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NASA NTRS2004

Open-systems architecture of a standardized command interface chip-set for switching and control of a spacecraft power bus

The Jet Propulsion Laboratory (JPL) has developed a command interface chip-set that primarily consists of two mixed-signal ASICs'; the Command Interface ASIC (CIA) and Analog Interface ASIC (AIA). The Open-systems architecture employed during the design of this chip-set enables its use as both an intelligent gateway between the system's flight computer and the control, actuation, and activation of the spacecraft's loads, valves, and pyrotechnics respectfully as well as the regulator of the spacecraft power bus. Furthermore, the architecture is highly adaptable and employed fault-tolerant design methods enabling a host of other mission uses including reliable remote data collection. The objective of this design is to both provide a needed flight component that meets the stringent environmental requirements of current deep space missions and to add a new element to a growing library that can be used as a standard building block for future missions to the outer planets.

flight computers↗

NASA NTRS1987

A generic applications subroutine library for the MPP

A new methodology to increase the utility of the Massively Parallel Processor (MPP) was developed, and is presented as an addition to the current methods of using the MPP. This methodology provides for the development of an MPP side abstraction layer that is callable from any host side high level language. Routines in the abstraction layer have the option of using a powerful software tool for accessing the stager as virtual memory. An additional abstraction layer that allows for remote access to the MPP via DECnet is discussed. This integrated approach to programming the MPP is a valuable tool for the implementation of interactive user driver systems that require the computational capabilities of the MPP as well as a controlled user view. It is expected that this methodology will be used to integrate the MPP into many such systems, and thus promote greater use of the MPP by scientific researchers who are accustomed to user friendly environments.

Gough, Michael L.↗

NASA NTRSDate not supplied

Lunar Science Investigations and Exploration in Celestial Mapping System

Introduction: As NASA expands the mission portfolio on the lunar surface, there is a need for applications with a broad range of analytical and functional capabilities that can be simultaneously deployed onto multiple mobile and desktop platforms to perform in-situ operations and hence enabling extensive Lunar exploration. Celestial Mapping System (CMS) [1,2] is developed to address the need for tools for science investigations, mission planning, operations and support for planetary sciences. Built on top of NASA WorldWind libraries, CMS can be simultaneously deployed onto multiple platforms, has the flexibility to update to the latest imagery and terrain datasets as they are being acquired (in real time) before and/or during the exploration mission and has the potential to enable traverse path planning suited for rovers, EVA and surface mobility units. It can provide critical functionalities such as equipment planning and optimized placement on Lunar surface, line of sight analysis to inform the coverage area for various equipment, powerful measurement tools based on 3D terrain, 3D COLLADA models to represent rovers, humans and equipment, visualization of derived mapping products (e.g. resource maps), and a data engine for hosting new observations that are not available in other contemporary lunar data tools [1]. Visualization of PSRs: The current presentation focuses on the work performed by the authors, to consume a unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [3]. This tool was developed in direct support of NASA's VIPER and Artemis programs to enhance the extremely low-light images of the interior of PSRs and provide the first-time ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the region near Nobili crater near Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Line of Sight Analysis and Traverse Planning in PSRs: We have developed an in-built line of sight analysis (LOS) tool in CMS that analyzes the terrain profile and obstructions and provides the visibility of a given terrain for a remote observer. This tool was utilized to perform viewshed analysis to investigate the area inside the PSR, a remote observer such as a rover could see without actually crossing the region. Figure 1 shows the viewshed analysis on the PSR in Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. Figure 1: (left) PSR image on top of a high-resolution mosaic of sunlit images of a crater, in Nobile region (right) Viewshed Analysis of the same PSR with observer location shown by yellow pin. This analysis was extended further to set different heights for various observers and then perform the viewshed analysis. Combining the different visibility profiles can help designing improved traverses within the crater. Future plans: Eventually, HORUS datasets will be integrated into CMS as a layer in selected lunar polar regions. Hazard Maps will then be created based on terrain analysis in those regions. This integration will enable multiple scientific and exploration applications, such as designing traverses within PSRs, analyzing potential landing and science mission targets, investigating the meter-scale geomorphology of PSRs, including craters, boulder, surface roughness, mass wasting features and other indications of the presence of water-ice and other volatiles. Acknowledgments: CMS developers team including Kaitlyn J. Dickinson, Tyler A. Lucarz, Tyler W. Choi from USRA, NASA WorldWind Advisory team including Mark Peterson and Guillermo Miguel Del Castillo, HORUS team member V.T. Bickel, Robinson, M., LRO MOON LROC 2 EDR V1.0, LRO-L-LROC-2-EDR-V1.0, NASA Planetary Data System (PDS), 2009. https://doi.org/10.17189/1520643 References: [1] https://celestial.arc.nasa.gov [2] Agrawal et. al. “Celestial Mapping System for Lunar Surface Mapping and Analytics”, Lunar Surface Innovation Consortium, 2021 [3] Bickel V. et al. (2021) Nat Commun 12, 5607

