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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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Contingency management is essential to the robust operation of complex systems such as spacecraft and Unpiloted Aerial Vehicles (UAVs). Automatic contingency handling allows a faster response to unsafe scenarios with reduced human intervention on low-cost and extended missions. Results, applied to the Autonomous Rotorcraft Project and Mars Science Lab, pave the way to more resilient autonomous systems.
What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.
Draper Laboratorys Timeliner is a scripting and automation system that runs onboard computers in the International Space Station (ISS). Timeliner is fully integrated with ISS and can be used to automate ISS operations tasks. Some of the most challenging aspects of operating a payload in low earth orbit are communication delays, ground equipment failures, and human errors. How does a Payload Developer (PD) know their equipment is operating nominally and collecting science in the most efficient way possible or even powered at any given time? During a ground Loss of Signal (LOS) data outage, PDs have no insight into their experiments state, and benefit greatly from Timeliner scripts executing on ISS to perform telemetry monitoring and commanding operations. This paper will discuss current software designs, and new operational uses for Timeliner. Existing Timeliner capabilities discussed will include: autonomous EXPRESS Rack activation and deactivation; autonomous science data downlinks; Minus Eighty Degree Freezer (MELFI) Dewar autonomous safing; JAXA and ESA module autonomous payload facility safing; as well as many others. New operational concepts discussed will include allowing Timeliner on the payload computer to issue core commands (Thermal, Power, Fire Detection), creation of new ground tools that will monitor the current status of Payload Racks as well as all the messaging from autonomous scripts executing, decreasing approval time for Timeliner bundles, and opening up the Payload MDM Enhanced Processor Integrated Communications Card (EPIC) interface. The EPIC interface could provide a new crew interface for PL Timeliner execution. The new interface could operate on either a Payload Computer System (PCS) or a Station Support Computer (SSC) that is plugged into either the Payload LAN or the Operations LAN which will make communicating to the PL MDM more flexible and greatly increase band width for communication.
Space science and solar system exploration are driving NASA to develop an array of small body missions ranging in scope from near body flybys to complete sample return. This paper presents an algorithm for onboard motion estimation that will enable the precision guidance necessary for autonomous small body landing. Our techniques are based on automatic feature tracking between a pair of descent camera images followed by two frame motion estimation and scale recovery using laser altimetry data. The output of our algorithm is an estimate of rigid motion (attitude and position) and motion covariance between frames. This motion estimate can be passed directly to the spacecraft guidance and control system to enable rapid execution of safe and precise trajectories.
To reduce the cost of future spaceflight missions and to perform new science, NASA has been investigating autonomous ground and space flight systems. These goals of cost reduction have been further complicated by nanosatellites for future science data-gathering which will have large communications delays and at times be out of contact with ground control for extended periods of time. This paper describes two prototype agent-based systems, the Lights-out Ground Operations System (LOGOS) and the Agent Concept Testbed (ACT), and their autonomic properties that were developed at NASA Goddard Space Flight Center (GSFC) to demonstrate autonomous operations of future space flight missions. The paper discusses the architecture of the two agent-based systems, operational scenarios of both, and the two systems autonomic properties.
The science return from future robotic exploration of the Martian surface can be enhanced by performing routine processing using on board computers.
Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.
Within the NASA Intelligent Systems Program, the Intelligent Data Understanding (IDU) element develops techniques for transforming data into scientific understanding. Automating such tools is critical for space science, space-based earth science, and planetary exploration with onboard scientific data analysis. Intelligent data understanding (IDU) is about extracting meaning from large, diverse science and engineering databases, via autonomous techniques that transform very large datasets into understanding. The earth science community in particular needs new tools for analyzing multi-formatted and geographically distributed datasets and for identifying cause-effect relationships in the complex data. Research within the IDU program element seeks to automate data analysis tasks so that humans can focus on creative hypothesis generation and knowledge synthesis. It may also enable NASA space missions in which autonomous agents must generate knowledge and take actions, and missions where limited bandwidth permits transmission of only the most interesting scientific observations, summaries, and conclusions. Twenty-seven research projects are-currently funded.
Space science and solar exploration are driving NASA to develop an array of small body missions ranging in scope from near body flybys to complete sample return.
The Planetary Pointing and Tracking System (PPTS) is being developed to provide precision pointing for science platforms on future autonomous planetary spacecraft. The PPTS design approach using a CCD optical sensor for closed-loop control with respect to the target body, a gyro for inertial stabilization, and brushless dc torque motors for smooth and continuous platform articulation is essential for high resolution planetary imaging and automated science execution. An integral part of PPTS is the correlation tracker which has the potential to revolutionize autonomous guidance.
Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.
With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.
In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically pursuing these science-collection opportunities during long-range rover traverses.
Autonomous operation of UAS holds promise for greater productivity of atmospheric science missions. However, several challenges need to be overcome before such missions can be made autonomous. This paper presents a framework for safe autonomous operations of multiple vehicles, particularly suited for atmospheric science missions. The framework revolves around the use of piecewise Bezier curves for trajectory representation, which in conjunction with path-following and time-coordination algorithms, allows for safe coordinated operations of multiple vehicles.
