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NASA NTRSDate not supplied

Game Based Learning For Earth Science Applications Training

Current NASA Earth capacity development programs employ mechanisms ranging from online resource sharing, and virtual and in-person trainings to share knowledge. While these programs are highly successful at engaging individuals around the world – in 2018, over 8000 individuals and over 2000 institutions from all 50 US states and over 140 countries were engaged through over 150 projects and trainings – user feedback has highlighted the desire for expanded hands-on, practical experiences in incorporating NASA EO insights with localized data and actions. We aim to address this gap by leveraging the benefits of game-based learning to build user skills in integrating NASA and local EO data to guide decisions for climate resiliency and hazard planning. This project is being executed as a two-phase crowdsourced challenge: 1) Phase 1 will require a well-researched product concept that reflects an understanding of NASA’s Earth data and tools and user needs, and proposes an innovative and interactive game or extended reality experience to train users in identifying relevant NASA data and applying insights to their climate resiliency decisions; 2) Winners of Phase 1 will be provided seed funding to develop a working prototype of the product. We aim to award 1-3 final winners to support the development of more than one game, thereby ensuring that NASA's diverse audiences around the world can access training games that best suit their needs and capabilities. This EO training game project fits in the NASA Earth Science Applied Sciences Program’s Capacity Development Program, contributing to the program mission of “helping people around the world better understand [NASA’s Earth] data and find ways to use them” (https://appliedsciences.nasa.gov/what-we-do/capacity-building). The final training game will complement existing programmatic activities of workforce development, trainings, and collaborative projects, while providing the unique value of providing interactive experiences to users and collecting real-time data and feedback to improve NASA’s Earth applications’ products and services related to climate resilience.

Human centered design↗

NASA NTRS1999

New approaches to virtual environment surgery

This research focused on two main problems: 1) low cost, high fidelity stereoscopic imaging of complex tissues and organs; and 2) virtual cutting of tissue. A further objective was to develop these images and virtual tissue cutting methods for use in a telemedicine project that would connect remote sites using the Next Generation Internet. For goal one we used a CT scan of a human heart, a desktop PC with an OpenGL graphics accelerator card, and LCD stereoscopic glasses. Use of multiresolution meshes ranging from approximately 1,000,000 to 20,000 polygons speeded interactive rendering rates enormously while retaining general topography of the dataset. For goal two, we used a CT scan of an infant skull with premature closure of the right coronal suture, a Silicon Graphics Onyx workstation, a Fakespace Immersive WorkBench and CrystalEyes LCD glasses. The high fidelity mesh of the skull was reduced from one million to 50,000 polygons. The cut path was automatically calculated as the shortest distance along the mesh between a small number of hand selected vertices. The region outlined by the cut path was then separated from the skull and translated/rotated to assume a new position. The results indicate that widespread high fidelity imaging in virtual environment is possible using ordinary PC capabilities if appropriate mesh reduction methods are employed. The software cutting tool is applicable to heart and other organs for surgery planning, for training surgeons in a virtual environment, and for telemedicine purposes.

NASA Discipline Neuroscience↗

NASA NTRS2006

Diurnal Variability of Vertical Structure from a TRMM Passive Microwave "Virtual Radar" Retrieval

Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).

Boccippio, Dennis J.↗

NASA NTRS2002

Training, transfer, and retention of three-dimensional spatial memory in virtual environments

