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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 NTRS1987

Mission planning for autonomous systems

Planning is a necessary task for intelligent, adaptive systems operating independently of human controllers. A mission planning system that performs task planning by decomposing a high-level mission objective into subtasks and synthesizing a plan for those tasks at varying levels of abstraction is discussed. Researchers use a blackboard architecture to partition the search space and direct the focus of attention of the planner. Using advanced planning techniques, they can control plan synthesis for the complex planning tasks involved in mission planning.

Pearson, G.↗
DOE OSTI2021

Low-Cost Aero Technology Demonstrations

The main focus of this work was to demonstrate the use of polymeric additive manufacturing (AM) to create tooling for both preforming and consolidation. Polymeric tooling was utilized where both modest and higher pressures are used for part consolidation. The key focus for the AM tooling development was for fabrication of complex structures such as ducting, C-channel stiffened skins, and airfoils where conventional male tooling would typically be trapped in the cured part. The AM tooling was evaluated for use as a tool master used to fabricate and re-shape deformable/re-formable mandrels based on SpinTech’s shape memory composite technology known as Smart Tooling. The AM tooling was also evaluated for use as a mold for composite infusion and consolidation. Key performance parameters were tracked for project schedule completion with each step comprising of “art to part” cycle time, cost, and model fidelity for dimensions, performance, and cost. The primary focus of this demonstration was to determine if a 50% cost reduction was achievable, for each AM tooling-set, as compared to conventional processes. UDRI leveraged project partner SpinTech, who manufactures tools and parts in these categories and thus provided a baseline regarding current best practices and provided valuable feedback during the entirety of this demonstration. This demonstration primarily focused on the use of AM tooling for fabrication of three composite component structures which are typically utilized in aircraft and comprise salient geometric features of broad interest. These components are often tooling intensive and have features requiring extraction of male tools which are usually trapped by the geometry. The three structures selected by the team included: 1) A one-piece airfoil shell comprised of compound contours where male tooling would be trapped unless the part were manufactured in two halves as is typically the case. 2) A one-piece duct used for air handling, comprised of compound contours where male tooling would be trapped unless the part were manufactured in two halves, or a washout mandrel were to be used. 3) A co-cured C-channel stiffened skin where typically C-channels would be individually manufactured and then bonded to a cured skin. The demonstration was comprised of three main tasks: • Task 1: AM Tool Feasibility Study – ensure the AM tooling meets the performance requirements as specified by SpinTech to match baseline performance. • Task 2: Complex Tool Demonstration – Fabricate tooling, preforms, and parts representative of an airfoil and duct. • Task 3: Large Aerostructure Fabrication Demonstration – Fabricate tooling, preforms, and part representative of a C-channel stiffened skin. With the conclusion of this project, a decision tree was developed to determine the key considerations necessary to determine if use of AM tooling for the three selected structures was able to attain the same quality as historically achieved on metallic tooling, while providing a significant cost reduction.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗
NASA NTRS1987

Development of Moire machine vision

Three dimensional perception is essential to the development of versatile robotics systems in order to handle complex manufacturing tasks in future factories and in providing high accuracy measurements needed in flexible manufacturing and quality control. A program is described which will develop the potential of Moire techniques to provide this capability in vision systems and automated measurements, and demonstrate artificial intelligence (AI) techniques to take advantage of the strengths of Moire sensing. Moire techniques provide a means of optically manipulating the complex visual data in a three dimensional scene into a form which can be easily and quickly analyzed by computers. This type of optical data manipulation provides high productivity through integrated automation, producing a high quality product while reducing computer and mechanical manipulation requirements and thereby the cost and time of production. This nondestructive evaluation is developed to be able to make full field range measurement and three dimensional scene analysis.

