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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

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 NTRS2021

Demonstrations of System-Level Autonomy for Spacecraft

System-level autonomy refers to autonomously meeting the crosscutting needs of a system through awareness and coordinated control spanning the system's breadth of capabilities. In contrast to function-level autonomy, which focuses on capabilities required to achieve a specific function such as surface navigation or image recognition, system-level autonomy addresses the needs to coordinate and manage activities and resources, and estimate the state, across subsystems. This paper describes demonstrations that were conducted on a spacecraft workstation testbed. The autonomy was provided by system-level planning and execution integrated with system-level estimators of orbit knowledge and spacecraft hardware health. These components are embedded in a system-level framework defining how goals are formed and executed, which elements exist, and how control authority is distributed among components. The planning and execution system at the heart of the framework has the capability to schedule, execute and monitor completion of tasks, as well as plan around unexpected events including new science opportunities and anomalies. The planning and scheduling system is the Multi-mission EXECutive (MEXEC), supported by the system-level health state estimator Model-Based Off-Nominal State Identification and Detection (MONSID), and Autonomous Navigation (AutoNav) algorithms, which determine the orbital system state based on optical observation of other targets. These components are applicable to many kinds of missions on different platforms. These demonstrations were elaborations of earlier experiments conducted on the ASTERIA (Arcsecond Space Telescope Enabling Research In Astrophysics) CubeSat, described in a companion submission [1]. The spacecraft’s extended mission served as an in-flight test platform, during which some individual autonomous capabilities were flown successfully. The autonomy experiments described here were performed on the ASTERIA workstation testbed.

Prather, Maurice↗
NASA NTRS1998

Testing Planetary Rovers: Technologies, Perspectives, and Lessons Learned

Rovers are a vital component of NASA's strategy for manned and unmanned exploration of space. For the past five years, the Intelligent Mechanisms Group at the NASA Ames Research Center has conducted a vigorous program of field testing of rovers from both technology and science team productivity perspective. In this talk, I will give an overview of the the last two years of the test program, focusing on tests conducted in the Painted Desert of Arizona, the Atacama desert in Chile, and on IMG participation in the Mars Pathfinder mission. An overview of autonomy, manipulation, and user interface technologies developed in response to these missions will be presented, and lesson's learned in these missions and their impact on future flight missions will be presented. I will close with some perspectives on how the testing program has affected current rover systems.

Thomas, Hans↗
NASA NTRS2013

Development and Execution of Autonomous Procedures Onboard the International Space Station to Support the Next Phase of Human Space Exploration

Now that major assembly of the International Space Station (ISS) is complete, NASA's focus has turned to using this high fidelity in-space research testbed to not only advance fundamental science research, but also demonstrate and mature technologies and develop operational concepts that will enable future human exploration missions beyond low Earth orbit. The ISS as a Testbed for Analog Research (ISTAR) project was established to reduce risks for manned missions to exploration destinations by utilizing ISS as a high fidelity micro-g laboratory to demonstrate technologies, operations concepts, and techniques associated with crew autonomous operations. One of these focus areas is the development and execution of ISS Testbed for Analog Research (ISTAR) autonomous flight crew procedures intended to increase crew autonomy that will be required for long duration human exploration missions. Due to increasing communications delays and reduced logistics resupply, autonomous procedures are expected to help reduce crew reliance on the ground flight control team, increase crew performance, and enable the crew to become more subject-matter experts on both the exploration space vehicle systems and the scientific investigation operations that will be conducted on a long duration human space exploration mission. These tests make use of previous or ongoing projects tested in ground analogs such as Research and Technology Studies (RATS) and NASA Extreme Environment Mission Operations (NEEMO). Since the latter half of 2012, selected non-critical ISS systems crew procedures have been used to develop techniques for building ISTAR autonomous procedures, and ISS flight crews have successfully executed them without flight controller involvement. Although the main focus has been preparing for exploration, the ISS has been a beneficiary of this synergistic effort and is considering modifying additional standard ISS procedures that may increase crew efficiency, reduce operational costs, and raise the amount of crew time available for scientific research. The next phase of autonomous procedure development is expected to include payload science and human research investigations. Additionally, ISS International Partners have expressed interest in participating in this effort. The recently approved one-year crew expedition starting in 2015, consisting of one Russian and one U.S. Operating Segment (USOS) crewmember, will be used not only for long duration human research investigations but also for the testing of exploration operations concepts, including crew autonomy.

