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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2025

Temperature Variation Analysis of the STIM300 Inertial Measurement Unit

A key navigation instrument found on most autonomous and semi-autonomous platforms is the inertial measurement unit (IMU). This type of sensor is a combination of microelectromechanical systems (MEMS) that provide inertial information to the navigation system. Typical MEMS hardware include a gyroscope and an accelerometer (for rates and accelerations, respectively) within a single platform. The Near Earth Asteroid (NEA) Scout mission, slated to launch in October of 2018 (Author’s note: NEA Scout ultimately launch on November 16, 2022), carries an on-board IMU from Sensonor. This particular model has yet to see spaceflight, and questions about its on-orbit reliability remain. Dynamic tests were produced in the summer of 2016 by a visiting faculty member from Arkansas Tech University, Daniel Bullock. During these experiments, concerns about the noise characteristics of the sensor were raised, specifically relating to how temperature fluctuations affected the unit. It was found that the IMU took an extended period of time before reaching an internal steady state temperature. These tests often displayed large temperature variations, making the calculation of noise characteristics, such as bias drift, difficult to attribute entirely to noise. Static and dynamic tests were re-performed under more stringent constraints on temperature. Tests reported herein conclude that the IMU is adequately capable of providing inertial information for the NEA Scout mission.

Ivan Rodrigues Bertaska↗
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗
DOE OSTI2020

The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Maintaining Situation Awareness with Autonomous Airborne Observation Platforms

Unmanned Aerial Vehicles (UAVs) offer tremendous potential as intelligence, surveillance and reconnaissance (ISR) platforms for early detection of security threats and for acquisition and maintenance of situation awareness in crisis conditions. However, using their capabilities effectively requires addressing a range of practical and theoretical problems. The paper will describe progress by the "Autonomous Rotorcraft Project," a collaborative effort between NASA and the U.S. Army to develop a practical, flexible capability for UAV-based ISR. Important facets of the project include optimization methods for allocating scarce aircraft resources to observe numerous, distinct sites of interest; intelligent flight automation software than integrates high-level plan generation capabilities with executive control, failure response and flight control functions; a system architecture supporting reconfiguration of onboard sensors to address different kinds of threats; and an advanced prototype vehicle designed to allow large-scale production at low cost. The paper will also address human interaction issues including an empirical method for determining how to allocate roles and responsibilities between flight automation and human operations.

Freed, Michael↗
NASA NTRS2001

Task-Level Control for a Full Semi-Autonomous Mission: Test Platform Development and Demonstration

Pseudolites can extend the availability of GPS-type positioning systems to a wide range of applications not possible with satellite-only GPS, including indoor and deep-space applications. Conventional GPS pseudolite arrays require that the devices be pre-calibrated through a survey of their locations, typically to sub-centimeter accuracy. This can sometimes be a difficult task, especially in remote or hazardous environments. By using the GPS signals that the pseudolites broadcast, however, it is possible to have the array self-survey its own relative locations, creating a Self-Calibrating Pseudolite Array (SCPA). In order to provide the bi-directional ranging signals between devices necessary for array self-calibration, pseudolite transceivers must be used. The basic principles behind the use of transceivers to create an SCPA were first presented in paper presented to the Institute of Navigation GPS-98 Conference. This paper begins with a brief review of the transceiver architecture and the fundamental direct-ranging algorithm presented in that paper. This is followed by a description of a prototype self-differencing transceiver system that has been constructed, and a presentation of experimental code- and carrier-phase ranging data obtained using that system. A second algorithm is then described which uses these fundamental range measurements between transceiver pairs to self-calibrate a larger stationary array and to provide positioning information for a vehicle moving within that array. Simulation results validating the accuracy and effective convergence of this algorithm are also presented.

Rock, Stephen M.↗
NASA NTRSDate not supplied

Model-Based Systems Engineering, Real-Time Operations, and Autonomy

Model-Based Systems Engineering has been enabled by the development of the SysML language and software tools to create systems models. Systems models described in SysML incorporate frames (Diagrams) that represent behaviors (activities, sequences, state machines, use cases), requirements, and structure (definitions, internal structure, parametric formulation, and packaging). The SysML models are, in turn, used by applications to do analysis and studies of the designs and operational capabilities. These uses of the model are based on simulations, and do not include hardware. This paper presents a software environment and processes that enables more comprehensive systems models for MBSE, and use of these rich models for real-time operations. The paper describes a software platform that enables creation of comprehensive models, beyond what is now possible with SysML and related software tools, called the NASA Platform for Autonomous Systems (NPAS). The platform encapsulates a paradigm and infrastructure for creating systems models with complexity levels comparable to the ones handled by SysML software tools, but with additional fidelity that includes detailed design diagrams encompassing sensors, components, and design topologies. Furthermore, NPAS enables incorporation of data, information, and knowledge (DIaK) to implement autonomy and Integrated System Health Management (ISHM) and the inherent integration of content encompassing SysML structure and behavior diagrams throughout the NPAS modelAnd lastly, the NPAS models are used in real-time operations, taking advantage of the fidelity and complexity encompassed in the models in order to implement “thinking” ISHM and/or autonomous operations. . Incorporation of SysML model content into an NPAS model is briefly discussed.

