Collaborating with NASA
Opportunities and potential to collaborate with NASA subject matter experts across all mission directorates
Stephanie Vivod↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Promoting Collaborative Open Science through the Stakeholder Engagement Program
Since 2016, the Satellite Needs Working Group (SNWG), an initiative of the U.S. Group on Earth Observations (USGEO), has surveyed federal agencies biennially to identify their satellite Earth observation needs. Coordinating with the agencies, NASA-led assessment teams work to identify solutions for each expressed need. Solutions to a need can involve existing data products or even the formation of new data products and technologies, such as the Harmonized Landsat Sentinel-2 (HLS) product and the Catalog of Archived Sub-Orbital Earth Science Investigations (CASEI). To enhance community engagement with these new data products and technologies, the SNWG Management Office’s Stakeholder Engagement Program (SEP) was established. The SEP within NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) at Marshall Space Flight Center has three goals: to increase awareness of the SNWG survey and its outcomes, to ensure training coordination and outreach efforts supporting integration and use of SNWG solutions, and to enhance coordination among solution development teams, NASA’s Distributed Active Archive Centers (DAACs), SNWG stakeholders, and end user communities. These goals align with NASA Earth Science Data System’s (ESDS) Open Science initiative by establishing a collaborative community focused on enhancing scientific research at diverse levels of understanding. This presentation will highlight SEP’s facilitation of these open science goals through its support in SNWG’s past and current survey cycles as well as its plans for future involvement.
Jenny Wood↗
A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks
The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from distributed sensing and smart spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs to demonstrate operation. The initial framework design will focus on supporting precision navigation and independent surveillance supporting conformance monitoring of aircraft in airspace corridors and vertiport airspaces. Preliminary results from this framework shows promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.
Distributed sensing↗
BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite
As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.
Artificial Intelligence↗
BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite
As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.
Artificial Intelligence↗
Inferring Collaboration Strategies and Usability from Remote Observations in a Spaceflight Analog Environment
Collecting usability and user evaluations of novel software in spaceflight analog missions is challenging. For one such analog mission, we collected audio recordings of crew members as they completed their assigned self-scheduling task using novel software. Conversation analysis provided insight about collaborative nature of task and potential new evaluation measures.
human computer interaction↗
COCPIT: Collaborative Activity Planning Software for Mars Perseverance Rover
Since landing on the Martian surface, the Perseverance rover has relied on a distributed team to generate commands for exploring its new environment each sol (Martian day). The team uses a complex suite of software tools to accomplish this challenging task in time for the next window of opportunity to send commands to the rover. A key piece of this software ecosystem is COCPIT (Component-based Campaign Planning, Implementation, and Tactical). COCPIT is part of the next generation of planning and scheduling software tools developed by NASA's Jet Propulsion Laboratory in partnership with NASA's Ames Research Center. COCPIT is a web-based application that allows users to collaboratively view and update the Perseverance rover's activity plans, continuously verify that the plan satisfies constraints, assign targets for directing scientific instruments, document science intent, and model power and data resources. Mars Surface Operations requires diverse expertise from team members within the Engineering, Science, Robotic, and Instrument Operations groups, distributed across North America and Europe. In order to improve efficiency and reduce risk, all teams are able to review and edit their activities simultaneously and see the effects on the plan in its entirety. As part of the Ground Data System (GDS) tool suite, COCPIT is responsible for the activity plan. It provides specialized views that allow operators to understand where there may be room for additional observations, see whether any planning constraints are being violated, and confirm that energy usage and data generation are within the defined limits. It contains details such as which filters a camera will use for a given observation, what the resolution of the images should be, where to store the data onboard, and how long the observation is expected to take. It predicts when specific data will be downlinked from the rover to a passing orbiter, so that the team knows when to expect that data on Earth for evaluation in future planning. Ultimately the information from the COCPIT plan is translated to sequences that will be bundled and radiated to Perseverance for execution. The COCPIT tool is used throughout all planning phases.
Kanefsky, Bob↗
NASA Glenn Research Center and Opportunities for Collaboration with UAF
Overview of GRC and potential opportunities for collaborations with University of Alaska Fairbanks
Tibor Kremic↗
NASA's Collaborative Digital Departure Rerouting (CDDR) Technology Reduces Flight Delays and Emissions
NASA’s Digital Information Platform is creating a digital information ecosystem to exchange services and provide access to airspace information to enable fuel efficient operations. This system allows providers to make their services more accessible and consumers to access information and services they need to optimize their operations. In this video we will focus on the Collaborative Digital Departure Rerouting service, which provides airlines with routing options similar to how drivers navigate using cellphone apps.
Keenan Roach↗
BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite
As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.
Artificial Intelligence↗
Advanced Information Systems Technology (AIST) 2023 Annual Reviews Analytic Collaborative Frameworks (ACF)
On July 12, 2023, the Advanced Information Systems Technology (AIST) program conducted grouped annual reviews of all its Analytic Collaborative Frameworkds (ACF) projects. This report regroups all these technical presentations.
Jacqueline Le Moigne↗
Collaborative Assistive Tool To Enable Novel Solutions (CATTENS ) DE Expansion
CATTENS is a model-based, multi disciplinary concurrent engineering tool whose purpose is to enable engineers and scientists to collaborate in defining and developing a mission or system concept. The initial deployment target for CATTENS is the GSFC Mission Design Laboratory (MDL), but CATTENS is available to all GSFC engineers and scientists.