Lunar Mapping↗

NASA NTRSDate not supplied

Validation of the NASA Electrical Power System – Sizing and Analysis Tool (EPS-SAT)

The electrification of aircraft propulsion systems has opened the design space for engineers by allowing for unique and highly specialized propulsion system and vehicle designs. NASA developed the Electrical Power System – Sizing and Analysis Tool (EPS-SAT) to conduct high-level trade studies and sensitivity studies on the various propulsion system designs that can be implemented in electrified aircraft. The results of these studies would be used to better direct investment dollars and determine strengths and weaknesses of propulsion system designs. In this paper, the results of the EPS-SAT tool were validated with hardware data extracted from the NASA Revolutionary Vertical Lift Technology (RVLT) Advanced Reconfigurable Electric Aircraft Lab (AREAL). Updated performance maps were added to the EPS-SAT library so that high-fidelity results could be calculated.

Patrick A. Hanlon↗

NASA NTRS1997

Develop Advanced Nonlinear Signal Analysis Topographical Mapping System

During the development of the SSME, a hierarchy of advanced signal analysis techniques for mechanical signature analysis has been developed by NASA and AI Signal Research Inc. (ASRI) to improve the safety and reliability for Space Shuttle operations. These techniques can process and identify intelligent information hidden in a measured signal which is often unidentifiable using conventional signal analysis methods. Currently, due to the highly interactive processing requirements and the volume of dynamic data involved, detailed diagnostic analysis is being performed manually which requires immense man-hours with extensive human interface. To overcome this manual process, NASA implemented this program to develop an Advanced nonlinear signal Analysis Topographical Mapping System (ATMS) to provide automatic/unsupervised engine diagnostic capabilities. The ATMS will utilize a rule-based Clips expert system to supervise a hierarchy of diagnostic signature analysis techniques in the Advanced Signal Analysis Library (ASAL). ASAL will perform automatic signal processing, archiving, and anomaly detection/identification tasks in order to provide an intelligent and fully automated engine diagnostic capability. The ATMS has been successfully developed under this contract. In summary, the program objectives to design, develop, test and conduct performance evaluation for an automated engine diagnostic system have been successfully achieved. Software implementation of the entire ATMS system on MSFC's OISPS computer has been completed. The significance of the ATMS developed under this program is attributed to the fully automated coherence analysis capability for anomaly detection and identification which can greatly enhance the power and reliability of engine diagnostic evaluation. The results have demonstrated that ATMS can significantly save time and man-hours in performing engine test/flight data analysis and performance evaluation of large volumes of dynamic test data.