MBSE↗
NASA NTRS1985

Space platforms and autonomy

Potential applications for autonomous space platforms (SP) are discussed. The platforms are assumed to have long in-service lifetimes and therefore be flexible as to configuration modification and payload changeout. Higher degrees of autonomy, particularly from ground control, are made possible because of the rapid increase of microprocessor power and artificial intelligence advances. Functioning independently, the platforms are to rely only on periodic refurbishment visits by, e.g., the Orbiter. The Manned Space Station (MSS) will be the most complex structure, involving multifacted man-machine interfaces. The SP can be subsystems of the MSS (or other platforms), handling communications enunciation, data acquisition, analysis and telemetry, fault detection and isolation, systems monitoring and control, etc. The SP adopted will depend in all cases on costs vs benefits analyses to determine the worth of removing the function(s) from direct, regular human intervention.

Easter, R. W.↗
NASA NTRSDate not supplied

Space Transformation -- Localizing the Remote and Connecting the Isolated

In motivating the Space Transformation theme for this year’s 4S symposium, the organizers provided the following context, “Transformation of economies are driven by a change in values and accelerated by new technologies.” These words rang particularly true when I read them at the beginning of the holiday season. Like so many others, I was in the early phases of my Christmas shopping procrastination campaign, and I’d just been reflecting on how Amazon Prime was the transformational tool I’d been waiting for. Basic limiting principles of time and space, supply and demand, were all but erased by the Amazon Prime phenomenon. Coupled with emerging 3D printing and other adaptive manufacturing technologies, a transformation from deliberate planning to “think it … have it” had occurred, empowering me to procrastinate longer than I’d ever dreamed possible. The organizers went on to ponder, “Will space transformation also affect society?”, just as our team at the Air Force Research Lab’s (AFRL) Center for Rapid Innovation (CRI) were working alongside partners within our larger Integrated Capabilities Directorate, NASA’s Flight Opportunities and Small Spacecraft Technology programs, and DARPA’s Luna-10 program to develop technologies and execute demonstration missions that leverage the space domain to genuinely connect even the most remote and austere domains on the timeline of need. Picking apart the miracle that is Amazon prime, where does the model fail, and why? More relevantly to the theme of this year’s symposium, how can the space domain be used to overcome its limitations and minimize its weaknesses? Perhaps it is best assessed in the context of Use Cases. What are the Amazon delivery cost, schedule, and cargo limiters to the Amundsen-Scott South Pole Research Station, or the Lunar South Pole Research Station? This paper will explore enabling infrastructure that allows Amazon prime to thrive and assess the transformational enabling technologies that would be necessary to extend that miracle to the truly remote or the truly austere. Localizing the Remote • First, it will evaluate the ability of the on-going AFRL Rocket Cargo and Space Initiatives Ringside Seats systems, coupled with Astrobotic’s Xodiak and Xogdor capabilities, developed to support the NASA Flight Opportunities Program (FOP), to supply orbital/suborbital delivery to both improved and austere sites on the Earth and Moon. • Then, it will add the surface terminal distribution leg, with an examination of Lunar Outpost’s Mobile Autonomous Prospecting Platform (MAPP), equipped with Mobile Autonomous Robotic Swarm (MARS) software, and Intuitive Machine’s Hopper, developed with support of AFRL and NASA’s Commercial Lunar Payload Services (CLPS) program. Connecting the Isolated From there, it will focus on the destination, asking what implied destination services are required to support highly assured autonomous delivery. • Specifically, it will highlight Astrobotic’s Skymage mesh-networked publish and subscribe communication and navigation service, as well as AFRL’s on-going developments of radioisotope and reactor nuclear-sourced thermoelectric power generation and distribution systems under development under the Joint Emergent Technology Supplying On-orbit Nuclear Power (JETSON) program by Lockheed Martin, Westinghouse, Intuitive Machines, and Zeno Power, to provide the power service to locations well off the grid. • Finally, the paper will connect to the “human machine”. What connects the remote or in-situ human consumer to the remote domain? What connects the diverse international government and commercial services to each other? The former will focus on AFRL’s OraCloud feeding their Space Defense Control and Characterization System (SDCCS) and Lunar Station’s MoonHacker systems, while the latter will focus on the BlueHalo/Tensor LunX Technology Platform for the Cislunar Commodity Marketplace. In 1984, Krafft Ehricke famously remarked that, “If God wanted man to become a spacefaring species, He would have given man a Moon.” This paper is not about the Moon, but is about humans as a spacefaring species, shedding the pesky land/air limitations of the Amazon Prime model … so that we can all live a procrastinator’s “think it … have it” existence.