Harding, Kevin G.↗
DOE OSTI2023

Editorial: Edge computation and digital distribution networks

The development of digital technologies is penetrating all areas of energy revolution. Based on the in-depth integration of advanced digital technologies, distribution networks are gradually transforming into digital distribution networks (DDNs) with tremendous changes from the structure to the operation mode. DDNs is the digitalized appearance of the physical distribution network, in which ubiquitous connections and massive data are the basic characteristics (Huo et al., 2022). It is an important task to utilize the massive data and propose novel operation modes to construct more efficient and intelligent distribution networks (Jian et al., 2022). Among the advanced digital technologies in DDNs, edge computing has received wide attention (Zhao et al., 2022). It has superior performance in local sensing and intelligent computation, which can effectively relieve huge communication pressure. However, the limited computing resources and the complex computing tasks at the edge side significantly challenge the collaboration of distribution network regulation and advanced digital technologies (Hu et al., 2022). It is necessary to find out proper methods to utilize advanced digital technology to construct DDNs. This Research Topic is organized to introduce the recent progress in the construction, operation and advanced computational methods for DDNs. Finally, seven papers have been accepted, which can be sorted into the following three categories: 1) Evolution and technical features of DDNs, 2) Intelligent operation control of DDNs, 3) Advanced simulation for large-scale DDNs. The three sections below respectively introduce the major research and contributions of the papers covered in each category.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

How Autonomous Intelligent Systems Can Facilitate Earth-independent Medical Care: Going Beyond Telepresence

During the last decade, teleoperated robotic systems have extended humans’ sensorimotor competence to digitally fly beyond the physical barrier of distance and scale and thus transmit sensorimotor skills of the human through direct communication. Telepresence capabilities have enabled tele-physical remote access at small scales thanks to telerobotic mediums. Although the concept was initially motivated by space applications, such technologies quickly have expanded into the medical domain and resulted in teleoperated medical robots, including telerobotic surgical systems (such as the da Vinci surgical system). Effective telepresence fundamentally depends on an agile, reliable, and secure communication medium that can transmit real-time information between the operator and a remote device. However, direct telepresence may not be achievable for long-duration exploration spaceflight missions. Thus, autonomous systems and local intelligence represent potential solutions to the aforementioned issues. One example solution employs demonstration systems which enable learning from the pre-captured inputs of a skilled human operator. These will be computationally modeled and later probabilistically replicated toward the completion of remote physical tasks when direct telepresence is not viable - such as under communication blackout conditions. In other words, trained autonomous systems (e.g., robots) can perform remote operations that mimic the physical performance of experts during remote operations/training. Beyond learning the physics of the task, autonomous agents can also be used to conduct algorithmic decision-making that mimics the higher-level cognition of the expert. Thus, using an autonomous system, pre-trained cognitive and manipulation-based skills can be leveraged (acquired during pre-mission events) to produce digital twins of an intelligent operator. Such systems can be used for the real-time conduction of intricate tasks in complex and unstructured environments. Such systems will operationalize “cognitive digital twins” and can expand the reach of human cognition and manipulation through the power of data-driven learning from demonstration algorithms. This system category will be discussed as a fully autonomous operation in this talk. In addition to the above, we will also propose and discuss the possibility of partial-automation using remote intelligence and remote sensing. In contrast to full automation, partial automation can close the loop through a local operator equipped with augmented sensory awareness through wearable systems. Such technologies will allow the local operator to conduct delicate tasks while being guided using sensory augmentation and being monitored to gauge her/his level of cognitive focus and performance. The difference with the previous category is that a remote human will conduct the task. Further, rather than making a digital twin of human cognition, we will augment the control inputs of the local human to match those of the skilled expert operator who is not accessible in real-time. Going beyond classic telepresence and thus approaching intelligent telepresence, our vision is that autonomous agents will eventually enable the safe, consistent and efficient delivery of complex, remote and smart medical care during space exploration across operators in an Earth-independent fashion. We will discuss our collective vision from NASA and MERIIT@NYU lab in this talk.

Telepresence↗
NASA NTRS1994

Design of a cooperative problem-solving system for en-route flight planning: An empirical evaluation

Both optimization techniques and expert systems technologies are popular approaches for developing tools to assist in complex problem-solving tasks. Because of the underlying complexity of many such tasks, however, the models of the world implicitly or explicitly embedded in such tools are often incomplete and the problem-solving methods fallible. The result can be 'brittleness' in situations that were not anticipated by the system designers. To deal with this weakness, it has been suggested that 'cooperative' rather than 'automated' problem-solving systems be designed. Such cooperative systems are proposed to explicitly enhance the collaboration of the person (or a group of people) and the computer system. This study evaluates the impact of alternative design concepts on the performance of 30 airline pilots interacting with such a cooperative system designed to support en-route flight planning. The results clearly demonstrate that different system design concepts can strongly influence the cognitive processes and resultant performances of users. Based on think-aloud protocols, cognitive models are proposed to account for how features of the computer system interacted with specific types of scenarios to influence exploration and decision making by the pilots. The results are then used to develop recommendations for guiding the design of cooperative systems.

