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NASA NTRSDate not supplied

TPSAS-NF1676L-18251-DND

The National Wildlife Federation (NWF) and its Eco-Schools USA program is focused providing an engaging educational experience to help students better understand the essential principles of Earth's systems and the impact of climate change on them. The program is aimed to provide those students with the ability to communicate about climate change and apply knowledge in decision making. To achieve these objectives, the NWF partnered with NASA scientists and developed the Eco-Schools USA Climate Change Connections (CCC) is a 9th-12th grade curriculum. CCC is designed to build upon and utilize the many NASA mission resources, programs, and associated interfaces to enhance authentic learning experiences for both educators and students. It seeks to develop an integrated systems-thinking approach to understanding and acting upon the issue of climate change. In collaboration with mission specialists from ICESat, LandSat, Terra, AQUA, AURA, a cross programmatic curriculum was developed to provide a unified or systems-thinking approach to addressing realworld Earth systems problems (see NWF web page below). In this presentation, we provide an overview of the Climate Change Connections lesson curriculum but focus specifically on a couple of example exercises where NASA data sets are used to teach basic lessons about aspects of the climate system as it pertains to the built environment. In particular, we show how data adapted from NASA’s GEWEX Surface Energy Budget are applied to a lesson in solar energy. These data sets are made available directly through NASA web portals entitled “My NASA Data” and the Prediction of Worldwide Renewable Energy Resource. At the POWER web portal there is a link to the Surface meteorology and Solar Energy web portal providing data sets tailored specifically to the assessment of solar energy resource at any location on the globe. Here, we depict the sources of these data and their subsequent usage in the Eco-Schools USA CCC Lessons.

Paul W Stackhouse↗

NASA NTRS2025

Provider of Services for Urban Air Mobility (PSU) Prototype Simulation (X5) Final Report

Urban Air Mobility (UAM) is a new air transportation service concept to carry passengers or cargo in metropolitan areas, leveraged by innovative aircraft and air traffic automation technologies. NASA has conducted a series of simulations, called the X-series simulation, to evaluate the UAM concept of operations and support the development of airspace procedures and services for UAM operations. The simulation called “X5” was conducted in 2023 to test a Provider of Services for UAM (PSU) prototype developed by NASA for UAM flight planning, strategic conflict management support, and data exchange between UAM operators. In this simulation, two strategic conflict management capabilities, Demand-Capacity Balancing and Sequencing and Scheduling, were further investigated. This document describes the UAM system architecture modeled, the X5 simulation environment to be executed (e.g., traffic scenario and UAM airspace construct), and the strategic conflict management processes developed and evaluated in this study. Then, the simulation results are provided using several system performance metrics, such as the number of operations planned and activated, demand-capacity imbalances detected and resolved, and pre-departure delays. Based on these metrics, the test findings and lessons learned from this simulation are discussed. NASA developed a PSU prototype as part of a reference implementation of UAM system architecture and evolved strategic conflict management capabilities for UAM operations from the previous collaborative simulations with industry partners. Below is the summary of the achievements: - Aligned NASA’s UAM reference architecture with the FAA’s UAM ConOps notional architecture - Extended UAM airspace management capabilities to include 1) Demand-Capacity Balancing (DCB) to ensure operators coordinate planned usage of shared vertiports, and 2) Sequencing and Scheduling (S&S) at UAM corridor entry and exit points to help facilitate an orderly flow of traffic - Defined the PSU information exchange APIs and requirements towards informing industry standards - Developed and tested a NASA PSU prototype as reference implementation to validate the requirements and APIs - Developed a prototype service connecting NASA’s PSU and the FAA system for testing future PSU-ATM interface requirements - Tested NASA-developed assumptions for UAM operations such as airspace design, procedures, vehicle performance, and strategic conflict management methods to inform future Cooperative Operating Practices (COPs) development with industry - Evaluated system performance metrics such as number of simultaneous operations and ground delays that can help define system-level requirements. The simulation results showed that the UAM traffic demand could be managed to minimize the needs of tactical separation provision with ground delays assigned by DCB and S&S. These accomplishments and the lessons learned from the PSU Prototype X5 simulation activities will be valuable inputs for the Air Mobility Pathfinders (AMP) project, which is NASA’s new project to create and evaluate a reference architecture for safe, secure, and scalable UAM operations.