No abstract provided
After four years on the International Space Station (ISS), the Astrobee Research Facility, has completed over 130 Test Sessions logging over 1000 hours of operations. Managed by the NASA ISS Program OZ office and supported by NASA Ames Research Center (ARC) in California, the Astrobee Team maintains three identical free-flying Astrobee robots for research on the ISS. As a technology demonstration platform, the Astrobee Robots are available for Guest Scientists to use for a spectrum of research capabilities. Astrobee, propelled by battery-operated fans, is designed to autonomously operate throughout most of the USOS (US Orbital Segment), with the objective of minimizing astronaut support. Astrobee carries a suite of six cameras, a two degree-of-freedom (DOF) arm with a gripper that can grasp ISS handrails and other objects, and three payload bays that provide power and data for guest science hardware. Astrobee can autonomously execute hours-long flight plans or be teleoperated from the ground or by astronauts. While the Astrobee Team continues to improve mapping and autonomous flight capabilities, one of the main goals of Astrobee Robots is to provide research opportunities for Guest Scientists. The Astrobee Robot Software (ARS) makes extensive use of the open-source Robot Operating System (ROS). The ARS can be used interchangeably with an Astrobee Simulator or as Astrobee’s onboard software. ARS features include autonomous docking and perching, real-time teleoperations from the ground, plan based autonomous tasks, multi Astrobee communication, among other capabilities. Through simulation software and ground testing laboratories, the Astrobee Team is available to support Guest Scientists during development and testing and lead real-time ISS operations. Guest Scientists can participate in this research opportunity following the Guest Science Lifecycle (GSL) shown in Figure 1: Guest Science Lifecycle below. The Astrobee Team and Guest Scientists have complete research including Gecko materials studies, RFID and sound sensing capabilities, student Robotics Programming Challenges, and Free Flyer formation flight investigations. Current science with the Astrobee Robots includes Free Flyer self-toss studies, new docking capabilities, advanced mapping resolution capabilities, and high resolution panoramic imagery. Future Guest scientists and Astrobee Team research will focus on robotics applications for future NASA missions such as Gateway and Artemis and potential experiments involving human-robot interactions. This presentation will focus on four main subjects, 1) completed, current, and future planned research using the Astrobee robots, 2) how Guest Scientist get from conception to the ISS, 3) Astrobee Facility resources available for Guest Science ground testing and real-time ISS operations support, and 4) lessons learned from four years of ISS operations.
After five years on the International Space Station (ISS), the Astrobee Research Facility, has completed over 160 Test Sessions logging over 1200 hours of operations. Managed by the NASA ISS Program OZ office and supported by NASA Ames Research Center (ARC) in California, the Astrobee Team currently maintains two identical free-flying Astrobee robots and a Docking Station for research on the ISS. As a technology demonstration platform, the Astrobee Robots are available for Guest Scientists to use for a spectrum of research capabilities. Using ambient air on the ISS, propelled by battery-operated fans, Astrobee is designed to autonomously operate throughout most of the USOS (US Orbital Segment), with the objective of minimizing the need for astronaut support. Astrobee carries a suite of six cameras, a two degree-of-freedom (DOF) arm with a gripper that can grasp ISS handrails and other objects, and three payload bays that provide power and data for guest science hardware. Astrobee can autonomously execute hours-long flight plans or be tele-operated from the ground. While the Astrobee Team continues to improve mapping and autonomous flight capabilities, one of the main goals of Astrobee Robots is to provide research opportunities for Guest Scientists. The Astrobee Robot Software (ARS) makes extensive use of the open-source Robot Operating System (ROS). The ARS can be used interchangeably with an Astrobee Simulator or as Astrobee’s onboard software. ARS features include autonomous docking and perching, real-time teleoperations from the ground, plan based autonomous tasks, multi Astrobee communication, among other capabilities. Through simulation software and ground testing laboratories, the Astrobee Team is available to support Guest Scientists during development and testing and lead real-time ISS operations. The Astrobee Team and Guest Scientists have completed research including Astrobatics maneuvers, RFID and sound sensing capabilities, Gecko materials studies, student Robotics Programming Challenges, and Free Flyer formation flight investigations. Current science with the Astrobee Robots is investigating new docking capabilities through software only research as well as testing new docking hardware installed on the Astrobees. The Astrobee Team and other researchers at NASA Ames continue to explore robotics applications for future NASA missions such as Gateway and potential experiments involving human-robot interactions. Continued advanced mapping resolution capabilities, and high-resolution panoramic imagery also remains areas of research. Exciting in development research involves docking for rendezvous proximity operation (CLINGERS), multi resolution 3D scanning (MRS), space debris removal in microgravity (REACCH). This presentation will mainly focus on completed research over the past year and current science being performed on the Astrobees. This presentation will also focus on how a Guest Scientist/Researcher progresses from conception to running their science on the Astrobees on the ISS, as well as discuss the Astrobee Facility resources available for supporting ground testing and real-time ISS operations.
Future space missions will continue to be scientifically and technically more ambitious, and will demand more autonomy to accomplish complex tasks in uncertain environments and in close proximity to extraterrestrial bodies.