beyond low earth orbit (BLEO)↗
NASA NTRS2010

Onboard Processing of Multispectral and Hyperspectral Data of Volcanic Activity for Future Earth-Orbiting and Planetary Missions

Autonomous onboard processing of data allows rapid response to detections of dynamic, changing processes. Software that can detect volcanic eruptions from thermal emission has been used to retask the Earth Observing 1 spacecraft to obtain additional data of the eruption. Rapid transmission of these data to the ground, and the automatic processing of the data to generated images, estimates of eruption parameters and maps of thermal structure, has allowed these products to be delivered rapidly to volcanologists to aid them in assessing eruption risk and hazard. Such applications will enhance science return from future Earth-orbiting spacecraft and also from spacecraft exploring the Solar System, or beyond, which hope to image dynamic processes. Especially in the latter case, long communication times between the spacecraft and Earth exclude a rapid response to what may be a transient process - only using onboard autonomy can the spacecraft react quickly to such an event.

Jupiter↗
NASA NTRS2006

The NASA Space Communications Data Networking Architecture

The NASA Space Communications Architecture Working Group (SCAWG) has recently been developing an integrated agency-wide space communications architecture in order to provide the necessary communication and navigation capabilities to support NASA's new Exploration and Science Programs. A critical element of the space communications architecture is the end-to-end Data Networking Architecture, which must provide a wide range of services required for missions ranging from planetary rovers to human spaceflight, and from sub-orbital space to deep space. Requirements for a higher degree of user autonomy and interoperability between a variety of elements must be accommodated within an architecture that necessarily features minimum operational complexity. The architecture must also be scalable and evolvable to meet mission needs for the next 25 years. This paper will describe the recommended NASA Data Networking Architecture, present some of the rationale for the recommendations, and will illustrate an application of the architecture to example NASA missions.

Israel, David J.↗
NASA NTRS2018

Artificial Intelligence - The Future of Space Communications

Presentation focus on the Cognitive Communications Project Overview. The project aims to develop cognitive communications technologies to increase mission science return and improve resource efficiencies. Another key aspect of the project is understanding the fundamental aspects of cognitive technology and developing artificial intelligence to advance the future of space communications. This involves the use of machine learning algorithms in the next generation architecture for space communications in the effort to increase efficiency, autonomy and increased performance of the space communication and navigations next generation architecture.

Briones, Janette C.↗
NASA NTRS2023

An Overview of Distributed Spacecraft Autonomy at NASA Ames

Autonomous decision-making significantly increases mission effectiveness by mitigating the effects of communication constraints, like latency and bandwidth, and mission complexity on multi-spacecraft operations. To advance the state of the art in autonomous Distributed Space Systems (DSS), the Distributed Spacecraft Autonomy (DSA) team at NASA's Ames Research Center is developing within five relevant technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. DSA is maturing these technologies - critical for future large autonomous DSS - from concept to launch via simulation studies and orbital deployments. A 100-node heterogenous Processor-in-the-Loop (PiL) testbed aids distributed autonomy capability development and verification of multi-spacecraft missions. The DSA software payload deployed to the D-Orbit SCV-004 spacecraft demonstrates multi-agent reconfigurability and reliability as part of an ESA-sponsored in-orbit technology demonstration. Finally, DSA's primary flight mission showcases collaborative resource allocation for multipoint science data collection with four small spacecraft as a payload on NASA's Starling 1.0 satellites.

Caleb Ashmore Adams↗
NASA NTRS2005

Monitoring Floods with NASA's ST6 Autonomous Sciencecraft Experiment: Implications on Planetary Exploration

NASA's New Millennium Program (NMP) Autonomous Sciencecraft Experiment (ASE) [1-3] has been successfully demonstrated in Earth-orbit. NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The versatile ASE spacecraft command and control, image formation, and science processing software was uploaded to the Earth Observer 1 (EO-1) spacecraft in early 2004 and has been undergoing onboard testing since May 2004 for the near real-time detection of surface modification related to transient geological and hydrological processes such as volcanism [4], ice formation and retreat [5], and flooding [6]. Space autonomy technology developed as part of ASE creates the new capability to autonomously detect, assess, react to, and monitor dynamic events such as flooding. Part of the challenge has been the difficulty to observe flooding in real time at sufficient temporal resolutions; more importantly, it is the large spatial extent of most drainage networks coupled with the size of the data sets necessary to be downlinked from satellites that make it difficult to monitor flooding from space. Below is a description of the algorithms (referred to as ASE Flood water Classifiers) used in tandem with the Hyperion spectrometer instrument on EO-1 to identify flooding and some of the test results.