Simulation↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Use Case Review and Future Functionality and Content Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, review of potential use case(s), and outline plans for further development and content generation. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗

NASA NTRSDate not supplied

Thermal Design for the New Era of Lunar Instruments: Lessons Learned from NASA Goddard Space Flight Center’s Instrument Design Laboratory

As NASA enters the Age of Artemis, the resurgence of interest in the Moon has not only contributed to rapid development of crewed vehicles and habitats, it has also inspired a design renaissance for robotic and astronaut-deployed instrumentation for the lunar surface. NASA Goddard Space Flight Center’s Instrument Design Laboratory (IDL) has been at the forefront of lunar instrument concept design, and in recent years has used its collaborative and concurrent process to design instruments ranging from small devices held or worn by astronauts to large lander- or rover-mounted instruments with multiple scientific or technical functions. This paper explores the wide spectrum of lunar instruments studied by the IDL within the past few years and their thermal design nuances. A series of lessons learned will be used to categorize the general trends prevalent across lunar-specific design and the thermal and systems-level drivers for instrumentation on the surface of the Moon. While the instruments investigated in this work are not named due to their competition sensitivity and proprietary nature, it is hoped that the lessons learned captured here will help identify specific design drivers and areas of focus for the thermal and systems engineers designing the next generation of lunar instrumentation.

Lunar, instrument, artemis, design, IDL, Goddard↗

NASA NTRS2011

Habitat Demonstration Unit (HDU) Pressurized Excursion Module (PEM) Systems Integration Strategy

The Habitat Demonstration Unit (HDU) project team constructed an analog prototype lunar surface laboratory called the Pressurized Excursion Module (PEM). The prototype unit subsystems were integrated in a short amount of time, utilizing a rapid prototyping approach that brought together over 20 habitation-related technologies from a variety of NASA centers. This paper describes the system integration strategies and lessons learned, that allowed the PEM to be brought from paper design to working field prototype using a multi-center team. The system integration process was based on a rapid prototyping approach. Tailored design review and test and integration processes facilitated that approach. The use of collaboration tools including electronic tools as well as documentation enabled a geographically distributed team take a paper concept to an operational prototype in approximately one year. One of the major tools used in the integration strategy was a coordinated effort to accurately model all the subsystems using computer aided design (CAD), so conflicts were identified before physical components came together. A deliberate effort was made following the deployment of the HDU PEM for field operations to collect lessons learned to facilitate process improvement and inform the design of future flight or analog versions of habitat systems. Significant items within those lessons learned were limitations with the CAD integration approach and the impact of shell design on flexibility of placing systems within the HDU shell.

Gill, Tracy↗

NASA NTRS2001

Interactive Learning During Solar Maximum

The goal of this project is to develop and distribute e-educational material for space science during times of solar activity that emphasizes underlying basic science principles of solar disturbances and their effects on Earth. This includes materials such as simulations, animations, group projects and other on-line materials to be used by students either in high school or at the introductory college level. The on-line delivery tool originally intended to be used is known as Interactive Multimedia Education at a Distance (IMED), which is a web-based software system used at UCLA for interactive distance learning. IMED is a password controlled system that allows students to access text, images, bulletin boards, chat rooms, animation, simulations and individual student web sites to study science and to collaborate on group projects.

Ashour-Abdalla, Maha↗

NASA NTRS2005

Designing Agent Utilities for Coordinated, Scalable and Robust Multi-Agent Systems

