A science-based executive for autonomous planetary vehicles
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autonomy planetary exploration vehicles↗
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Development Approach for the Accommodation of Materials Science Research for the Materials Science Research Facility on the International Space Station
The Materials Science Research Facility (MSRF) is a modular facility comprised of autonomous Materials Science Research Racks (MSRR's) for research in the microgravity environment afforded by the International Space Station (ISS). The initial MSRF concept consists of three Materials Science Research Racks (MSRR-1, MSRR-2, and MSRR-3) which will be developed for a phased deployment beginning on the third Utilization Flight (UF-3). The facility will house materials processing apparatus and common subsystems required for operating each device. Each MSRR is a stand alone autonomous rack and will be comprised of either on-orbit replaceable Experiment Modules, Module Inserts, investigation unique apparatus, and/or multiuser generic processing apparatus. Each MSRR will support a wide range of materials science themes in the NASA research program and will use the ISS Active Rack Isolation System (ARIS). MSRF is being developed for the United States Laboratory Module and will provide the apparatus for satisfying near-term and long-range Materials Science Discipline goals and objectives.
Schaefer, D. A.↗
Lessons learned from the introduction of autonomous monitoring to the EUVE science operations center
The University of California at Berkeley's (UCB) Center for Extreme Ultraviolet Astrophysics (CEA), in conjunction with NASA's Ames Research Center (ARC), has implemented an autonomous monitoring system in the Extreme Ultraviolet Explorer (EUVE) science operations center (ESOC). The implementation was driven by a need to reduce operations costs and has allowed the ESOC to move from continuous, three-shift, human-tended monitoring of the science payload to a one-shift operation in which the off shifts are monitored by an autonomous anomaly detection system. This system includes Eworks, an artificial intelligence (AI) payload telemetry monitoring package based on RTworks, and Epage, an automatic paging system to notify ESOC personnel of detected anomalies. In this age of shrinking NASA budgets, the lessons learned on the EUVE project are useful to other NASA missions looking for ways to reduce their operations budgets. The process of knowledge capture, from the payload controllers for implementation in an expert system, is directly applicable to any mission considering a transition to autonomous monitoring in their control center. The collaboration with ARC demonstrates how a project with limited programming resources can expand the breadth of its goals without incurring the high cost of hiring additional, dedicated programmers. This dispersal of expertise across NASA centers allows future missions to easily access experts for collaborative efforts of their own. Even the criterion used to choose an expert system has widespread impacts on the implementation, including the completion time and the final cost. In this paper we discuss, from inception to completion, the areas where our experiences in moving from three shifts to one shift may offer insights for other NASA missions.
Lewis, M.↗
Testing Mars Exploration Rover-Inspired Operational Strategies for Semi-Autonomous Rovers on the Moon Ii: the GeoHeuristic Operational Strategies Test in Alaska
We used MER-derived semi-autonomous rover science operations strategies to determine best practices suitable for remote semi-autonomous lunar rover geology. Two field teams studied two glacial moraines as analogs for potential ice-bearing lunar regolith. At each site a Rover Team commanded a human rover to execute observations based on common MER sequences; the resulting data were used to identify and characterize targets of interest. A Tiger Team followed the Rover Team using traditional terrestrial field methods, and the results of the two teams were compared. Narrowly defined goals that can be addressed using cm-scale or coarser resolution may be met sufficiently by the operational strategies adapted from MER survey mode. When reconnaissance is the primary goal, the strategies tested are necessary but not sufficient. Further, there may be a set of optimal observations for such narrowly defined, hypothesis-driven science goals, such that collecting further data would result in diminishing returns. We confirm results of previous tests that indicated systematic observations might improve efficiency during strategic planning, and improve science output during data analysis. This strategy does not markedly improve the rate at which a science team can ingest data to feed back into tactical decision-making. Other methods should be tested to separate the strategic and tactical processes, and to build in time for data analysis.