Jong, Jen-Yi↗

NASA NTRS2024

Quantifying Risk to Improve Medical System Design for Long Duration Artemis Missions: A Demonstration of NASA's IMPACT Tradespace Analysis Tool

BACKGROUND NASA’s human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. A greater distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and constraints on the evacuation of ill or injured crew. Mass, volume, and power will be limited while higher demands will be placed on the crews to manage medical events. NASA’s Moon to Mars exploration strategy outlines increasingly complex Artemis missions both in terms of duration and operations. In these more challenging deep space missions, it is important to quantitatively estimate the human system risk attributable to medical conditions and use these estimates to advance medical system design. METHODS IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a probabilistic risk assessment (PRA) and tradespace analysis tool developed by NASA to advance exploration mission medical system design. IMPACT v1.0 includes a novel evidence library baselined to exploration environments; an expanded list of 119 medical conditions; a large increase in the number of medical resources and the flexibility of their use; and the ability for rapid and iterative analysis. Medical system risk estimates include loss of crew life, consideration of the need for return to definitive care (medical evacuation), and an estimate of crew time affected due to medical conditions. A notional long duration lunar orbit and lunar surface design reference mission (DRM) was chosen with a 4-astronaut crew to mimic a foundational exploration Artemis mission. The DRM profile includes outbound transit on Orion, Gateway space station rendezvous in lunar orbit, 6 months on the Lunar surface with extravehicular activity (EVA), return rendezvous with Gateway, and transit back to Earth. RESULTS/DISCUSSION: Overall, IMPACT successfully quantified medical risk and derived an optimal medical system to support crew on a long duration lunar mission. In this DRM, the calculated loss of crew life from a medical event was 0.008 events per mission, risk of potential need for evacuation was 0.30 events per mission, and cumulative crew time affected by medical conditions was 103 days. The medical conditions that most contributed to medical risk were decompression sickness, trauma, and respiratory failure. The conditions that had the largest effects on crew performance included musculoskeletal injuries and lunar dust exposure. The IMPACT-generated medical system included resources that target the most common and highest risk conditions and performed as expected. This demonstrates the value of the IMPACT tool in medical system design for human exploration spaceflight missions.

Arian Anderson↗

NASA NTRS2017

'Fly Like This': Natural Language Interface for UAV Mission Planning

With the increasing presence of unmanned aerial vehicles (UAVs) in everyday environments, the user base of these powerful and potentially intelligent machines is expanding beyond exclusively highly trained vehicle operators to include non-expert system users. Scientists seeking to augment costly and often inflexible methods of data collection historically used are turning towards lower cost and reconfigurable UAVs. These new users require more intuitive and natural methods for UAV mission planning. This paper explores two natural language interfaces - gesture and speech - for UAV flight path generation through individual user studies. Subjects who participated in the user studies also used a mouse-based interface for a baseline comparison. Each interface allowed the user to build flight paths from a library of twelve individual trajectory segments. Individual user studies evaluated performance, efficacy, and ease-of-use of each interface using background surveys, subjective questionnaires, and observations on time and correctness. Analysis indicates that natural language interfaces are promising alternatives to traditional interfaces. The user study data collected on the efficacy and potential of each interface will be used to inform future intuitive UAV interface design for non-expert users.

Chandarana, Meghan↗

NASA NTRS2021

BUMPER: A Tool for Analyzing Spacecraft Micrometeoroid and Orbital Debris Risk

“Bumper” is a computer program for analyzing spacecraft micrometeoroid and orbital debris (MMOD) risk. Bumper was developed in the late-1980s and has been continuously maintained and used since. The user base has grown from a few government entities now include numerous commercial entities as well. The National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) group is responsible for all aspects of the Bumper software. Bumper has been used to characterize MMOD risk on many spacecraft. All of the International Space Station (ISS) modules, visiting vehicles and numerous external components and systems have been analyzed. Bumper was used to analyze the Space Shuttle, Orion, and many space probes, telescopes and satellites. Bumper is also being used to analyze future spacecraft such as the Deep Space Gateway (DSG) and Mars Sample Return (MSR) missions. The Bumper Configuration Control Board (CCB) ensures that all changes to the code are approved, reviewed, and documented. The current Bumper version – “Bumper 3” – is a Fortran executable that utilizes a 64-bit architecture. Bumper has numerous features that make it a powerful tool for analyzing spacecraft MMOD risk. Bumper uses the latest orbital debris and meteoroidenvironment models. Bumper also has a large library of ballistic limit “damage” equations available that can be used for a wide variety of MMOD shielding configurations. Bumper can also handle large spacecraft finite elementmodels (FEMs) and conducts checks of the model. This paper introduces Bumper and the MMOD risk analysis process using a simplified cube-shaped spacecraft model

hypervelocity↗

NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗

NASA NTRS1997

Desktop Access to Full-Text NACA and NASA Reports: Systems Developed by NASA Langley Technical Library