Coordinating the behavior of a large number of agents to achieve a system level goal poses unique design challenges. In particular, problems of scaling (number of agents in the thousands to tens of thousands), observability (agents have limited sensing capabilities), and robustness (the agents are unreliable) make it impossible to simply apply methods developed for small multi-agent systems composed of reliable agents. To address these problems, we present an approach based on deriving agent goals that are aligned with the overall system goal, and can be computed using information readily available to the agents. Then, each agent uses a simple reinforcement learning algorithm to pursue its own goals. Because of the way in which those goals are derived, there is no need to use difficult to scale external mechanisms to force collaboration or coordination among the agents, or to ensure that agents actively attempt to appropriate the tasks of agents that suffered failures. To present these results in a concrete setting, we focus on the problem of finding the sub-set of a set of imperfect devices that results in the best aggregate device. This is a large distributed agent coordination problem where each agent (e.g., device) needs to determine whether to be part of the aggregate device. Our results show that the approach proposed in this work provides improvements of over an order of magnitude over both traditional search methods and traditional multi-agent methods. Furthermore, the results show that even in extreme cases of agent failures (i.e., half the agents failed midway through the simulation) the system's performance degrades gracefully and still outperforms a failure-free and centralized search algorithm. The results also show that the gains increase as the size of the system (e.g., number of agents) increases. This latter result is particularly encouraging and suggests that this method is ideally suited for domains where the number of agents is currently in the thousands and will reach tens or hundreds of thousands in the near future.

Tumer, Kagan↗

NASA NTRS2000

NASA's Design and Development of a Field Goniometer Instrument Using Solid Works

With NASA suffering severe funding cutbacks, engineers at NASA are required to produce state-of-the-art hardware with limited personnel and financial resources. In light of these constraints, the new NASA mandate is to build better, faster and cheaper. In April of 1998, Stennis Space Center's Commercial Remote Sensing Program contracted to the Systems Engineering Division at NASA Ames Research Center to develop a device known as a Field Goniometer. A Field Goniometer is a device that measures bi-directional reflectance of a target, such as vegetation, relative to the sun and an imaging system in an aircraft or spacecraft. The device is able to provide a spectral fingerprint of the surface it is measuring in wavelengths from 350nm-2500nm using a hyperspectral imager. To accomplish this project, several obstacles had to be overcome. First, the design had to be completed in less than four months. Second, due to the complexity of the design, the use of solid modeling was highly desirable but most of the group's solid modelers were assigned to other jobs. Third, the amount of funding available from the customer was one half to one third the funding typically expended for a job of this nature. Our choices for this project were to design with standard 2-D CAD systems currently used in-house or train additional engineers on our existing solids package or purchase a new solid model package. The use of a 2D CAD system was very undesirable due to the complexity of the design. Using our existing solids modeler would have required a learning curve for our engineers that would be incompatible with our schedule. Prior to this project, a member of our design group researched the solid modeling industry and decided to purchase SolidWorks. After examining the product for ease of use, modeling capability, training time required and cost, we decided our highest probability of success would be to design with Solidworks. During the design phase, our fabrication group was able to provide input at the very early stages, which added significant benefit to the final product. Fabrication cost and schedule savings have been realized by having complex part geometries translated directly from the SolidWorks design models to Surfcam and other computer-aided manufacturing (CAM) software. This direct model translation capability optimized the fabrication processes. The end result was that we were able to successfully complete the project on time and on budget. Other advantages of using SolidWorks, as cited by the design team, include a rapid negotiation of the initial learning curve, the ability to develop solid model hardware prototypes (used to communicate the design intent to both the customer and the fabricator), and the ability to work as a team collaborating on a large, complex model. These types of tools and efforts represent our response to NASA's challenge to produce higher quality products within shorter design and fabrication times.

Turner, Mark↗

NASA NTRS2019

Fatigue and Fatigue Countermeasures

Sleep can play an important role in alleviating health disparities, as inadequate sleep is associated with serious, adverse health conditions including cardiovascular disease, diabetes, obesity, cancer, and premature mortality. Nonetheless, dissemination of sleep health interventions targeted towards health disparities populations remains limited. Social workers and social work students serve populations designated as health disparities populations and are in an optimal position to promote sleep health among their underserved clients. However, social work students lack the specific training and resources necessary to promote healthy sleep practices among their clients. In fact, bachelors, master's, and doctoral social work programs in the United States do not incorporate any sleep health training as part of their curriculum. To improve public health through the promotion of healthy sleep practices among underserved populations, we propose to equip social work students, through an online learning module, with the knowledge and resources necessary to promote healthy sleep practices among their clients in underserved communities. In order to maximize effectiveness and reach, this proposal employs: (1) a collaborative team of sleep medicine and social work professionals; and (2) an ideal university environment with sophisticated eLearning capabilities that has one of the largest social work programs in the state of Florida.