Layton, Charles↗
NASA NTRS1994

Design of a cooperative problem-solving system for en-route flight planning: An empirical evaluation

Both optimization techniques and expert systems technologies are popular approaches for developing tools to assist in complex problem-solving tasks. Because of the underlying complexity of many such tasks, however, the models of the world implicitly or explicitly embedded in such tools are often incomplete and the problem-solving methods fallible. The result can be 'brittleness' in situations that were not anticipated by the system designers. To deal with this weakness, it has been suggested that 'cooperative' rather than 'automated' problem-solving systems be designed. Such cooperative systems are proposed to explicitly enhance the collaboration of the person (or a group of people) and the computer system. This study evaluates the impact of alternative design concepts on the performance of 30 airline pilots interacting with such a cooperative system designed to support enroute flight planning. The results clearly demonstrate that different system design concepts can strongly influence the cognitive processes and resultant performances of users. Based on think-aloud protocols, cognitive models are proposed to account for how features of the computer system interacted with specific types of scenarios to influence exploration and decision making by the pilots. The results are then used to develop recommendations for guiding the design of cooperative systems.

Layton, Charles↗
DOE OSTI2024

Coincident learning for unsupervised anomaly detection of scientific instruments

Abstract Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, F ^ β , out of analogy to the supervised classification F β statistic. CoAD uses F ^ β to train an anomaly detection algorithm on unlabeled data , based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.

43 PARTICLE ACCELERATORS↗
NASA NTRS2009

Complex Event Recognition Architecture

Complex Event Recognition Architecture (CERA) is the name of a computational architecture, and software that implements the architecture, for recognizing complex event patterns that may be spread across multiple streams of input data. One of the main components of CERA is an intuitive event pattern language that simplifies what would otherwise be the complex, difficult tasks of creating logical descriptions of combinations of temporal events and defining rules for combining information from different sources over time. In this language, recognition patterns are defined in simple, declarative statements that combine point events from given input streams with those from other streams, using conjunction, disjunction, and negation. Patterns can be built on one another recursively to describe very rich, temporally extended combinations of events. Thereafter, a run-time matching algorithm in CERA efficiently matches these patterns against input data and signals when patterns are recognized. CERA can be used to monitor complex systems and to signal operators or initiate corrective actions when anomalous conditions are recognized. CERA can be run as a stand-alone monitoring system, or it can be integrated into a larger system to automatically trigger responses to changing environments or problematic situations.