NASA Langley Technical Library has been involved in developing systems for full-text information delivery of NACA/NASA technical reports since 1991. This paper will describe the two prototypes it has developed and the present production system configuration. The prototype systems are a NACA CD-ROM of thirty-three classic paper NACA reports and a network-based Full-text Electronic Reports Documents System (FEDS) constructed from both paper and electronic formats of NACA and NASA reports. The production system is the DigiDoc System (DIGItal Documents) presently being developed based on the experiences gained from the two prototypes. DigiDoc configuration integrates the on-line catalog database World Wide Web interface and PDF technology to provide a powerful and flexible search and retrieval system. It describes in detail significant achievements and lessons learned in terms of data conversion, storage technologies, full-text searching and retrieval, and image databases. The conclusions from the experiences of digitization and full- text access and future plans for DigiDoc system implementation are discussed.

Ambur, Manjula Y.↗

NASA NTRSDate not supplied

Low SWaP Onboard Satellite Navigation, Guidance, and Control Technology

Onboard autonomy is a necessity for responsive space operations. Autonomous navigation, guidance, and control (NGC) enables space missions to reduce their dependence on high demand ground assets and costly ground personnel. It also allows for in-situ decision making and higher return on mission data. A flight software and hardware system providing this capability, called “autoNGC,” is currently being developed at NASA Goddard Space Flight Center for infusion into multiple future missions. The first build of autoNGC, providing autonomous navigation for lunar orbiting spacecraft, is targeted for completion by Fall 2024. It provides sensor fusion of multiple measurement types including pseudo-range from a weak signal Global Navigation Satellite Service (GNSS) receiver, 1-way and 2-way direct to Earth (DTE) range and Doppler, bearing and range from optical camera sensed images, and an accelerometer. AutoNGC is also being targeted for future missions that involve small body proximity operations, Sun Earth Libration point orbits, and distributed systems missions (DSMs) including those at outer planets. AutoNGC flight software is being built upon the plug-and-play architecture of the core Flight System (cFS) [Ref. 1]. Figure (Slide 7) shows the message-based software bus layout of various software applications (“apps”) consisting of the standard cFS apps and autoNGC interface apps and libraries. Accurate onboard navigation and timing is obtained through the Goddard Enhanced Onboard Navigation System (GEONS) software library [Ref. 2], which fuses different measurement types through an extended Kalman filter (EKF) framework. Optical measurements that are ingested in GEONS are provided by the cFS Goddard Image Analysis and Navigation Tool (cGIANT) app [Ref. 3]. This app processes optical images to extract the bearing angles of the centroid of the imaged body (near or far), the range to the imaged body, and/or of the features on the surface of a body to perform terrain relative navigation (TRN). Measurement of range to the body’s center of mass can also be derived from the detection of the limb. The first build of autoNGC for a lunar orbiting spacecraft is a minimal size, weight, and power (SWaP) hardware design allowing for inclusion into CubeSats and SmallSat-size class buses. Advancements in miniaturized space processors, such as the SpaceCube 3.0 Mini and the SpaceCube Mini-Z [Ref. 4] are utilized for low SWaP while maintaining a high level of performance. Figure (Slide 11) shows the composition of the first autoNGC build. The current enclosure design has dimensions 12 cm x 17 cm x 13.5 cm. The box mass is expected to be less than 2 kg, and the nominal power is 21 W. The hardware interfaces are designed for flexibility with a variety of sensor inputs. The achievable navigation performance depends on the sensors utilized, including the onboard clock for 1-way pseudo-range measurements. Analysis using a configuration that consists of weak signal GPS, TRN, and 1-way DTE has shown position and velocity accuracies of 10 meters and 2 cm/s (3-σ ) RSS, respectively, with onboard time knowledge estimated to better than 13 ns (3-σ ), for a spacecraft in a representative 12-hour eccentric lunar orbit. Other measurement types such as x-rays from known pulsars (called XNAV) and cross-links can also be processed in GEONS. With the plug-and-play architecture of autoNGC, cFS apps can easily be added and replaced, even after launch. Goddard is actively seeking partners to collaborate in the development of additional capabilities for autoNGC, including industry, academia, and others across the US Government. Plans are being formulated to make the autoNGC software platform available for use by any US government organization to leverage the non-recurring engineering associated with the development of onboard autonomous NGC 3 capabilities. As advancements in space qualified sensors, microprocessors, and algorithms are made, the autoNGC platform provides a ready starting point for inclusion of these technologies.