MER↗
The Earth Science Afternoon Constellation: Preparing for Autonomous but Coordinated Operations
This paper describes how the challenges of coordinating the autonomous operations of geographically dispersed mission control centers for several small and large satellites are being overcome. The Earth Science Afternoon Constellation, also referred to as the "A-Train", is an international grouping of five NASA satellites (two major NASA EOS missions and three NASA/Earth System Science Pathfinder missions) and one French satellite orbiting in close proximity. This grouping of satellites provides scientists with the opportunity to perform coincident observations using data from two or more instruments on various satellites with measurements taken at approximately the same time. Three of the six missions are currently on-orbit, with the two missions expected to join the constellation later this year and one mission in 2007. The operational challenges are daunting for several reasons. There are several Mission Control Centers (widely separated on two continents), operating autonomously under tight budget constraints. All of the Mission Control Centers have reasons to be concerned about safety while flying in close proximity to other satellites, but most Centers did not have the resources or the desire to address this concern alone - the interfaces are too numerous and anticipated operations too costly. Clearly, an efficient approach was needed. This paper describes the steps taken to make this Earth science constellation a reality. Agreements were forged to allow the Mission Control Centers to maintain their autonomy, while ensuring their satellite's safety. Each member mission in the constellation operates independently in accordance with its own mission requirements, but the member missions have agreed to coordinate their operations, i.e., orbital positions and control to ensure the safety of the entire constellation. A centralized system was developed at NASA Goddard Space Flight Center to collect, analyze, and distribute ephemeris data used by each of the mission teams to determine the positions of the satellites in the constellation. The system issues warnings regarding possible dangerous configurations, eliminating the need for redundant capabilities at each Mission Control Center. On-orbit contingency situations were identified and analyzed; agreements were reached in advance of contingency operations to ensure that coordination between the Mission Control Centers can be handled expeditiously and fairly. In this manner, recovery from anomalous situations can be more quickly realized, thereby increasing the science return and reducing costs. The process used to develop these contingency procedures and the systems used to facilitate the contingency resolution are described as well.
Case, Warren↗
Preliminary Concepts for the Materials Science Research Facility on the International Space Station
The Materials Science Research Facility (MSRF) is designed to accommodate the current and evolving cadre of peer-reviewed materials science investigations selected to conduct research in the microgravity environment of the International Space Station (ISS). The MSRF consists of modular autonomous Materials Science Research Racks (MSRR's). The initial MSRF concept consists of three Materials Science Research Racks (MSRR-1, MSRR-2, and MSRR-3) which will be developed for a phased deployment beginning on Utilization Flight 3. Each MSRR is a stand-alone autonomous rack and will be comprised of either on-orbit replaceable Experiment Modules, Module Inserts, investigation unique apparatus, or multi-user generic processing apparatus Each MSRR will support a wide variety of scientific investigations.
Cobb, S.D.↗
Space Technology 5: Pathfinder for Future Micro-Sat Constellations
The Space Technology 5 (ST-5) Project, currently in the implementation phase, is part of the National Aeronautics and Space Administration (NASA) s New Millennium Program (NMP). ST-5 will consist of a constellation of three miniature satellites, each with mass less than 25 kg and size approximately 60 cm by 30 cm. ST-5 addresses technology challenges, as well as fabrication, assembly, test and operations strategies for future micro-satellite missions. ST-5 will be deployed into a highly eccentric, geo-transfer orbit (GTO). This will expose the spacecraft to a high radiation environment as well as provide a low level magnetic background. A three-month flight demonstration phase is planned to validate the technologies and demonstrate concepts for future missions. Each ST-5 spacecraft incorporates NMP competitively-selected breakthrough technologies. These include Cold Gas Micro-Thrusters for propulsion and attitude control, miniature X-band transponder for space-ground communications, Variable Emittance Coatings for dynamic thermal control, and CULPRiT ultra low power logic chip used for Reed-Solomon encoding. The ST-5 spacecraft itself is a technology that can be infused into future missions. It is a fully functional micro-spacecraft built within tight volume and mass constraints. It is built to withstand a high radiation environment, large thermal variations, and high launch loads. The spacecraft power system is low-power and low-voltage, and is designed to turn on after separation &om the launch vehicle. Some of the innovations that are included in the ST-5 design are a custom spacecraft deployment structure, magnetometer deployment boom, nutation damper, X-band antenna, miniature spinning sun sensor, solar array with triple junction solar cells, integral card cage assembly containing single card Command and Data Handling and Power System Electronics, miniature magnetometer, and lithium ion battery. ST-5 will demonstrate the ability of a micro satellite to perform research-quality science. Each ST-5 spacecraft will deploy a precision magnetometer to be used both for attitude determination and as a representative science instrument. The spacecraft has been developed with a low magnetic signature to avoid interference with the magnetometer. The spacecraft will be able to detect and respond autonomously to science events, i.e. significant changes in the magnetic field measurements. The three spacecraft will be a pathfinder for future constellation missions. They will be deployed to demonstrate an appropriate geometry for scientific measurements as a constellation. They will be operationally managed as a constellation, demonstrating automation and communication strategies that will be useful for future missions. The technologies and future mission concepts will be validated both on the ground and in space. Technologies will be validated on the ground by a combination of component level and system level testing of the flight hardware in a thermal vacuum environment. In flight, specific validation runs are planned for each of the technologies. Each validation run consists of one or more orbits with a specific validation objective. This paper will describe the ST-5 mission, and the applicability of the NMP technologies, spacecraft, and mission concepts to future missions. It will also discuss the validation approach for the ST-5 technologies and mission concepts.
