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DOE OSTI2024

Shaping the Future of Self-Driving Autonomous Laboratories Workshop

The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Polarimetry-Enhanced Imaging towards Autonomous Solar Field and Receiver Inspections

During the typical operation of a Concentrating Solar Power (CSP) plant, a large portion of the energy (~45%) can be lost due to various imperfect conditions, such as blocking, shading, mirror soiling, tracking and canting errors, etc. It is necessary to develop efficient and effective field inspection technology to optically survey and characterize a CSP field, which can be used as an input for autonomous control, maintenance scheduling and spot repair whenever necessary to maximize the overall efficiency of the plant. In this project, We aim to apply polarimetric imaging for CSP collector and receiver inspection and develop polarimetric drone cameras (via integrating polarimetric imagers onto drones) for autonomous field inspection in CSP plants.

14 SOLAR ENERGY↗

DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Autonomous Radiation Cartographer (ARC) System Training Manual

This training manual is designed to provide end users with a comprehensive knowledge base for the safe and effective use of the Autonomous Radiation Cartographer (ARC) System. The ARC is a fully autonomous radiation detection robot based on the Spot Robot platform manufactured by Boston Dynamics.

42 ENGINEERING↗

DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1972

Autonomous power subsystem design for an Outer Planet Spacecraft.

This paper describes the overall design of the Thermoelectric Outer Planet Spacecraft (TOPS) power subsystem. It discusses the implementation of spacecraft requirements into a fault-tolerant design in which an on-board self-test and repair computer is utilized to provide autonomous operation. Development of a protected bus concept is discussed whereby electrical power supplied to essential spacecraft loads is maintained in the event of major on-board power blackouts. Particular attention is given to describing the interfaces and operation of the power subsystem with the spacecraft control computer subsystem. Autonomous power management operations are discussed where the on-board computer adjusts the spacecraft load demand to provide maximum utilization of source power for a given mission mode.

Andrews, R. E.↗

NASA NTRS1975

Autonomous navigation preparations for future unmanned space missions

In order to prepare for the era of autonomous navigation, this paper first examines existing techniques for spacecraft navigation by describing the basic elements of the Mark-I and II navigation technologies. The remainder of the paper deals with identification of criteria which necessitate onboard navigation, and with promising solutions now under consideration. An attractive solution possibility is proposed which utilizes narrow-angle optics, a charge-coupled device area-array image sensor, a small advanced onboard flight computer, and a simplified set of software algorithms. Onboard commands would be derived and sent to the appropriate spacecraft attitude control, propulsion, and science platform pointing subsystems. The proposed autonomous navigation system does not address all mission options, but focuses primarily on precision approach trajectory control and adaptive science instrument pointing for asteroid, cometary, and outer planet satellite missions.

Kohlhase, C. E.↗

NASA NTRS1975

Autonomous target acquisition techniques

Target acquisition is often needed in a deep space mission where the detector on board the space vehicle must be able to perform the decision making process in acquiring the target. That is, the system for target acquisition must be autonomous. This paper presents several autonomous target acquisition techniques, applicable to deep space mission, for detecting stationary and moving targets. These techniques are useful for sensors such as radar, star tracker and television since the target must be found before it can be tracked. A minimum signal-to-noise ratio can be specified for successful acquisition of target.

Brock, H. I.↗

NASA NTRS1977

Autonomous satellite navigation with the Global Positioning System

This paper discusses the potential of using the Global Positioning System (GPS) to provide autonomous navigation capability to NASA satellites in the 1980 era. Some of the driving forces motivating autonomous navigation are presented. These include such factors as advances in attitude control systems, onboard science annotation, and onboard gridding of imaging data. Simulation results which demonstrate baseline orbit determination accuracies using GPS data on Seasat, Landsat-D, and the Solar Maximum Mission are presented. Emphasis is placed on identifying error sources such as GPS time, GPS ephemeris, user timing biases, and user orbit dynamics, and in a parametric sense on evaluating their contribution to the orbit determination accuracies.

A J Fuchs↗

NASA NTRS1978

The role of autonomous satellite navigation in the NEEDS program

A number of applications of autonomous satellite navigation are discussed. They include the onboard annotation of all experimental data and the onboard correction of imaging data. The accuracy levels required for each function are presented. Two alternative techniques for achieving autonomous satellite navigation are considered. One technique utilizes data from the Global Positioning System (GPS). The other approach utilizes data from a geosynchronous relay satellite system such as the Tracking and Data Relay Satellite System (TDRSS). The TDRSS approach makes use of one-way range and Doppler data along with a time transfer mechanism. Comparative accuracy levels for the onboard determination of position, velocity, and time based upon the GPS and TDRSS approaches are presented.

