SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
NASA’s Distributed Spacecraft Autonomy (DSA) Project developed and demonstrated software to enhance multi-spacecraft mission adaptability, efficiently allocate tasks between spacecraft using ad-hoc networking, and enable human-swarm commanding of Distributed Space Missions.
Distributed space systems are a mission architecture consisting of multiple spacecraft as a cohesive system which provide multipoint sampling, increased mission coverage, or improved sample resolution, while reducing mission risk through redundancy. To fully realize the potential of these systems, eventually scaling to hundreds or thousands of spacecraft, distributed space systems need to be operated as a single entity, which will enable a variety of novel scientific space missions. The Distributed Spacecraft Autonomy (DSA) project is a software project which aims to mature the technology needed for those systems, namely autonomous decision-making and swarm networking. The DSA project leverages a containerized swarm test framework to simulate spacecraft software, which can identify emergent behavior early in development. Container virtualization allows distributed spacecraft systems to be simulated entirely in software on a single computer, avoiding the overhead associated with conventional approaches like hardware facsimiles and virtual machines. For this approach to be effective, the simulated system behavior must not be artificially influenced by the swarm test framework itself. To address this, we present a series of benchmarks to quantify virtual network bandwidth available on a single-host computer and contextualize this against the network and application behavior of the DSA swarm test framework.
Overview of autonomy definition and needs for space missions, including examples of the Distributed Spacecraft Autonomy and Integrated System for Autonomous and Adaptive Caretaking projects.
Autonomous decision-making is crucial for enhancing mission effectiveness in Distributed Space Systems (DSS), particularly in multi-spacecraft operations where communication constraints and mission complexity pose challenges. The Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is advancing autonomy in DSS through five key technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. The DSA experiment onboard the Starling 1.0 Mission showcases collaborative resource allocation for multi-point science data collection with four small spacecraft. Autonomy in decision-making is highlighted as a crucial factor for multi-spacecraft missions, enabling spacecraft to operate independently, reducing reliance on ground control. This capability is particularly significant for future deep-space missions, where communication delays and limited data transmission capacity make traditional command and control approaches impractical. This demonstration focuses on a GPS Channel Selection Experiment, leveraging emergent capabilities like "shared sampling" and "simultaneous sampling" to optimize channel selection across the spacecraft swarm. The experiment aims to capture ionospheric phenomena such as the Equatorial Ionization Anomaly and Polar Patches. The DSA system's autonomous reconfiguration ability is showcased, emphasizing its adaptability to natural phenomena without significant integration efforts. The GPS Channel Selection Experiment utilizes a dual-band GPS receiver to estimate plasma density in the ionosphere. Explorative and exploitative channel selections are employed based on the nature of observed phenomena. The performance of DSA algorithms is evaluated in terms of optimal channel allocations and responsiveness to changes in observed features. The DSA Flight Software utilizes the Core Flight System (cFS) framework, ensuring compatibility with the Starling 1.0 flight mission software. DSA showcases results from RTI’s Connext DDS Micro communication middleware, enabling message routing over the Ad-Hoc Network of Starling 1.0. This paper provides a comprehensive overview of the DSA experiment's initial results, emphasizing the advancements in autonomy for Distributed Space Systems and the successful collaboration with the Starling 1.0 mission.
Explore the source record for details and available documents.
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.
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.
Distributed Spacecraft Systems are a type of multi-spacecraft mission architecture that can not only provide improved resolution, coverage, and availability of existing missions, but also enable missions that would be previously infeasible using traditional approaches. Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems. In previous science swarm missions, the spacecraft involved have not been able to communicate with each other without utilizing a ground station. Now that the spacecraft can perform inter-satellite communication, the spacecraft can be treated as a collective. Swarm autonomy is critical for a growing number of satellites which means novel ways of displaying swarm data needs to be implemented. Such systems introduce unique challenges to traditional approaches for command and control of these spacecraft, due to the large number of spacecraft and the complexity of the interactions between them. The ground data system for DSA addresses these challenges through the creation of a custom user interface that allows a single operator to orchestrate a multi-spacecraft swarm in a scalable way. This plenary describes the details of the autonomy demonstration being performed, the requirements of those using the interface to analyze the spacecraft telemetry to assess demonstration success, and the approach taken by the ground systems team to create an interface that satisfies these requirements. This approach involves the creation of several distinct components that correspond to the level of detail presented to the user. These components are based on conventional user roles in human-robot interaction, including supervisor, operator, and mechanic, extended to accommodate the additional overhead of coordinating actions between agents. One main feature of the interface is the listenability matrix component which will represent inter-satellite communications in a heat mapped matrix. The above work described will enable users to command and interact with the spacecraft as a collective.
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.
Explore the source record for details and available documents.
The MIT's Space Systems Laboratory developed the Synchronized Position Hold Engage and Reorient Experimental Satellites (SPHERES) as a risk-tolerant spaceborne facility to develop and mature control, estimation, and autonomy algorithms for distributed satellite systems for applications such as satellite formation flight. Tests performed study interferometric mission-type formation flight maneuvers in deep space. These tests consist of having the satellites trace a coordinated trajectory under tight control that would allow simulated apertures to constructively interfere observed light and measure the resulting increase in angular resolution. This paper focuses on formation initialization (establishment of a formation using limited field of view relative sensors), formation coordination (synchronization of the different satellite s motion) and fuel-balancing among the different satellites.
