in the Pacific Northwest using satellite-derived data. The team engaged with platforms for collaboration both internally with NASA affiliates and externally with community organizations. Working from multiple states, the team members used a variety of software including Google Meet, Microsoft Teams, and Google Earth Engine to foster communication and work with data in a shared virtual environment. Throughout the project, the team learned that executing the project in a distanced work setting made it easier to reach out to scientists across the country for expertise and guidance. To study changes in air quality resulting from wildfire smoke, the team utilized data from NASA’s Fire Information from Resource Management System (FIRMS) and the ESA’s Sentinel-5 TROPOspheric Monitoring Instrument (TROPOMI). The team created a Google Earth Engine web-based tool, “Plume Hazards and Observations of Emissions by Navigating an Interactive eXplorer” (PHOENIX), to visualize changes in pollutants and aerosol optical depth after fire events. The potential relationship between plume height and fire radiative power was evaluated by using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra satellites and NASA’s Multi-angle Imaging SpectroRadiometer (MISR) aboard Terra with the MISR INteractive eXplorer (MINX). The PHOENIX smoke assessment tool and science communication infographics will be shared electronically with the partner organizations. Furthermore, the team introduced the partners to MINX and will provide a tailored tutorial that included a recorded video and a written component with a live virtual workshop. These resources build capacity for further research and education on wildfire smoke and air quality within communities.

NASA DEVELOP↗
NASA NTRSDate not supplied

VTXO: The Virtual Telescope for X-ray Observations

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with⇠1 km focal length and with⇠50 milli-arc second angular resolution. VTXO is formed by using precision formation flying of two SmallSats: a smaller OpticsSat that houses the PFLs and navigation beacons while a larger DetectorSat contains an X-ray camera, a precision start tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly elliptical super synchronous orbit allow the formation to hold in an inertial frame around the 90,000 km apogee for 10 hours of the 32.5 hour orbit with nearly a year mission lifetime. VTXO’s fine angular resolution enables measuring the environments close to the central engines of bright compact X-ray sources. This X-ray imaging capability allows for the study of the effects of dust scattering near to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure near to the compact object in X-ray novae such as Cyg X-1 and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The VTXO SmallSat and instrument designs, mission parameters, and science performance are described. VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions

John F Krizmanic↗
NASA NTRS2018

Virtual Sensors Determined Through Machine Learning

We propose a method that increases the capability of a conventional sensor, transforming it into an enhanced virtual sensor. This paper focuses on a virtual thermal Infrared Radiation (IR) sensor based on a conventional visual (RGB) sensor. The estimation of thermal IR images can enhance the ability of terrain classification, which is crucial for autonomous navigation of rovers. The estimate in IR from visual band has inherent limitations, as these are different bands, yet correlations between visual RGB and thermal IR images exist, as different terrains, which visually may appear different, also have different thermal inertia. This paper describes the developed deep learning-based algorithm that estimates thermal IR images from RGB images of terrains, providing the feasibility of the idea with average 1.21 error [degree Celsius].

Torresen, Jim↗
NASA NTRS2018

Virtual Sensors Determined Through Machine Learning

We propose a method that increases the capability of a conventional sensor/instrument, transforming it into an enhanced virtual sensor/instrument. This paper focuses on a virtual thermal IR sensor based on a conventional visual (RGB) sensor. The estimation of thermal IR images can enhance the ability of terrain classification, which is crucial for autonomous navigation of rovers. The estimate in IR from visual band has inherent limitations, as these are different bands, yet correlations between visual/RGB and thermal IR images exist, as different terrains, which visually may appear different, also have different thermal inertia. We developed a deep learning-based algorithm that estimates thermal IR images from RGB images of terrains, providing the feasibility of the idea with average 1.21 error [degree].