Charles Finley↗
DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2012

Method and system for providing autonomous control of a platform

The present application provides a system for enabling instrument placement from distances on the order of five meters, for example, and increases accuracy of the instrument placement relative to visually-specified targets. The system provides precision control of a mobile base of a rover and onboard manipulators (e.g., robotic arms) relative to a visually-specified target using one or more sets of cameras. The system automatically compensates for wheel slippage and kinematic inaccuracy ensuring accurate placement (on the order of 2 mm, for example) of the instrument relative to the target. The system provides the ability for autonomous instrument placement by controlling both the base of the rover and the onboard manipulator using a single set of cameras. To extend the distance from which the placement can be completed to nearly five meters, target information may be transferred from navigation cameras (used for long-range) to front hazard cameras (used for positioning the manipulator).

Seelinger, Michael J.↗
DOE OSTI2025

A generalized platform for artificial intelligence-powered autonomous enzyme engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-fold improvement in substrate preference and 16-fold improvement in ethyltransferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2009

Life Science Research in Outer Space: New Platform Technologies for Low-Cost, Autonomous Small Satellite Missions

We develop integrated instruments and platforms suitable for economical, frequent space access for autonomous life science experiments and processes in outer space. The technologies represented by three of our recent free-flyer small-satellite missions are the basis of a rapidly growing toolbox of miniaturized biologically/biochemically-oriented instrumentation now enabling a new generation of in-situ space experiments. Autonomous small satellites (~ 1 50 kg) are less expensive to develop and build than fullsize spacecraft and not subject to the comparatively high costs and scheduling challenges of human-tended experimentation on the International Space Station, Space Shuttle, and comparable platforms. A growing number of commercial, government, military, and civilian space launches now carry small secondary science payloads at far lower cost than dedicated missions; the number of opportunities is particularly large for so-called cube-sat and multicube satellites in the 1 10 kg range. The recent explosion in nano-, micro-, and miniature technologies, spanning fields from telecommunications to materials to bio/chemical analysis, enables development of remarkably capable autonomous miniaturized instruments to accomplish remote biological experimentation. High-throughput drug discovery, point-of-care medical diagnostics, and genetic analysis are applications driving rapid progress in autonomous bioanalytical technology. Three of our recent missions exemplify the development of miniaturized analytical payload instrumentation: GeneSat-1 (launched: December 2006), PharmaSat (launched: May 2009), and O/OREOS (organism/organics exposure to orbital stresses; scheduled launch: May 2010). We will highlight the overall architecture and integration of fluidic, optical, sensor, thermal, and electronic technologies and subsystems to support and monitor the growth of microorganisms in culture in these small autonomous space satellites, including real-time tracking of their culture density, gene expression, and metabolic activity while in the space environment. Flight data and results will be presented from GeneSat-1, which tracked gene expression levels of GFP-labeled E. coli and from PharmaSat, which monitored the dose dependency of an antifungal agent against S. cerevisiae. The O/OREOS SESLO instrument, which will study the effects of radiation and microgravity upon the viability and growth characteristics of B. subtilis and the halophile Halorubrum chaoviatoris for periods of 0 - 6 months in space, will be described as well. The ongoing expansion of the small satellite toolbox of biological technologies will be summarized.

Ricco, Antonio J.↗
DOE OSTI2026

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.

Anomaly detection↗
NASA NTRS2012

Autonomous Operations Planner: A Flexible Platform for Research in Flight-Deck Support for Airborne Self-Separation

The Autonomous Operations Planner (AOP), developed by NASA, is a flexible and powerful prototype of a flight-deck automation system to support self-separation of aircraft. The AOP incorporates a variety of algorithms to detect and resolve conflicts between the trajectories of its own aircraft and traffic aircraft while meeting route constraints such as required times of arrival and avoiding airspace hazards such as convective weather and restricted airspace. This integrated suite of algorithms provides flight crew support for strategic and tactical conflict resolutions and conflict-free trajectory planning while en route. The AOP has supported an extensive set of experiments covering various conditions and variations on the self-separation concept, yielding insight into the system s design and resolving various challenges encountered in the exploration of the concept. The design of the AOP will enable it to continue to evolve and support experimentation as the self-separation concept is refined.

Karr, David A.↗
DOE OSTI2023

Control System of Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS) & HIL Platforms for Design

Multi-port autonomous reconfigurable solar power plant (MARS) provides an attractive alternative to connect photovoltaic (PV) and energy storage systems (ESSs) to high-voltage direct current (HVdc) links and high-voltage alternating current (ac) grids. In this paper, a unique hierarchical control system of MARS is proposed and evaluated. To evaluate the control system and associated algorithms in early-stage research of complex architectures like MARS, it is important to develop unique suitable hardware-in-the-loop (HIL) platforms. In this paper, the HIL platforms for MARS to evaluate the performance of the hierarchical control system and the control algorithms implemented are presented. Further, they help with the design process of control systems. The real-time simulation models and algorithms that are utilized for MARS in the HIL platforms are also discussed in the paper. The HIL experiments of the control system of MARS showcase the capability to provide continuity of operation under faults and frequency support to the power grid during loss of generation. They also showcase the stability of the proposed hierarchical control system of MARS.

14 SOLAR ENERGY↗
DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Implementing Artificial Thinking Autonomy with Model-Based System Engineering

Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.

Artificial Thought↗