C. J. Gramling↗

NASA NTRS2024

Creating Camera Controls for First-Person Camera in VulkanSceneGraph

The way users interact with a virtual, three-dimensional (3D) scene is heavily influenced by the way the camera used to view the scene is controlled. A first-person camera is a common form of camera control for computer programs. Its role is to create an immersive viewer experience, which allows users to traverse a scene as they might in the real world. Allowing for the implementation of the first-person camera makes for a more holistic, well-rounded way to interact with the visualization program. Accomplishing this involves mathematical calculations that define how the camera should be moved for the computer system. These movements equate to the rotation, translation, and scale of the changes. It should be noted that a computer does not inherently process what movement directions (left, right, up, or down) mean. The mathematical equations used define these principles in a way the computer can process. Further aspects to consider are detecting when the camera has moved and how far. This is most frequently accomplished through user |input through external devices. These devices, for the purpose of this report, include mouse input and keyboard input. Additionally, the in-development program this report is referencing works with the VulkanSceneGraph (VSG) library to create the scene and build the base of the camera controls. Although VSG is a powerful library with many capabilities, additional Application Programming Interfaces (APIs) might be needed during development to produce the desired results, as is the case in this program. Through combining proper mathematical calculations, utilizing additional APIs, and implementing the existing VSG library capabilities, implementing first-person camera controls is possible in a 3D scene.

Kristie O'Brien↗

NASA NTRSDate not supplied

Quantifying Medical Risk on a Long Duration Lunar Mission: A Demonstration of NASA’s IMPACT Tradespace Analysis Tool

Background NASA’s human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. The distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and constraints on the medical evacuation of astronauts. Mass, volume, power, and data will be limited while higher demands will be placed on the crew to manage medical care. NASA’s Moon to Mars exploration strategy lays out increasingly complex Artemis missions both in terms of duration and operations. In these more challenging deep space missions, it is important to quantitatively estimate the human medical risk to inform a traditional heuristic approach to medical risk. Prior tools have been developed for missions in low Earth orbit, but a new tool is required to plan for future exploration missions. Methods IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments; an expanded list of 119 medical conditions; the addition of medical resources; and the ability for rapid and iterative analysis. Medical system risk estimates include loss of crew life, consideration of the need for return to definitive care (medical evacuation), and an estimate of crew time affected due to medical conditions. A notional long duration lunar orbit and lunar surface design reference mission (DRM) was chosen with a 4-astronaut crew to represent a sustained exploration Artemis mission. Results/Discussion Overall, IMPACT successfully quantified medical risk and derived an optimized medical system to support crew on a long duration lunar mission. In this DRM, the calculated loss of crew life from a medical event was 0.008 events per mission, risk of potential need for evacuation was 0.30 events per mission, and cumulative crew time affected by medical conditions was 103 days. The medical conditions that most contributed to overall medical risk were decompression sickness, trauma conditions, and respiratory failure. The conditions that had the largest effects on crew performance included musculoskeletal injuries and lunar dust exposure. The IMPACT-generated medical system included resources that target the most common and highest risk conditions. This systematic analysis demonstrates the value of the IMPACT tool in medical system design for human exploration spaceflight missions.