Stephen Waterbury↗
Integrated (Physical and Digital) Collaborative Experimentation: Advancing Dialog and Leveraging by Aerospace Researchers and Developers
This paper 1) documents findings and observations from the American Institute of Aeronautics and Astronautics (AIAA) Ground Test Technical Committee (GTTC) Future of Ground Test Working Group and the Applied Aeronautics Technical Committee (APATC) Collaborative Experiments & Computation Discussion Group, and 2) develops a more focused approach for sharing and advancing integrated development and use of physical experimental and computational capabilities for aerospace research and development. The GTTC and APATC are engaging with the larger AIAA technical community by creating a Focus Group on this topical area that will support working together on common interests in the public domain. This paper summarizes the knowledge capture from the last ten+ years and proposes a structure and scope going forward for the new, combined Focus Group.
ground testing↗
Quality of Candidate Flights and Submission Prediction in Collaborative Digital Departure Reroute
Collaborative Digital Departure Reroute (CDDR) enables the reroute of flights using a flight operator proposed set of alternative route options, referred to as Trajectory Option Set (TOS), in order to reduce delay on the airport's surface and in the Metroplex environment. The reroute functionality is enabled through NASA's Digital Information Platform (DIP). TOS candidate flights are defined as flights with an alternative route with delay savings greater than the flight operator defined relative trajectory cost. This paper analyzes the TOS candidate flights at Dallas/Fort Worth International Airport (KDFW) in the North Texas Metroplex to gain insight into which candidate flights are higher quality through a scoring method. This insight will inform refinements to help CDDR focus on high quality reroute opportunities. Binary classification models for predicting the flight operator's submission of candidate flights are also explored in this paper.
Machine Learning↗
Quality of Candidate Flights and Submission Prediction in Collaborative Digital Departure Reroute
Collaborative Digital Departure Reroute (CDDR) enables the reroute of flights using a flight operator proposed set of alternative route options, referred to as Trajectory Option Set (TOS), in order to reduce delay on the airport's surface and in the Metroplex environment. The reroute functionality is enabled through NASA's Digital Information Platform (DIP). TOS candidate flights are defined as flights with an alternative route with delay savings greater than the flight operator defined relative trajectory cost. This paper analyzes the TOS candidate flights at Dallas/Fort Worth International Airport (KDFW) in the North Texas Metroplex to gain insight into which candidate flights are higher quality through a scoring method. This insight will inform refinements to help CDDR focus on high quality reroute opportunities. Binary classification models for predicting the flight operator's submission of candidate flights are also explored in this paper.
Machine Learning↗
Development and Implementation of a Small Satellite Systems Engineering Webinar Series: A Collaboration Between United Nations Office for Outer Space Affairs and National Aeronautics and Space Administration
The United Nations Office for Outer Space Affairs (UNOOSA) in collaboration with the National Aeronautics and Space Administration (NASA) established a webinar series on NASA systems engineering standards and practices for the purpose of sharing knowledge in this area through UNOOSA’s “Access to Space for All” which provides free and accessible educational content. Through NASA’s Small Spacecraft Systems Virtual Institute (S3VI), the four-part series was designed to cover basic systems engineering and project management skills that are fundamental for planning, developing, and implementing an experiment or a space project, and that serve as critical knowledge for those engaged in space activities, whether as a designer, builder, or manager of space infrastructure and services. This first series of webinars was convened over the course of November 2023 through February 2024. The webinar series was segmented into four one and a half hour webinars which covered topics related to the Fundamentals of Systems Engineering, Requirements and Systems Engineering, System Assembly, Integration and Test, and Spacecraft Handling, as well as an Introduction to the Small Spacecraft Systems Virtual Institute (S3VI). The agendas for the first three webinars focused on addressing a number of questions relevant to a particular topic. Examples of these questions include: What is systems engineering and what does a systems engineer do? Why does NASA use systems engineering? How do you define requirements for a small spacecraft mission? Why are requirements important to space missions? How are trade studies used to determine parts selection and why are they important? The final webinar of the first series provided interactive discussions and demonstrations of the S3VI tools; shared information on additional webinar opportunities offered by the S3VI; and reviewed databases curated by the S3VI that include the Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool, Mission Design Tools, Small Spacecraft Information Search, and the State-of-the-Art (SoA) Small Spacecraft Report. Future webinar series will be conducted with topics are to be determined. The presenter will discuss details related to the first webinar series and future plans for additional series.
systems engineering↗
MAV Software Development: Streamlined Collaboration for MSR
This poster presents the coordinated efforts of the Mars Ascent Vehicle (MAV) software development teams, encompassing Flight Software, Ground Software, and Hardware-in-the-Loop Labs. This collaborative approach, which stems from the Artemis program, ensures efficiency in preparing for the Mars Sample Return (MSR) mission, focusing on the critical role of software integration for mission success.
MAV↗
Development and Implementation of A Small Satellite Systems Engineering Webinar Series: A Collaboration Between the United Nations Office for Outer Space Affairs and the National Aeronautics and Space Administration
The United Nations Office for Outer Space Affairs (UNOOSA) in collaboration with the National Aeronautics and Space Administration (NASA) established a webinar series on NASA systems engineering standards and practices for the purpose of sharing knowledge in this area. UNOOSA’s “Access to Space for All” initiative provides capacity-building opportunities in space science, technology, and space applications for United Nations member states. Due to the cooperation among established space actors, the United Nations, and emerging space entities, the initiative enables students from developing countries from all over the world to carry out projects using technologies and space applications. Through NASA’s Small Spacecraft Systems Virtual Institute (S3VI), the four-part webinar series was designed to cover basic systems engineering and project management skills that are fundamental to planning, developing, and implementing an experiment or a space project and that serve as critical knowledge for those engaged in space activities, whether as a designer, builder, or manager of space infrastructure and services. The first series of webinars was convened over the course of November 2023 through February 2024.
Systems Engineering↗