Fuchs, A. J.↗

NASA NTRS1981

Autonomous spacecraft maintenance study group

A plan to incorporate autonomous spacecraft maintenance (ASM) capabilities into Air Force spacecraft by 1989 is outlined. It includes the successful operation of the spacecraft without ground operator intervention for extended periods of time. Mechanisms, along with a fault tolerant data processing system (including a nonvolatile backup memory) and an autonomous navigation capability, are needed to replace the routine servicing that is presently performed by the ground system. The state of the art fault handling capabilities of various spacecraft and computers are described, and a set conceptual design requirements needed to achieve ASM is established. Implementations for near term technology development needed for an ASM proof of concept demonstration by 1985, and a research agenda addressing long range academic research for an advanced ASM system for 1990s are established.

Marshall, M. H.↗

NASA NTRS1982

Autonomous scheduling technology for Earth orbital missions

The development of a dynamic autonomous system (DYASS) of resources for the mission support of near-Earth NASA spacecraft is discussed and the current NASA space data system is described from a functional perspective. The future (late 80's and early 90's) NASA space data system is discussed. The DYASS concept, the autonomous process control, and the NASA space data system are introduced. Scheduling and related disciplines are surveyed. DYASS as a scheduling problem is also discussed. Artificial intelligence and knowledge representation is considered as well as the NUDGE system and the I-Space system.

Srivastava, S.↗

NASA NTRS1982

Autonomous onboard crew operations: A review and developmental approach

A review of the literature generated by an intercenter mission approach and consolidation team and their contractors was performed to obtain background information on the development of autonomous operations concepts for future space shuttle and space platform missions. The Boeing 757/767 flight management system was examined to determine the relevance for transfer of the developmental approach and technology to the performance of the crew operations function. In specific, the engine indications and crew alerting system was studied to determine the relevance of this display for the performance of crew operations onboard the vehicle. It was concluded that the developmental approach and technology utilized in the aeronautics industry would be appropriate for development of an autonomous operations concept for the space platform.

Rogers, J. G.↗

NASA NTRS1984

Autonomous Integrated Receive System (AIRS) requirements definition. Volume 3: Performance and simulation

The autonomous and integrated aspects of the operation of the AIRS (Autonomous Integrated Receive System) are discussed from a system operation point of view. The advantages of AIRS compared to the existing SSA receive chain equipment are highlighted. The three modes of AIRS operation are addressed in detail. The configurations of the AIRS are defined as a function of the operating modes and the user signal characteristics. Each AIRS configuration selection is made up of three components: the hardware, the software algorithms and the parameters used by these algorithms. A comparison between AIRS and the wide dynamics demodulation (WDD) is provided. The organization of the AIRS analytical/simulation software is described. The modeling and analysis is for simulating the performance of the PN subsystem is documented. The frequence acquisition technique using a frequency-locked loop is also documented. Doppler compensation implementation is described. The technological aspects of employing CCD's for PN acquisition are addressed.

Chie, C. M.↗

NASA NTRS1983

Algorithm development for a spacecraft autonomous power system

Two algorithms were developed to demonstrate the implementation of autonomous functions in an existing spacecraft power system. The functions selected for autonomous operation include a typical performance monitoring function, battery state of charge, and a fault detection and response function represented by a battery state of charge below a preselected limit. The constraints imposed by the existing power system configuration are the available data in the telemetry stream and the existing commands and command structure. The areas requiring future development are the degree of battery characterization, the effects of hardware/software faults, and the verification of faults.

Bridgeforth, A. O.↗

NASA NTRS1984

Autonomous spacecraft design methodology

A methodology for autonomous spacecraft design blends autonomy requirements with traditional mission requirements and assesses the impact of autonomy upon the total system resources available to support fault-tolerance and automation. A baseline functional design can be examined for autonomy implementation impacts, and the costs, risk, and benefits of various options can be assessed. The result of the process is a baseline design that includes autonomous control functions.

Divita, E. L.↗

NASA NTRS1985

Autonomously managed high power systems

The need for autonomous power management capabilities will increase as the power levels of spacecraft increase into the multi-100 kW range. The quantity of labor intensive ground and crew support consumed by the 9 kW Skylab cannot be afforded in support of a 75-300 kW Space Station or high power earth orbital and interplanetary spacecraft. Marshall Space Flight Center is managing a program to develop necessary technologies for high power system autonomous management. To date a reference electrical power system and automation approaches have been defined. A test facility for evaluation and verification of management algorithms and hardware has been designed with the first of the three power channel capability nearing completion.

Weeks, D. J.↗