Fitzgerald, William A.↗
DOE OSTI2020

Nuclear Decontamination Evolution and Revolution - 20349

Decontamination, one of the oldest practices in the nuclear industry, is about to change. While no single approach to decontamination is appropriate in all situations, decontamination of highly contaminated surfaces is historically viewed as difficult and/or marginally effective and fixatives and strippable coatings are often called on to manage time sensitive contamination control issues. The deferral of proper decontamination can lead to accumulated concentrations of radioactive material increasing the risk of cross-contamination of workers and equipment while also increasing radiation levels, which further discourages decontamination. The results of the subject testing demonstrate that superior decontamination factors are readily achievable and the ALARA benefit of decontamination is viable through advanced decontamination technologies in conjunction with innovative application techniques. The collateral benefits will save countless radiological man-hours and personnel radiological exposure. The appropriate use of radiological decontamination techniques can prevent or limit the adverse effects of highly radioactive contamination in the work area. High levels of radiological contamination are typically associated with some of the most physically demanding work in the nuclear industry. Additionally, working in highly contaminated environments increases the risk of exposure to elevated levels of airborne radioactivity and radiation from the source term of the contamination, particularly in hard to reach areas or complex equipment. Veolia's Alaron Nuclear Services (Alaron), a fully integrated nuclear facility, has provided the nuclear industry waste treatment, consolidation, repackaging, and broad decontamination services for almost 35 years. In looking at solutions for its customers, as well as for their own facility, Alaron has recently collaborated with Environmental Alternatives, Inc. (EAI), which has provided innovative solutions addressing difficult nuclear decontamination and industrial cleaning challenges since 1989. This broad range of experience along with the products to support the work created an ideal partner for Alaron's needs at their facility. The benefits of collaboration were immediately recognizable to the management of both companies although the decontamination challenge was significant. Due to the nature of the services offered, contamination at the Alaron facility is routine. The recent decontamination experience at Alaron utilized an innovative surfactant process that quickly produces remarkably higher decontamination factors for both removable surface contamination and fixed contamination. Considering the time to decontaminate areas historically designated for high radiological hazard work, the results from this decontamination effort not only reduced contamination levels but also significantly lowered exposure rates in the working environment. The relative ease with which the surfactant is applied, combined with extraordinary decontamination test results, indicates the potential to alter current radiological work processes in a way that improves worker comfort, and removes radiological engineering barriers, allowing Alaron to complete complex radiological tasks more efficiently and effectively. In a series of two simple applications of the decontamination agent to contaminated high bay vertical surfaces, as well as a variety of horizontal and vertical surfaces with variable porousness and surface sealants, there was an overall reduction in removable contamination of approximately 73% with a reduction in area dose of approximately 93%. The results indicate additional reduction in fixed contamination with application of the surfactant. These values are much higher than experienced with more traditional decontamination agents. In conjunction with demonstrating EAI's surfactant on room surfaces, additional studies were conducted on complex geometry equipment including waste processing equipment, tools, and heavily contaminated parts normally handled from Alaron's customers. Evaluations were made with straight application of EAI's surfactant as well as adding additional techniques to the treatment which afforded revolutionary improvements in the levels of contamination removed. Successful decontamination with up to 95% reduction in removable contamination with just one application were demonstrated in several of the trials. This paper outlines the planned approach to use this technology, the variety of surfaces and equipment treated, and the results of the decontamination efforts. Furthermore, this paper discusses options for pretreatment of certain heavily contaminated equipment prior to employees handling and/or working with the equipment. The potential for dose saving and the reduced risk of cross-contamination with the added benefit of lower PPE requirements, produces an enormous potential for cost and time savings. Attendees will benefit from Alaron's experiences and more fully understand the capabilities of EAI's decontamination process. With the variety of contamination agents, forms and surfaces on which the technology was demonstrated, the information will be valuable to a broad cross-section of industry users. The significance of this report demonstrates superior decontamination factors are achievable utilizing the next evolution of decontamination technology and that the ALARA benefits of decontamination are available through advanced and modern decontamination efforts. The collateral benefits will save countless radiological man-hours and valuable personnel radiological exposure. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Immersive Technologies for Human-in-the-Loop Lunar Surface Simulations

NASA, the National Aeronautics and Space Administration, continually seeks innovative solutions to enhance its operations, particularly in the realms of testing, evaluation, and training for future missions. Immersive technologies, such as virtual, augmented, and mixed reality have proven to be powerful tools for realistic, interactive, and engaging environments. This paper explores how the Simulation and Graphics Branch at NASA’s Johnson Space Center (JSC) leverages immersive technology, modern commercial rendering engines, and physics-based systems simulations to develop human-in-the-loop systems for humanity’s return to the Moon through the Artemis program. When NASA returns to the Moon, astronauts will travel to the Moon’s South Pole where lighting conditions will cause a more complex operational environment. Human-in-the-loop testing plays a crucial role in NASA's mission planning, spacecraft and space systems development, and evaluation of operational scenarios. The development of immersive environments such as a lunar rover mockup at a video wall enables engineers and astronauts to simulate and experience mission scenarios, integrated spacecraft systems, and operational procedures in a relevant environment before deployment. By integrating realistic virtual environments, immersive technology allows for the visualization and interaction with virtual spacecraft models, mission landscapes, and complex operational tasks. This approach helps identify potential design flaws, operational challenges, and safety considerations. It also provides valuable insights for risk reduction and helps improve mission efficiency and effectiveness. With advanced motion tracking systems and custom virtual environments data can be gathered and evaluated to help NASA refine training protocols, develop specialized training procedures and optimize human-robotic interactions for future space missions. Furthermore, immersive technology offers opportunities for future training initiatives at NASA. The Virtual Reality Laboratory at JSC has pioneered training with Virtual Reality (VR) since the Hubble Space Telescope repair missions in the early 1990’s. Extended Reality (XR) simulations enable astronauts to rehearse complex spacewalks, spacecraft maneuvers, and extravehicular activities in a safe and controlled environment. By replicating the physical and cognitive challenges of space missions, immersive training experiences enhance astronauts' situational awareness, decision-making abilities, and adaptability to unexpected scenarios. Additionally, immersive technology facilitates collaborative training, allowing geographically dispersed crew and mission control personnel to engage in synchronized simulations, fostering teamwork and effective communication. The adoption of immersive technology in NASA's testing, evaluation, and future training programs has yielded significant benefits. By incorporating human-in-the-loop testing for studies involving Extra Vehicular Activities (EVA), surface mobility and landing systems, NASA can identify and mitigate risks, optimize operational procedures, and enhance mission success. Ultimately, immersive training experiences can empower astronauts to better navigate the complexities of space missions, ensuring their safety, productivity, and success in the dynamic and challenging environments they will experience at the Lunar South Pole.