Missions to Mars↗

NASA NTRS2024

Quantifying Medical Risk on a Long Duration Lunar Mission: A Demonstration of NASA’s IMPACT Tradespace Analysis Tool

Missions to Mars↗

NASA NTRS2023

Exploration Medical Capability IMPACT Medical Database – Medical Item Database (MedID) Content Development Methods

The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project encompasses a suite of computational tools that are used to inform systematic trade study evaluations and research prioritizations regarding the optimization of spaceflight medical systems, including the provision of risk assessment metrics, for a given design reference mission (DRM). The IMPACT Medical Database (IMPACT-MD) is the component that virtually houses the clinical and engineering data for medical conditions, capabilities, and resources used to support these analyses. In addition, IMPACT-MD also provides data to the associated SysML model to support the Level 2, Level 3, and Level 4 requirements development needed for IMPACT. IMPACT-MD contains an internal Evidence Library (EL) sub-component that hosts the clinical evidence, treatment and outcome metric data, and incidence data for each medical condition identified for exploration-class missions. Furthermore, a separate Medical Item Database (MedID) sub-component hosts the engineering data and physical attributes (e.g., mass, volume, power, etc.) associated with each medical resource item identified to address the requisite medical conditions. The IMPACT project is conducted under the Exploration Medical Capability (ExMC) element of the Human Research Program (HRP) within NASA's Space Operations Mission Directorate (SOMD). The reader is referred to the documents in Appendix A for a more complete overview of the IMPACT tool suite and functionality as well as further detail on any associated aspects of the project. The primary purpose of this document is to describe the methods utilized in the collection and verification of engineering data content during the project development phase for the MedID sub-component of the IMPACT-MD project. Engineering content data (both existing and new/future) will be stored in IMPACT-MD for use by the IMPACT project. This document will detail the content development and data credibility for the engineering data housed within the MedID subcomponent of IMPACT-MD, which is used to inform the IMPACT 1.0 project deliverables.

Exploration Medical Capability↗

NASA NTRSDate not supplied

Celestial Mapping System and Digital Lunar Library Initiative

We are preparing to create an interactive, global 3D lunar environment with integrated dataset and AI/ML tools to provide unique value to mission planners, scientists and the entire lunar community. This lunar environment will be based on NASA Ames Celestial Mapping System (CMS) [1] and Digital Lunar Library (DLL) Initiative. CMS provides a 3D virtual Lunar Globe with extensive user friendly tool sets, that include high resolution terrain visualization, elevation profiles, measurement kits, slope analysis, path optimization, line of sight analysis, equipment planning and placement tools and many other functionalities [1]. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The application is developed to provide situational and domain awareness on the Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on the Lunar surface. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. In the future, these capabilities will be enhanced by incorporating AI/ML tools and a plug-in architecture to enable customization by the user groups. With the help of DLL we will be able to : 1) Amplify the value of lunar information with AI-powered data enhancements 2) Acquire and integrate lunar data with AI-assisted georectification and homogenization 3) Analyze lunar data with advanced 3D visualization, intelligent search-by-example 4) Apply lunar data insights to specific use cases with an open plug-in architecture. The CMS-DLL initiative will have several potential use cases for NASA and the lunar community in general, including subsurface lava tube visualization and analysis, soil analysis, in-situ lunar resource visualization and representation on 3D globe, and data analytics for utilization. REFERENCES: [1] https://celestial.arc.nasa.gov/