Ip, Felipe↗
DOE OSTI2024

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, in this work, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗
NASA NTRS2016

WindBots: A Concept for Persistent In-Situ Science Explorers for Gas Giants

This report summarizes the study of a mission concept to Jupiter with one or multiple Wind Robots able to operate in the Jovian atmosphere, above and below the clouds - down to 10 bar, for long durations and using energy obtained from local sources. This concept would be a step towards persistent exploration of gas giants by robots performing in-situ atmospheric science, powered by locally harvested energy. The Wind Robots, referred in this report as WindBots (WBs), would ride the planetary winds and transform aeolian energy into kinetic energy of flight, and electrical energy for on-board equipment. Small shape adjustments modify the aerodynamic characteristics of their surfaces, allowing for changes in direction and a high movement autonomy. Specifically, we sought solutions to increase survivability to strong/turbulent winds, and mobility and autonomy compared to passive balloons.

WindBots↗
NASA NTRS2022

Starling Animation

NASA’s Starling mission is advancing the readiness of various technologies for cooperative groups of spacecraft – also known as distributed missions, clusters, or swarms. Starling will demonstrate technologies to enable multipoint science data collection by several small spacecraft flying in swarms. The six-month mission will use four CubeSats in low-Earth orbit to test four technologies that let spacecraft operate in a synchronized manner without resources from the ground. The technologies will advance the following capabilities. • Network Communication • Cluster Flight Control • Relative navigation • Distributed Spacecraft Autonomy The Starling mission will test whether the technologies work as expected, what their limitations are, and what developments are still needed for CubeSat swarms to be successful. This animation illustrates Starling mission operations, offering an overview of each technology being demonstrated.

Howard N Cannon↗
NASA NTRS1998

Rovers for Mars Polar Exploration

Mobility is a generic capability needed for Mars exploration. Requirements for mobility range from those to get observations of individual rocks all the way to getting high resolution observations of regional areas. Table 1 shows the required-range of mobility to achieve various tasks. The Pathfinder mission and field experiments simulating rover missions [1, 2, 3, 4, 5] provide guidance as to rover capabilities that can reasonably be expected in the next decade. Rover mobility can be accomplished in a variety of ways, the most common being wheels or tracks and legged-walkers. Wheeled vehicles can traverse over rocks smaller than 1/2 wheel diameter, and with path planning to avoid larger rocks, can traverse terrains comparable to those seen on Mars in the Viking and Pathfinder landing sites. Slopes of 45 deg can be easily negotiated by wheeled rovers. Walking vehicles can negotiate even more complex terrain but requires computation capability to select each leg placement. Extremely complex terrain was traversed by the Nomad II walker which descended into (and most of the way out of) an active volcanic caldera (Mount Spur, AK) in 1995, although a slope failure eventually resulted in broken legs. The traverse range of a rover is limited by its science objectives, performance capabilities, and operational lifetime. The speed of rover traverse is a relative minor factor. With a different communication system, and no stops for science experiments, Sojourner could probably have traveled a kilometer. But, achieving land speed records is not a major objective of a science mission. Achieving science objectives requires targeting particular objects and studying them in detail, and the associated operational requirements will likely limit rover traverse range significantly. Traversing from target to target requires relatively few command cycles provided the traverse is over a short enough distance that it can be adequately planned. An operational goal of 100 m traverse per command cycle, arriving at a predetermined target, seems achievable. Investigating science targets requiring manipulator or instrument placement and sample collection will likely take several command cycles per target. Mission simulations [6] have demonstrated that traverse dI stances of 100-300 m, with detailed investigation of 5-10 targets take 50-100 command cycles, not unlike the Pathfinder experience in spite of the use of larger, faster, more capable rovers. Significant advances in rover autonomy will be needed to improve this Situation and it is not clear how much improvement will be brought to flight programs in the next decade. Dust accumulation on solar panels degrades power over time and, without dust removal, rover operational lifetimes may be limited to 90 sols.

Stoker, Carol↗