Simulation Modeling Virtual Reality Immersive Tech↗
NASA NTRSDate not supplied

Immersive Technologies for Human-in-the-Loop Lunar Surface Simulations

NASA, the National Aeronautics and Space Administration, continually seeks innovative solutions to enhance its operations, particularly in the realms of testing, evaluation, and training for future missions. Immersive technologies, such as virtual, augmented, and mixed reality have proven to be powerful tools for immersing users in realistic, interactive, and engaging environments. This paper explores how the Simulation and Graphics Branch at NASA’s Johnson Space Center (JSC) leverages immersive technology, modern commercial rendering engines, and physics-based systems simulations to develop human-in-the-loop systems for humanity’s return to the Moon through the Artemis program. When NASA returns to the Moon, astronauts will travel to the Moon’s South Pole where lighting conditions will cause a more complex operational environment. Human-in-the-loop simulations play a crucial role in NASA’s mission planning, spacecraft and space systems development, and evaluation of operational scenarios. The development of immersive environments such as a lunar rover mockup at a video wall enables engineers and astronauts to simulate and experience mission scenarios, integrated spacecraft systems, and operational procedures in a relevant environment before deployment. By integrating realistic virtual environments, immersive technology allows for the visualization and interaction with virtual spacecraft models, mission landscapes, and complex operational tasks. This approach helps identify potential design flaws, operational challenges, and safety considerations. It also provides valuable insights for risk reduction and helps improve mission efficiency and effectiveness. With advanced motion tracking systems and custom virtual environments, data can be gathered and evaluated to help NASA refine training protocols, develop specialized training procedures, and optimize human-robotic interactions for future space missions. Furthermore, immersive technology offers opportunities for future training initiatives at NASA. The Virtual Reality Laboratory at JSC has pioneered training with Virtual Reality (VR) since the Hubble Space Telescope repair missions in the early 1990’s. Extended Reality (XR) simulations enable astronauts to rehearse complex spacewalks, spacecraft maneuvers, and extravehicular activities in a safe and controlled environment. By replicating the physical and cognitive challenges of space missions, immersive training experiences enhance astronauts’ situational awareness, decision-making abilities, and adaptability to unexpected scenarios. Additionally, immersive technology facilitates collaborative training, allowing geographically dispersed crew and mission control personnel to engage in synchronized simulations, fostering teamwork and effective communication. The adoption of immersive technology in NASA’s testing, evaluation, and future training programs has yielded significant benefits. By incorporating human-in-the-loop simulations for studies involving Extra Vehicular Activities (EVA), surface mobility and landing systems, NASA can identify and mitigate risks, optimize operational procedures, and enhance mission success. Ultimately, immersive simulation experiences can empower astronauts to better navigate the complexities of space missions, ensuring their safety, productivity, and success in the dynamic and challenging environments they will experience at the Lunar South Pole.

HITL↗
NASA NTRSDate not supplied

What is the Role of Usability and Trust in Autonomy?

Usability encompasses learnability, efficiency, memorability, effectiveness, and satisfaction. NASA’s standards for usability acceptance criteria focus on interfaces that help operators achieve their tasks efficiently, effectively, and with satisfaction. However, discussions on usability, especially regarding future highly automated and autonomous systems, rarely include trust. As NASA plans for long-duration exploration missions, it envisions astronauts operating more independently from Mission Control on Earth. This independence will drive the development of these highly automated and autonomous systems that astronauts will use daily. To prepare for this future, our team has developed a scheduling and execution software tool that facilitates self-scheduling, allowing astronauts to independently manage their own schedule without Mission Control’s involvement. Over many years, we have developed, matured, and evaluated our software tool in extreme environments, prioritizing user-centered design and high usability. These evaluations have included multiple campaigns in NASA analogs, including NEEMO, BASALT, and HERA, as well as technology demonstrations onboard the International Space Station. Our recent research on software interfaces for future astronaut autonomy revealed a strong correlation between usability and trust measures. In a controlled lab experiment, we asked novice users to perform a complex scheduling task, during which the software immediately validated the schedule’s constraints and checked for violations. We collected usability (User Experience Questionnaire, UEQ) and trust (Trust in Automated Systems scale, TAS) measures; significant, strong, and moderate correlations emerged between several of the UEQ metrics and TAS. These results support the argument for investing in usability early to enable and sustain trust in highly automated and autonomous systems.