fatigue countermeasure↗

NASA NTRS2020

Object and Gas Source Detection with Robotic Platforms in Perceptually-Degraded Environments

In exploration-oriented robotic missions for disaster relief in unknown subterranean environments, it is of prime importance for a human supervisor to rapidly gain situational awareness of salient objects within the environment. In this paper we present an automated object detection pipeline that is adaptable to heterogeneous robots with arbitrary sensor configurations. It has been deployed in time-critical scenarios with multiple collaborative robots in a variety of demanding underground environments. For visually observable objects, detections are made in both the visible and thermal spectra using a state-of-the-art machine learning framework for object detection and classification. Our pipeline can be rapidly adapted to a specific task by using a small, structured dataset to fine-tune a pre-trained convolutional neural network (CNN). Relative localization is separated from the CNN for speed of operation. A robust architecture for localization is used with outlier rejection and a hierarchy of fall-back distance measurement methods. Point-source objects such as gas and WiFi hotspots can also be detected, by tracking signal strength over time and presenting an intuitive visualization on a map. Observations of each object types are presented to the operator in ranked confidence order for final evaluation.

Agha-mohammadi, Ali-akbar↗

NASA NTRSDate not supplied

The NASA Jitter Handbook Development Activity: A Multidisciplinary Collaborative Effort

Spaceflight missions hosting high-performance vibration-sensitive optical sensor payloads with stringent pointing stability requirements are especially challenged in the areas of predicting, managing, controlling, and testing spacecraft line-of-sight (LoS) jitter. The community recognizes that this is a formidable multidisciplinary engineering task. However, while some textbooks and references in the aerospace literature may touch on the subject of jitter, important details of specific flight proven methodologies and techniques used by today’s jitter engineering practitioners are not readily accessible. There is no know engineering reference document with guidelines and best practices for managing and mitigating space-craft jitter. To fill this knowledge gap the NASA Engineering and Safety Center (NESC) has undertaken the task of developing a NASA Jitter Handbook which would include lessons learned and best practices for design, analysis, and test, along with operational guidelines to mitigate jitter. In this paper the plan for this handbook development will be described along with the envisioned handbook framework, key NASA stakeholders, and potential envisioned jitter engineering case studies. Opportunities exist technical collaboration on this handbook with NASA-external organizations.

Christopher D'Souza↗

NASA NTRSDate not supplied

Advancements in Blowing Dust Detection at Night via Machine Learning

This presentation introduces operational users to a machine-learning based Dust Probability product developed by the NASA SPoRT program for the application of detecting and monitoring blowing dust plumes at night. Advances in earth observing satellites has improved monitoring and detection of dust both day and night through derived imagery such as the Dust RGB. However, limitations of the RGB at night result in less contrast between dust and land surface features, as seen by the user. A Machine Learning (ML) model has been developed and applied to GOES-16 ABI to overcome this limitation and improve nighttime dust detection. The ML capability is a subset of Artificial Intelligence methods. In this case the Dust ML model was developed using a simple Random Forest (RF) model, typically used to solve classification challenges (or to provide regression type output). The goal was to leverage the strengths of the RF model to learn how to identify blowing dust, and hence, overcome the limitation of a user trying to detect blowing dust within the satellite imagery by eye alone. A brief description of the ML model development will be provided. However, the focus of the presentation will be on the initial user feedback from the assessment of this tool for the 2022 blowing dust events of March through April. During this time several users across the U.S. Southwest collaborated to apply this Dust ML product at night as a complement to the existing Dust RGB in order to determine if it provided greater operational efficiency and value.

