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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Developments in Online Monitoring Technologies for Autonomous Microreactor Operations

This report presents the results of an ongoing research and development (R&D) effort to develop an online monitoring (OLM) system to support autonomous microreactor operations. A key component of this work is an evaluation of Artificial Intelligence (AI) and Machine Learning (ML) techniques to identify, diagnose, and predict problems with sensors and processes of the reactor. As described herein, selected methods of AI/ML were used to identify and diagnose anomalous sensor and system behaviors using data from a thermal-hydraulic flow loop and from operating nuclear power plants. This work serves to further the state-of-the-art in OLM technologies for nuclear reactor applications and will ultimately result in a comprehensive system to enable OLM of critical structures, systems, components, and processes in microreactors.

Machine learning↗
NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗
NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗
DOE OSTI2020

Real-time biomass feedstock particle quality detection using image analysis and machine vision

Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2020

Scale-free, programmable design of morphable chain loops of kilobots and colloidal motors

Micron-scale robots require systems that can morph into arbitrary target configurations controlled by external agents such as heat, light, electricity, and chemical environment. Achieving this behavior using conventional approaches is challenging because the available materials at these scales are not programmable like their macroscopic counterparts. To overcome this challenge, we propose a design strategy to make a robotic machine that is both programmable and compatible with colloidal-scale physics. Our strategy uses motors in the form of active colloidal particles that constantly propel forward. We sequence these motors end-to-end in a closed chain forming a two-dimensional loop that folds under its mechanical constraints. We encode the target loop shape and its motion by regulating six design parameters, each scale-invariant and achievable at the colloidal scale. We demonstrate the plausibility of our design strategy using centimeter-scale robots called kilobots . We use Brownian dynamics simulation to explore the large design space beyond that possible with kilobots, and present an analytical theory to aid the design process. Multiple loops can also be fused together to achieve several complex shapes and robotic behaviors, demonstrated by folding a letter shape “M,” a dynamic gripper , and a dynamic pacman . The material-agnostic, scale-free, and programmable nature of our design enables building a variety of reconfigurable and autonomous robots at both colloidal scales and macroscales.

36 MATERIALS SCIENCE↗
NASA NTRS2001

Fault Tolerant Magnetic Bearing for Turbomachinery

NASA Glenn Research Center (GRC) has developed a Fault-Tolerant Magnetic Bearing Suspension rig to enhance the bearing system safety. It successfully demonstrated that using only two active poles out of eight redundant poles from each radial bearing (that is, simply 12 out of 16 poles dead) levitated the rotor and spun it without losing stability and desired position up to the maximum allowable speed of 20,000 rpm. In this paper, it is demonstrated that as far as the summation of force vectors of the attracting poles and rotor weight is zero, a fault-tolerant magnetic bearing system maintained the rotor at the desired position without losing stability even at the maximum rotor speed. A proportional-integral-derivative (PID) controller generated autonomous corrective actions with no operator's input for the fault situations without losing load capacity in terms of rotor position. This paper also deals with a centralized modal controller to better control the dynamic behavior over system modes.

Choi, Benjamin↗
NASA NTRS2001

Adjustable Autonomy Testbed

The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.

Malin, Jane T.↗
NASA NTRS2016

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

Hybrid Systems Diagnosis↗
NASA NTRS2001

Learning Sequences of Actions in Collectives of Autonomous Agents

In this paper we focus on the problem of designing a collective of autonomous agents that individually learn sequences of actions such that the resultant sequence of joint actions achieves a predetermined global objective. We are particularly interested in instances of this problem where centralized control is either impossible or impractical. For single agent systems in similar domains, machine learning methods (e.g., reinforcement learners) have been successfully used. However, applying such solutions directly to multi-agent systems often proves problematic, as agents may work at cross-purposes, or have difficulty in evaluating their contribution to achievement of the global objective, or both. Accordingly, the crucial design step in multiagent systems centers on determining the private objectives of each agent so that as the agents strive for those objectives, the system reaches a good global solution. In this work we consider a version of this problem involving multiple autonomous agents in a grid world. We use concepts from collective intelligence to design goals for the agents that are 'aligned' with the global goal, and are 'learnable' in that agents can readily see how their behavior affects their utility. We show that reinforcement learning agents using those goals outperform both 'natural' extensions of single agent algorithms and global reinforcement, learning solutions based on 'team games'.

Turner, Kagan↗
NASA NTRS2003

Wavelet-Based Real-Time Diagnosis of Complex Systems

A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning.