usability↗
NASA NTRS2011

Countermeasures to Enhance Sensorimotor Adaptability

During exploration-class missions, sensorimotor disturbances may lead to disruption in the ability to ambulate and perform functional tasks during the initial introduction to a novel gravitational environment following a landing on a planetary surface. The goal of our current project is to develop a sensorimotor adaptability (SA) training program to facilitate rapid adaptation to novel gravitational environments. We have developed a unique training system comprised of a treadmill placed on a motion-base facing a virtual visual scene that provides an unstable walking surface combined with incongruent visual flow designed to enhance sensorimotor adaptability. We have conducted a series of studies that have shown: Training using a combination of modified visual flow and support surface motion during treadmill walking enhances locomotor adaptability to a novel sensorimotor environment. Trained individuals become more proficient at performing multiple competing tasks while walking during adaptation to novel discordant sensorimotor conditions. Trained subjects can retain their increased level of adaptability over a six months period. SA training is effective in producing increased adaptability in a more complex over-ground ambulatory task on an obstacle course. This confirms that for a complex task like walking, treadmill training contains enough of the critical features of overground walking to be an effective training modality. The structure of individual training sessions can be optimized to promote fast/strategic motor learning. Training sessions that each contain short-duration exposures to multiple perturbation stimuli allows subjects to acquire a greater ability to rapidly reorganize appropriate response strategies when encountering a novel sensory environment. Individual sensory biases (i.e. increased visual dependency) can predict adaptive responses to novel sensory environments suggesting that customized training prescriptions can be developed to enhance adaptability. These results indicate that SA training techniques can be added to existing treadmill exercise equipment and procedures to produce a single integrated countermeasure system to improve performance of astro/cosmonauts during prolonged exploratory space missions.

Bloomberg, J. J.↗
NASA NTRS2017

Reinventing Image Detective: An Evidence-Based Approach to Citizen Science Online

Usability studies demonstrate that web users are notoriously impatient, spending as little as 15 seconds on a home page. How do you get users to stay long enough to understand a citizen science project? How do you get users to complete complex citizen science tasks online? Image Detective, a citizen science project originally developed by scientists and science engagement specialists at the NASA Johnson Space center to engage the public in the analysis of images taken from space by astronauts to help enhance NASA's online database of astronaut imagery, partnered with the CosmoQuest citizen science platform to modernize, offering new and improved options for participation in Image Detective. The challenge: to create a web interface that builds users' skills and knowledge, creating engagement while learning complex concepts essential to the accurate completion of tasks. The project team turned to usability testing for an objective understanding of how users perceived Image Detective and the steps required to complete required tasks. A group of six users was recruited online for unmoderated and initial testing. The users followed a think-aloud protocol while attempting tasks, and were recorded on video and audio. The usability test examined users' perception of four broad areas: the purpose of and context for Image Detective; the steps required to successfully complete the analysis (differentiating images of Earth's surface from those showing outer space and identifying common surface features); locating the image center point on a map of Earth; and finally, naming geographic locations or natural events seen in the image. Usability test findings demonstrated that the following best practices can increase participation in Image Detective and can be applied to the successful implementation of any citizen science project: (1) Concise explanation of the project, its context, and its purpose; (2) Including a mention of the funding agency (in this case, NASA); (3) A preview of the specific tasks required of participants; (4) A dedicated user interface for the actual citizen science interaction. In addition, testing revealed that users may require additional context when a task is complex, difficult, or unusual (locating a specific image and its center point on a map of Earth). Video evidence will be made available with this presentation.

Romano, Cia↗
DOE OSTI2021

Tunable Stochasticity in an Artificial Spin Network

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. Furthermore, this type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

Artificial spin network↗