Machine Learning↗

NASA NTRS2008

Health and Environment Linked for Information Exchange (HELIX)-Atlanta: A CDC-NASA Joint Environmental Public Health Tracking Collaborative Project

HELIX-Atlanta was developed to support current and future state and local EPHT programs to implement data linking demonstration projects which could be part of the CDC EPHT Network. HELIX-Atlanta is a pilot linking project in Atlanta for CDC to learn about the challenges the states will encounter. NASA/MSFC and the CDC are partners in linking environmental and health data to enhance public health surveillance. ~ The use of NASA technology creates value added geospatial products from existing environmental data sources to facilitate public health linkages. Proving the feasibility of the approach is the main objective

Al-Hamdan, Mohammad↗

NASA NTRSDate not supplied

Conducting Feasibility Studies in a Virtual World: Lessons Learned and Emerging Best Practices from the NASA DEVELOP Program

In response to new workplace realities, the NASA DEVELOP National Program pivoted from co-locating students, emerging professionals, and science advisors to bringing together virtual teams from across the United States. In its spring 2020 term, rapidly evolving circumstances required an ad-hoc roll-out of a virtual approach to complete the spring projects. Based on the experience from the spring term and a few weeks of planning, DEVELOP then conducted a fully virtual summer term with features such as 1) online collaboration tools, 2) virtual machines for analysis, and 3) streamed training offerings, including DEVELOP’s first ever program-wide Software Carpentry workshop. This full term of bringing together remote actors to select, build, and manage teams brought many challenges. Summer feedback has influenced planning for the fall 2020 term and process improvement is ongoing. This presentation will highlight lessons learned throughout this period of rapid change. Feedback from spring and summer terms and the Software Carpentry workshop will be summarized. Beyond participant impacts, there will also be discussion of effects on project results and partner experience. Final takeaways will focus on best practices that have been distilled for virtually-conducted feasibility studies.

NASA DEVELOP↗

NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗

NASA NTRS2020

Is Structured Agile an Oxymoron? Tales from Implementing and Executing Agile in a US Government Environment

To paraphrase a famous quote, "No plan survives contact with the reality." Software (SW) development is often a classic example of this: whatever the plan was for a particular development, it often does not survive contact with technical realities, budget realities, program realities and schedule realities. Traditionally, SW development has followed a waterfall methodology with requirements being rigorously specified before the design, which was completed before the coding and unit testing started, which were in turn finished before validation and verification started. This model of SW engineering derives much from the HW engineering of large systems, and has been the standard methodology used in US government software acquisitions and systems for decades, with highly variable results. US Government SW requirements are built around Waterfall concepts, which assume that the plan will survive contact with reality, or at least that modifications to the plan are relatively small, and relatively few.Because of the inefficiencies and difficulties inherent in Waterfall, the commercial SW world started using a different SW development methodology called Agile more than 20 years ago. Agile believes that a plan should evolve and learn rapidly in response to the realities encountered. At its core, there are a few key elements of Agile:- A small team of people which is highly flexible and adaptive. The team collaborates and interoperates through sophisticated development architectures and release environments- An iterative, incremental development and release approach which is based upon the concept that knowledge comes from experience within the team, and that the team makes decisions based upon what it knows- A team culture which prizes transparency, inspection and adaptation. These values are necessary so that the team experience and decision making is transparent and responsive to the realities encountered during development and testingSo, how to use Agile in a US Government environment? GMSEC (Goddard Mission Services Evolution Center) develops satellite ground system software for NASA and other US Government agencies. The SW developed by the team contains a large code base of many applications used within satellite mission operations centers. It spans the full gamut of SW development types: from SW which is in a classic maintenance and sustainment mode, to new developments with a fairly well understood scope and approach, to new developments whose scope and approach are quite unclear and which require significant research and prototyping. Team members move between all of these different types of SW development. Waterfall was inadequate to the programmatic and technical needs of the team, as well as the various types of SW development being done. The software plan was not surviving contact with the technical and programmatic realities experienced by the team. To address this, the team started a small pilot project in 2016 to test the use of Agile within a small subset of the team for a new web services application. In early 2018, the use of Agile was expanded to the whole team and all the software, but we had to fulfill the NASA SW development requirements. And we needed to do this while still remaining true to the key Agile elements of transparency, inspection and adaption. In order to do this, the team worked very closely with the Software Process Improvement (SPI) team at NASA Goddard, as well as NASA engineering manageme

Beech, Theresa W.↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

Jan Stupl↗