Kurazume, Ryo↗
NASA NTRS2021

Virtual Reality Training Lab

This video highlights the Virtual Reality Laboratory. It shows off the different systems the lab supports for crew training including Charlotte Mass Handling, Robotics and hardware checkout and SAFER. It focuses on the SAFER (Simplified Aid for EVA Rescue) system and shows briefly how to perform a SAFER rescue in Virtual Reality.

Simulation↗
NASA NTRSDate not supplied

Virtual International Satellite Training Experiences for Weather, Marine, Climate, and Environmental Applications

Since January 2020, National Oceanic and Atmospheric Administration, its Cooperative Institutes, and international partners provided over 15,000 hours of virtual satellite focused on user engagement. The training provides and guides users to analyze a large variety of satellite data for weather, climate, and environmental applications and decision support. The training team reached users in over 50 countries via monthly Regional Focus Group and special topic sessions, World Meteorological Organization Regional Association III and IV Workshops, and American Meteorological Society Short Courses. The training sessions were offered in both English and Spanish covering such themes as GOES-R, JPSS and other satellite capabilities, data access using GEONET Cast, data display, and user applications. The training also emphasized the operational application of satellite observations for cross-disciplinary topics like heavy rain and hail events, aviation hazards, fire and smoke detection, volcanic eruptions and ash monitoring, climate indices, monitoring coral reef health, and in tsunami preparedness. The presentation will highlight the methods used and challenges posed by the various training approaches and future modifications to adapt to the virtual environment during training sessions planned for the next year.

Satellite Training↗
NASA NTRSDate not supplied

A Virtual Reality Planning Environment for High-Risk, High-Latency Teleoperation

Teleoperation of robots in space is challenging due to high latency and limited workspace visibility. Previously, the Interactive Planning and Supervised Execution (IPSE) and Augmented Virtuality systems were developed to reduce failure risk. These tools were visualized on a 3D da Vinci surgical console and operated using the da Vinci manipulators or visualized on conventional monitors and operated with a keyboard and mouse. Experimental studies indicated operator preference for the latter. In this work, we develop a 3D virtual reality (VR) interface for IPSE, implemented on a Meta Quest 2 head-mounted display (HMD), and evaluate it against the prior 2D, keyboard-and-mouse-based interface. The results demonstrate improved operator load with the 3D VR interface, with no decrease in task performance, while also providing cost and portability benefits compared to the conventional 2D interface.

Will Pryor↗
NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗
DOE OSTI2020

Virtual Slit for Improved Resolution in Longitudinal Emittance Measurement

A technique to reduce point-spread originating from physical slit width in emittance measurements is described. This technique is developed to improve phase resolution in a longitudinal emittance apparatus consisting of a dipole magnet, energy-selecting slit and bunch shape monitor. In this apparatus, the energy and phase resolutions are directly proportional to the width of the slit, but the virtual slit method enables sub-slit resolution. The bunch phase profile is measured at two points in the energy distribution with a separation less than the physical slit width. The difference of these two profiles is used to reconstruct the profile from a virtual slit of width equal to the separation.

Ruisard, Kiersten↗
DOE OSTI2021

Employer Info Session: Virtual Solar Career Fair

Leading up to the 4/08 virtual career fair, this session featured an overview of the Solar Ready Vets program, a presentation by Hiring Our Heroes staff with tips on navigating the virtual career fair platform, and Q&A with solar employers interested in participating in the event.

Long, Becky↗
DOE OSTI2021

Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.

Xie, Chenhao↗
DOE OSTI2021

Virtual Framework for Science Federations with Instruments Access and Control

Experimental science workflows require federations of geographically dispersed science instruments and computing systems connected over a wide-area network. We develop a Virtual Infrastructure Twin (VIT) framework, which is a digital twin of the physical infrastructure that utilizes network virtualization and containerization technologies to support the development and testing of science workflow codes. Using VIT, we illustrate the access to instruments via EPICS system and the orchestration of containerized computations across federation computing systems. We also present a machine learning method to convert VIT throughput measurements to closely match the corresponding physical testbed measurements.

Rao, Nageswara↗