The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.
Space missions of the 21st Century will be characterized by constellations of distributed spacecraft, miniaturized sensors and satellites, increased levels of automation, intelligent onboard processing, and mission autonomy. Programmatically, these missions will be noted for dramatically decreased budgets and mission development lifecycles. Current progress towards flexible, scaleable, low-cost, reusable mission control systems must accelerate given the current mission deployment schedule, and new technology will need to be infused to achieve desired levels of autonomy and processing capability. This paper will discuss current and future missions being managed at NASA's Goddard Space Flight Center in Greenbelt, MD. It will describe the current state of mission control systems and the problems they need to overcome to support the missions of the 21st Century.
Modern advancements in spacecraft technology have enabled engineers to develop radically smaller and lighter spacecraft, which has drastically reduced the cost of putting spacecraft into space. Despite these advancements and the shrinking cost to get spacecraft into space, space exploration is still prohibitively expensive. So much so that many space missions prefer to err on the side of caution than take on additional risk by trying newer, unproven technologies. This risk-averse mission design, while very reasonable from a program management point of view, significantly impacts engineers’ ability to solve newer, more complicated problems and limits scientists’ ability to develop more complex experiments that rely on newer technology. Often these new technologies remain stuck at lower technology readiness levels for many years due to the space community's reluctance to take on the additional risks of proving out unproven technology. The Distributed Spacecraft Autonomy (DSA) team at NASA Ames Research Center is developing a containerized solution to enable the rapid development of newer space technologies and accelerate their adoption into space missions. The Opportunistic Software Experiments for Spacecraft Autonomy Testbeds (OSE-SAT) is an on-orbit test bed that aims to reduce the amount of risk associated with newer, unproven space technologies by containerizing each experiment in its own isolated environment and providing a safe, robust, and controlled interface to access spacecraft host resources that is monitored in real time by thoroughly tested Trusted Container developed by DSA. This paper will describe DSA’s implementation of OSE-SAT and discuss the benefits, as well as challenges, of on-orbit containerization.
The Mission Simulation Toolkit (MST) is a flexible software system for autonomy research. It was developed as part of the Mission Simulation Facility (MSF) project that was started in 2001 to facilitate the development of autonomous planetary robotic missions. Autonomy is a key enabling factor for robotic exploration. There has been a large gap between autonomy software (at the research level), and software that is ready for insertion into near-term space missions. The MST bridges this gap by providing a simulation framework and a suite of tools for supporting research and maturation of autonomy. MST uses a distributed framework based on the High Level Architecture (HLA) standard. A key feature of the MST framework is the ability to plug in new models to replace existing ones with the same services. This enables significant simulation flexibility, particularly the mixing and control of fidelity level. In addition, the MST provides automatic code generation from robot interfaces defined with the Unified Modeling Language (UML), methods for maintaining synchronization across distributed simulation systems, XML-based robot description, and an environment server. Finally, the MSF supports a number of third-party products including dynamic models and terrain databases. Although the communication objects and some of the simulation components that are provided with this toolkit are specifically designed for terrestrial surface rovers, the MST can be applied to any other domain, such as aerial, aquatic, or space.
Distributed Space Systems (DSS) are an emerging class of mission designs that enable new scientific and commercial opportunities. In order to enable those new opportunities, these systems will need to have significantly expanded autonomous capabilities compared to their single-spacecraft predecessors. In this paper, we present Distributed Spacecraft Autonomy (DSA) project, a payload on NASA's Starling spacecraft experiment. We first describe a step-by-step process for characterizing what features are needed in an autonomous DSS, and show how this process applied to DSA. We then describe the Starling mission, a four-spacecraft swarm hosting multiple DSS payloads. We then describe DSA, which will mature in-space networking and autonomous planning technologies to measure topside ionosophere features using data from the Starling spacecraft's GPS receivers. We describe how DSA will coordinate observations of GPS satellites using Starling's underlying communications infrastructure combined with novel DSS technology. The flight validation of DSS technology will provide mature technology to enable future DSS missions.
NASA is designing future complex satellite missions ranging from single satellites and constellations to space networks and sensor webs. These missions require more interoperability, autonomy, and coordination than previous missions; in addition, a desire exists to have scientists retrieve data directly from the satellite rather than a central distribution source. To meet these goals, NASA has been studying the possibility of extending the Transmission Control Protocol/Internet Protocol (TCP/IP) suite for spacebased applications.
As the envisioned next-generation SCaN Network transitions into an end-to-end “system of systems” with new enabling capabilities, it is anticipated that the introduction of machine learning, artificial intelligence, and other cognitive strategies into the network infrastructure will result in increased mission science return, improved resource efficiencies, and increased autonomy and reliability. This enhanced set of cognitive capabilities will be implemented via a “space cloud” concept to achieve a service-oriented architecture with distributed cognition, de-centralized routing, and shared, on-orbit data processing. The enabling cognitive communications and networking capabilities that may facilitate the desired network enhancements are identified in this document, and the associated enablers of these capabilities, such as technologies and standards, are described in detail.