Gulati, Sandeep↗
NASA NTRS2018

Assurance of Model-Based Fault Diagnosis

Autonomy is an increasingly important technology for robotic scientific and commercial spacecraft. An important motivation for developing onboard autonomy is to enable quick response to dynamic environment and situations, including fault conditions that a spacecraft may encounter. The reliability of such autonomous capabilities depends on the quality of their knowledge of a spacecraft’s health state. Model-based approaches to fault management, i.e. model-based fault diagnosis (MBFD), is one approach to continuously verify correct behavior in addition to diagnosing symptoms to estimate the spacecraft’s health state. The proper functioning of MBFD is dependent on 1) the quality of the model that is analyzed and compared to the outputs of onboard sensors to estimate the system’s health state, and 2) the correct functioning of the diagnosis engine that interrogates the model and compares its analysis to observed system behavior. We are currently developing Verification and Validation (V&V) approaches to provide the necessary confidence that MBFD systems are correctly estimating the health of on-board spacecraft components and systems. Our work is intended to narrow the gap between the rapidly maturing field of ModelBased System Engineering (including MBFD) and the less-well understood area of identifying and applying appropriate V&V techniques to MBFD. Our effort is investigating three areas: 1) developing V&V techniques for the diagnostic model, 2) developing V&V techniques for the diagnostic engine in isolation, and 3) developing V&V techniques for the diagnostic engine and model in combination. This paper describes the work we have completed in the first area. We describe our approach to selecting a system to be represented by a model, the approach to modeling the system, the verification approach we developed for the model, and the results of the verification activity. We conclude with a description of the work remaining in the last two areas, which will be addressed over the next two years.

Chung, Seung↗
DOE OSTI2026

Autonomous elemental characterization enabled by a low cost robotic platform built upon a generalized software architecture

Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.

Cao, Xuan [Lawrence Berkeley National Laboratory (↗
DOE OSTI2025

Autonomous Anomaly Detection For Continuous Streams

The code implements the Isolation Forest (IFML) algorithm within the digital twin (DT) of the AGN-201 nuclear reactor. The DT captures real-time operational data including control rod positions, reactor power, and temperature. The IFML model isolates anomalies by detecting patterns that deviate from expected operational behavior. The algorithm recursively partitions the data and assigns anomaly scores based on the isolation of rare and different events. By tuning parameters specific to the reactor’s operational data, the IFML identifies deviations such as unauthorized material insertions or reactor reactivity shifts. The system streams data using LabView and integrates with the DeepLynx data warehouse for anomaly processing.

Trevino, Eduardo↗
NASA NTRS2025

HabSim: A Modular Coupled Virtual Testbed for Simulating ExtraTerrestrial Habitat Systems

Extraterrestrial habitats involve a tightly coupled combination of hardware, software, and humans while operating in an unforgiving environment that poses many risks, both anticipated and unanticipated. Traditional approaches with such systems of systems focus on reliability, robustness, and redundancy. These approaches seek to avoid failure rather than reduce overall risk. However, faults are inevitable, and understanding and managing the complex and emergent behavior and cascading events of such a complex system is critical. This study describes the development of HabSim, a computational simulation environment intended to support research to establish the know-how to design and operate resilient and autonomous SmartHabs. HabSim is a modular virtual testbed composed of many of the coupled dynamic systems expected in a typical SmartHab. A heterogeneous set of interconnected physics-based and phenomenological models is used to represent the essential functions of a SmartHab. HabSim further considers disruptions and models damage and repair of certain components. This paper discusses a) system and subsystem requirements of the deep space habitat included in the HabSim platform; b) architectural choices made in response to the requirements; c) technical considerations for developing, verifying, configuring, and executing HabSim; and d) illustrative sample results from a simulation of a representative disruption scenario.

Mohsen Azimi↗
NASA NTRS1989

A design strategy for autonomous systems

Some solutions to crucial issues regarding the competent performance of an autonomously operating robot are identified; namely, that of handling multiple and variable data sources containing overlapping information and maintaining coherent operation while responding adequately to changes in the environment. Support for the ideas developed for the construction of such behavior are extracted from speculations in the study of cognitive psychology, an understanding of the behavior of controlled mechanisms, and the development of behavior-based robots in a few robot research laboratories. The validity of these ideas is supported by some simple simulation experiments in the field of mobile robot navigation and guidance.

Forster, Pete↗
NASA NTRS1990

Monitoring the health of control system components

One characteristic of intelligent behavior is recognition of impaired capability. So an intelligent system should be able to monitor its own performance and determine when one of its components has failed - causing it to lose some degree of capability. This is especially important in autonomous operation which is one of the primary motivations for intelligent systems. This paper briefly reviews the principal approaches that have been suggested for monitoring control systems for failures of its sensors and actuators. The important characteristics of these methods are noted and two are selected for further consideration. These two methods, the failure detection filter and generalized parity relations, are described. Results of experiments are then given of detection and isolation of sensor failures using data taken on the Mini-Mast facility at the NASA Langley Research Center. The effects of certain design options are illustrated. The paper concludes with some observations about the general problem of monitoring the health of control systems.

Vander Velde, W. E.↗