Carlisle, Candace↗
NASA Tech Briefs, June 2011
Topics covered include: Wind and Temperature Spectrometry of the Upper Atmosphere in Low-Earth Orbit; Health Monitor for Multitasking, Safety-Critical, Real-Time Software; Stereo Imaging Miniature Endoscope; Early Oscillation Detection Technique for Hybrid DC/DC Converters; Parallel Wavefront Analysis for a 4D Interferometer; Schottky Heterodyne Receivers With Full Waveguide Bandwidth; Carbon Nanofiber-Based, High-Frequency, High-Q, Miniaturized Mechanical Resonators; Ultracapacitor-Based Uninterrupted Power Supply System; Coaxial Cables for Martian Extreme Temperature Environments; Using Spare Logic Resources To Create Dynamic Test Points; Autonomous Coordination of Science Observations Using Multiple Spacecraft; Autonomous Phase Retrieval Calibration; EOS MLS Level 1B Data Processing Software, Version 3; Cassini Tour Atlas Automated Generation; Software Development Standard Processes (SDSP); Graphite Composite Panel Polishing Fixture; Material Gradients in Oxygen System Components Improve Safety; Ridge Waveguide Structures in Magnesium-Doped Lithium Niobate; Modifying Matrix Materials to Increase Wetting and Adhesion; Lightweight Magnetic Cooler With a Reversible Circulator; The Invasive Species Forecasting System; Method for Cleanly and Precisely Breaking Off a Rock Core Using a Radial Compressive Force; Praying Mantis Bending Core Breakoff and Retention Mechanism; Scoring Dawg Core Breakoff and Retention Mechanism; Rolling-Tooth Core Breakoff and Retention Mechanism; Vibration Isolation and Stabilization System for Spacecraft Exercise Treadmill Devices; Microgravity-Enhanced Stem Cell Selection; Diagnosis and Treatment of Neurological Disorders by Millimeter-Wave Stimulation; Passive Vaporizing Heat Sink; Remote Sensing and Quantization of Analog Sensors; Phase Retrieval for Radio Telescope and Antenna Control; Helium-Cooled Black Shroud for Subscale Cryogenic Testing; Receive Mode Analysis and Design of Microstrip Reflectarrays; and Chance-Constrained Guidance With Non-Convex Constraints.
Source record↗
The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization
Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.
autonomous↗
From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments
The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.
artificial intelligence↗
Onboard science analysis and replanning for increased science return
The Autonomous Sciencecraft Constellation (ASC) experiment, which will fly onboard the Air Force's TechSat-21 constellation, will demonstrate the use of onboard science analysis and replanning to increase science return.
onboard↗
Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science
This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.
97 MATHEMATICS AND COMPUTING↗
Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science
This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.
36 MATERIALS SCIENCE↗
Results From the First Four Years of Aegis Autonomous Target-ing for Chemcam on Mars Science Laboratory and New Capability Planned for Supercam on Mars 2020 Rover
Autonomous Exploration for Gathering Increased Science (AEGIS) was uploaded on the NASA Mars Science Laboratory (MSL) Curiosity rover in 2015 for autonomous target selection. This paper presents results from the first four years of its regular opera-tion on Mars for autonomously selecting targets for the ChemCam remote geochemical spectrometer with a focus on the most recent findings. Results show that AEGIS has targeted the most desired material greater than 93% of the time vs 24% without onboard intelligent targeting. There has also been a notable increase in the rate of ChemCam observations. AE-GIS is also part of surface flight software for the NASA Mars 2020 Perseverance rover. This paper describes new AEGIS capabilities that will be availa-ble for autonomously targeting the SuperCam in-strument after the planned 18 February 2021 landing in Jezero crater on Mars.
Castano, R↗
Beyond Human Vision: Exploring Materials with Machine Intelligence
Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.
artificial intelligence↗
Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis
Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, in this study, we demonstrate an approach to enable real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD). We validated the approach using simulated and experimental reflectivity data for WSe 2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T'-MoTe 2 . The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.
36 MATERIALS SCIENCE↗
Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)
Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.
59 BASIC BIOLOGICAL SCIENCES↗
Evaluating the Impacts of Autonomous Electric Vehicles Adoption on Vehicle Miles Traveled and CO2 Emissions
Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO2) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO2 yearly, which is approximately 30% of the total CO2 emitted by internal combustion engine (ICE) cars in the region.
33 ADVANCED PROPULSION SYSTEMS↗