3D Globe↗

NASA NTRS2016

Next Generation Flight Displays Using HTML5

The Human Integrated Vehicles and Environments (HIVE) lab at Johnson Space Center (JSC) is focused on bringing together inter-disciplinary talent to design and integrate innovative human interface technologies for next generation manned spacecraft. As part of this objective, my summer internship project centered on an ongoing investigation in to building flight displays using the HTML5 standard. Specifically, the goals of my project were to build and demo "flight-like" crew and wearable displays as well as create a webserver for live systems being developed by the Advanced Exploration Systems (AES) program. In parallel to my project, a LabVIEW application, called a display server, was created by the HIVE that uses an XTCE (XML (Extensible Markup Language) Telemetry and Command Exchange) parser and CCSDS (Consultative Committee for Space Data System) space packet decoder to translate telemetry items sent by the CFS (Core Flight Software) over User Datagram Protocol (UDP). It was the webserver's job to receive these UDP messages and send them to the displays. To accomplish this functionality, I utilized Node.js and the accompanying Express framework. On the display side, I was responsible for creating the power system (AMPS) displays. I did this by using HTML5, CSS and JavaScript to create web pages that could update and change dynamically based on the data they received from the webserver. At this point, I have not started on the commanding, being able to send back to the CFS, portion of the displays but hope to have this functionality working by the completion of my internship. I also created a way to test the webserver's functionality without the display server by making a JavaScript application that read in a comma-separate values (CSV) file and converted it to XML which was then sent over UDP. One of the major requirements of my project was to build everything using as little preexisting code as possible, which I accomplished by only using a handful of JavaScript libraries. As a side project, I created a model of the HIVE lab and Building 29 using SketchUp. I obtained the floorplans of the building from the JSC Geographic Information Systems (GIS), which were computer-aided design (CAD) files, and imported them into SketchUp. I then took those floorplans and created a 3D model of the building from them. Working in conjunction with the Hybrid Reality lab in Building 32, the SketchUp model was imported into Unreal Engine for use with the HTC Vive. Using the Vive, I was able to interact with the model I created in virtual reality (VR). The purpose of this side project was to be able to visualize potential lab layouts and mockup designs as they are in development in order to finalize design decisions. Pending approval, the model that I created will be used in the Build-As-You-Test: Can Hybrid Reality Improve the SE/HSI Design Process project in the fall. Getting the opportunity to work at NASA has been one of the most memorable experiences of my life. Over the course of my internship, I improved my programming and web development abilities substantially. I will take all the skills and experiences I have had while at NASA back to school with me in the fall and hope to pursue a career in the aerospace industry after graduating in the spring.

Greenwood, Brian↗

NASA NTRS2019

Safe and Optimal Techniques Enabling Recovery, Integrity, and Assurance

There is a trend in the aviation industry to go from federated to integrated computing systems. Combining a number of traditional stand-alone federated systems into an integrated common platform (called Integrated Modular Avionics, IMA) has the benefit of increased power efficiency, reduced support hardware, and reduced cabling. However, changing from federated to integrated has a significant impact on the system architecture and hence the process of how avionic systems are to be analyzed. Traditional approaches to safety analysis become inefficient when functional boundaries can no longer be assumed for failure independence and fault isolation. In this report, we describe a tool that we developed to accelerate the safety engineer's ability to perform safety analysis of IMA systems through modeling, as well as optimize the system engineer's ability to develop a system through architecture synthesis. This work was the result of a three-year research effort called SOTERIA (Safe and Optimal Techniques Enabling Recovery, Integrity, and Assurance). We developed a compositional modeling language that supports rapid development, modification, and evaluation of architectures. The modeling language is structured such that the end-user defines a library of components with information on component reliability, connectivity, and fault propagation logic. The system model is built by instantiating the components from the library, connecting the components, and identifying the top-level faults of interest. Our tool is compositional in that the end-user only needs to define safety aspects at the component level. The tool takes the model and automatically synthesizes both the qualitative and quantitative safety analyses. We go further by allowing users to describe system information such as components to use in an architecture and their connection compatibility and automatically synthesize an architecture that meets the top-level probability target adhering to end-user specified constraints. This capability allows users to rapidly explore a design space..

Siu, Kit Y.↗