Human orientation requires one to remember and visualize spatial arrangements of landmarks from different perspectives. Astronauts have reported difficulties remembering relationships between environmental landmarks when imagined in arbitrary 3D orientations. The present study investigated the effects of strategy training on humans' 1) ability to infer their orientation from landmarks presented ahead and below, 2) performance when subsequently learning a different array, and 3) retention of configurational knowledge over time. On the first experiment day, 24 subjects were tested in a virtual cubic chamber in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures, they had to memorize the spatial relationships among the six pictures around them and learn to predict the direction to a specific picture when facing any view direction, and in any roll orientation. Half of the subjects ("strategy group") were taught methods for remembering picture groupings, while the remainder received no such training ("control group"). After learning one picture array, the procedure was repeated in a second. Accuracy (% correct) and response time learning curves were measured. Performance for the second array and configurational memory of both arrays were also retested 1, 7, and 30 days later. Results showed that subjects "learned how to learn" this generic 3D spatial memory task regardless of their relative orientation to the environment, that ability and configurational knowledge was retained for at least a month, that figure rotation ability and field independence correlate with performance, and that teaching subjects specific strategies in advance significantly improves performance. Training astronauts to perform a similar generic 3D spatial memory task, and suggesting strategies in advance, may help them orient in three dimensions.

Non-NASA Center↗

NASA NTRS2001

Simulation of Physical Experiments in Immersive Virtual Environments

An object-oriented event-driven immersive Virtual environment is described for the creation of virtual labs (VLs) for simulating physical experiments. Discussion focuses on a number of aspects of the VLs, including interface devices, software objects, and various applications. The VLs interface with output devices, including immersive stereoscopic screed(s) and stereo speakers; and a variety of input devices, including body tracking (head and hands), haptic gloves, wand, joystick, mouse, microphone, and keyboard. The VL incorporates the following types of primitive software objects: interface objects, support objects, geometric entities, and finite elements. Each object encapsulates a set of properties, methods, and events that define its behavior, appearance, and functions. A container object allows grouping of several objects. Applications of the VLs include viewing the results of the physical experiment, viewing a computer simulation of the physical experiment, simulation of the experiments procedure, computational steering, and remote control of the physical experiment. In addition, the VL can be used as a risk-free (safe) environment for training. The implementation of virtual structures testing machines, virtual wind tunnels, and a virtual acoustic testing facility is described.

Noor, Ahmed K.↗

NASA NTRS2010

Virtual VMASC: A 3D Game Environment

The advantages of creating interactive 3D simulations that allow viewing, exploring, and interacting with land improvements, such as buildings, in digital form are manifold and range from allowing individuals from anywhere in the world to explore those virtual land improvements online, to training military personnel in dealing with war-time environments, and to making those land improvements available in virtual worlds such as Second Life. While we haven't fully explored the true potential of such simulations, we have identified a requirement within our organization to use simulations like those to replace our front-desk personnel and allow visitors to query, naVigate, and communicate virtually with various entities within the building. We implemented the Virtual VMASC 3D simulation of the Virginia Modeling Analysis and Simulation Center (VMASC) office building to not only meet our front-desk requirement but also to evaluate the effort required in designing such a simulation and, thereby, leverage the experience we gained in future projects of this kind. This paper describes the goals we set for our implementation, the software approach taken, the modeling contribution made, and the technologies used such as XNA Game Studio, .NET framework, Autodesk software packages, and, finally, the applicability of our implementation on a variety of architectures including Xbox 360 and PC. This paper also summarizes the result of our evaluation and the lessons learned from our effort.

Manepalli, Suchitra↗

NASA NTRS2002

Intelligent Virtual Station (IVS)

The Intelligent Virtual Station (IVS) is enabling the integration of design, training, and operations capabilities into an intelligent virtual station for the International Space Station (ISS). A viewgraph of the IVS Remote Server is presented.

Source record↗

NASA NTRS1994

Surgery applications of virtual reality

Virtual reality is a computer-generated technology which allows information to be displayed in a simulated, bus lifelike, environment. In this simulated 'world', users can move and interact as if they were actually a part of that world. This new technology will be useful in many different fields, including the field of surgery. Virtual reality systems can be used to teach surgical anatomy, diagnose surgical problems, plan operations, simulate and perform surgical procedures (telesurgery), and predict the outcomes of surgery. The authors of this paper describe the basic components of a virtual reality surgical system. These components include: the virtual world, the virtual tools, the anatomical model, the software platform, the host computer, the interface, and the head-coupled display. In the chapter they also review the progress towards using virtual reality for surgical training, planning, telesurgery, and predicting outcomes. Finally, the authors present a training system being developed for the practice of new procedures in abdominal surgery.

Rosen, Joseph↗

NASA NTRS2025

What's Behind This Door: Prototype Immersive Technology (PIT) Lab - Episode

This is one video in the "What's Behind This Door?" series. This series is designed to give the public a behind-the-scenes look at the various analogs and testing facilities used by NASA on our journey to return to the Moon. This particular episode explores JSC's PIT Lab, which uses a virtual reality environment for training subjects by simulating various environments such as the lunar surface.

Diana L Rodgers↗

NASA NTRS1994

Stereoscopic, Force-Feedback Trainer For Telerobot Operators

Computer-controlled simulator for training technicians to operate remote robots provides both visual and kinesthetic virtual reality. Used during initial stage of training; saves time and expense, increases operational safety, and prevents damage to robots by inexperienced operators. Computes virtual contact forces and torques of compliant robot in real time, providing operator with feel of forces experienced by manipulator as well as view in any of three modes: single view, two split views, or stereoscopic view. From keyboard, user specifies force-reflection gain and stiffness of manipulator hand for three translational and three rotational axes. System offers two simulated telerobotic tasks: insertion of peg in hole in three dimensions, and removal and insertion of drawer.

Kim, Won S.↗

NASA NTRS2009

Small Earth Observing Satellites Flying with Large Satellites in the A-Train

This paper/poster presents a real-life example of the benefits of flying small satellites with other satellites, large or small, and vice versa. Typically, most small satellites fly payloads consisting of one or two instruments and fly in orbits that are independent from that of other satellites. The science data from these satellites are either used in isolation or correlated with instrument data from other satellites. Data correlation with other satellites is greatly improved when the measurements of the same point or air mass are taken at approximately the same time. Scientists worldwide are beginning to take advantage of the opportunities for improved data correlation, or coincidental science, offered by the international Earth Observing Constellation known as the A-Train (sometimes referred to as the Afternoon Constellation). Most of the A-Train satellites are small - the A-Train is anchored by two large NASA satellites (EOS-Aqua and EOS-Aura), but consists also of 5 small satellites (CloudSat, CALIPSO, PARASOL, OCO and Glory these last two will join in 2009). By flying in a constellation, each mission benefits from coincidental observations from instruments on the other satellites in the constellation. Essentially, from a data point of view, the A-Train can be envisioned as a single, virtual science platform with multiple instruments. Satellites in the A-Train fly at 705 km in sun-synchronous orbits. Their mean local times at the equator are within seconds to a few minutes of each other. This paper describes the challenges of operating an international constellation of independent satellites from the U.S. and Europe to maximize the coincidental science opportunities while at the same time minimizing the level of operational interactions required between team members. The A-Train mission teams have been able to demonstrate that flying as members of an international constellation does not take away the flexibility to accommodate new requirements. Specific examples will be cited, including CloudSat's relocation (to accommodate a new viewing angle for the CALIPSO satellite), Glory's replan to move closer to PARASOL, and OCO's long term plans to minimize on-orbit operations costs while maintaining safety. In all cases, safety is ensured, science returns are enhanced, and operational flexibility is retained to the maximum extent possible.

Kelly, Angelita C.↗

NASA NTRS2011

ARC+(Registered Trademark) and ARC PC Welding Simulators: Teach Welders with Virtual Interactive 3D Technologies

123 Certification Inc., a Montreal based company, has developed an innovative hands-on welding simulator solution to help build the welding workforce in the most simple way. The solution lies in virtual reality technology, which has been fully tested since the early 90's. President and founder of 123 Certification Inc., Mr. Claude Choquet Ing. Msc. IWE. acts as a bridge between the welding and the programming world. Working in these fields for more than 20 years. he has filed 12 patents world-wide for a gesture control platform with leading edge hardware related to simulation. In the summer of 2006. Mr Choquet was proud to be invited to the annual IIW International Weld ing Congress in Quebec City to launch the ARC+ welding simulator. A 100% virtual reality system and web based training center was developed to simulate multi process. multi-materiaL multi-position and multi pass welding. The simulator is intended to train welding students and apprentices in schools or industries. The welding simulator is composed of a real welding e[eetrode holder (SMAW-GTAW) and gun (GMAW-FCAW). a head mounted display (HMD), a 6 degrees of freedom tracking system for interaction between the user's hands and head. as well as external audio speakers. Both guns and HMD are interacting online and simultaneously. The welding simulation is based on the law of physics and empirical results from detailed analysis of a series of welding tests based on industrial applications tested over the last 20 years. The simulation runs in real-time, using a local logic network to determine the quality and shape of the created weld. These results are based on the orientation distance. and speed of the welding torch and depth of penetration. The welding process and resulting weld bc.1d are displayed in a virtual environment with screenplay interactive training modules. For review. weld quality and recorded process values can be displayed and diagnosed after welding. To help in the le.tming process, a learning curve for each student and each Virtual Welding Class'" can be plotted, for an instructor's review or a required third party evaluation.

Choquet, Claude↗

NASA NTRS2007

Adaptive Responses in Eye-Head-Hand Coordination Following Exposures to a Virtual Environment as a Possible Space Flight Analog

Virtual environments (VE) offer unique training opportunities, particularly for training astronauts and preadapting them to the novel sensory conditions of microgravity. Sensorimotor aftereffects of VEs are often quite similar to adaptive sensorimotor responses observed in astronauts during and/or following space flight. The purpose of this research was to compare disturbances in sensorimotor coordination produced by dome virtual environment display and to examine the effects of exposure duration, and repeated exposures to VR systems. The current study examined disturbances in eye-head-hand (EHH) and eye-head coordination. Preliminary results will be presented. Eleven subjects have participated in the study to date. One training session was completed in order to achieve stable performance on the EHH coordination and VE tasks. Three experimental sessions were performed each separated by one day. Subjects performed a navigation and pick and place task in a dome immersive display VE for 30 or 60 min. The subjects were asked to move objects from one set of 15 pedestals to the other set across a virtual square room through a random pathway as quickly and accurately as possible. EHH coordination was measured before, immediately after, and at 1 hr, 2 hr, 4 hr and 6 hr following exposure to VR. EHH coordination was measured as position errors and reaction time in a pointing task that included multiple horizontal and vertical LED targets. Repeated measures ANOVAs were used to analyze the data. In general, we observed significant increases in position errors for both horizontal and vertical targets. The largest decrements were observed immediately following exposure to VR and showed a fairly rapid recovery across test sessions, but not across days. Subjects generally showed faster RTs across days. Individuals recovered from the detrimental effects of exposure to the VE on position errors within 1-2 hours. The fact that subjects did not significantly improve across days suggests that in order to achieve dual adaptation of EHH coordination may require more than three training sessions. These findings provide some direction for developing training schedules for VE users that facilitate adaptation, support the idea that preflight training of astronauts may serve as useful countermeasure for the sensorimotor effects of space flight, and support the idea that VEs may serve as an analog for sensorimotor effects of spaceflight.

Harm, Deborah L.↗

NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRS1994

Virtual environment application with partial gravity simulation

To support manned missions to the surface of Mars and missions requiring manipulation of payloads and locomotion in space, a training facility is required to simulate the conditions of both partial and microgravity. A partial gravity simulator (Pogo) which uses pneumatic suspension is being studied for use in virtual reality training. Pogo maintains a constant partial gravity simulation with a variation of simulated body force between 2.2 and 10 percent, depending on the type of locomotion inputs. this paper is based on the concept and application of a virtual environment system with Pogo including a head-mounted display and glove. The reality engine consists of a high end SGI workstation and PC's which drive Pogo's sensors and data acquisition hardware used for tracking and control. The tracking system is a hybrid of magnetic and optical trackers integrated for this application.

Ray, David M.↗

NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses - What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Lucie Poulet↗

NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses: What Can We Infer from Current Analog and Space Missions?

Veggie↗