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NASA NTRS2016

Utilizing the ISS Mission as a Testbed to Develop Cognitive Communications Systems

The ISS provides an excellent opportunity for pioneering artificial intelligence software to meet the challenges of real-time communications (comm) link management. This opportunity empowers the ISS Program to forge a testbed for developing cognitive communications systems for the benefit of the ISS mission, manned Low Earth Orbit (LEO) science programs and future planetary exploration programs. In November, 1998, the Flight Operations Directorate (FOD) started the ISS Antenna Manager (IAM) project to develop a single processor supporting multiple comm satellite tracking for two different antenna systems. Further, the processor was developed to be highly adaptable as it supported the ISS mission through all assembly stages. The ISS mission mandated communications specialists with complete knowledge of when the ISS was about to lose or gain comm link service. The current specialty mandated cognizance of large sun-tracking solar arrays and thermal management panels in addition to the highly-dynamic satellite service schedules and rise/set tables. This mission requirement makes the ISS the ideal communications management analogue for future LEO space station and long-duration planetary exploration missions. Future missions, with their precision-pointed, dynamic, laser-based comm links, require complete autonomy for managing high-data rate communications systems. Development of cognitive communications management systems that permit any crew member or payload science specialist, regardless of experience level, to control communications is one of the greater benefits the ISS can offer new space exploration programs. The IAM project met a new mission requirement never previously levied against US space-born communications systems management: process and display the orientation of large solar arrays and thermal control panels based on real-time joint angle telemetry. However, IAM leaves the actual communications availability assessment to human judgement, which introduces unwanted variability because each specialist has a different core of experience with comm link performance. Because the ISS utilizes two different frequency bands, dynamic structure can be occasionally translucent at one frequency while it can completely interdict service at the other frequency. The impact of articulating structure on the comm link can depend on its orientation at the time it impinges on the link. It can become easy for a human specialist to cross-associate experience at one frequency with experience at the other frequency. Additionally, the specialist's experience is incremental, occurring one nine-hour shift at a time. Only the IAM processor experiences the complete 24x7x365 communications link performance for both communications links but, it has no "learning capability." If the IAM processor could be endowed with a cognitive ability to remember past structure-induced comm link outages, based on its knowledge of the ISS position, attitude, communications gear, array joint angles and tracking accuracy, it could convey such experience to the human operator. It could also use its learned communications link behaviors to accurately convey the availability of future communications sessions. Further, the tool could remember how accurately or inaccurately it predicted availability and correct future predictions based on past performance. The IAM tool could learn frequency-specific impacts due to spacecraft structures and pass that information along as "experience." Such development would provide a single artificial intelligence processor that could provide two different experience bases. If it also "knew" the satellite service schedule, it could distinguish structure blockage from schedule or planet blockage and then quickly switch to another satellite. Alternatively, just as a human operator could judge, a cognizant comm system based on the IAM model could "know" that the blockage is not going to last very long and continue tracking a comm satellite, waiting for it to track away from structure. Ultimately, once this capability was fully developed and tested in the Mission Control Center, it could be transferred on-orbit to support development of operations concepts that include more advanced cognitive communications systems. Future applications of this capability are easily foreseen because even more dynamic satellite constellations with more nodes and greater capability are coming. Currently, the ISS fully employs its high-data-rate return link for harvesting payload science. In the coming months, it will double that data rate and is forecast to fully utilize that capability. Already there is talk of an upgrade that quadruples the current data rate allocated to ISS payload science before the end of its mission and laser comm links have already been tested from the ISS. Every data rate upgrade mandates more complicated and sensitive communications equipment which implies greater expertise invested in the human operator. Future on-orbit cognizant comm systems will be needed to meet greater performance demands aboard larger, far more complicated spacecraft. In the LEO environment, the old-style one-satellite-per-spacecraft operations concept will give way to a new concept of a single customer spacecraft simultaneously using multiple comm satellites. Much more highly-dynamic manned LEO missions with decades of crew members potentially increase the demand for communications link performance. A cognizant on-board communications system will meet advanced communications demands from future LEO missions and future planetary missions. The ISS has fledgling components of future exploration programs, both LEO and planetary. Further, the Flight Operations Directorate, through the IAM project, has already begun to develop a communications management system that attempts to solve advanced problems ideally represented by dynamic structure impacting scheduled satellite service. With an earnest project to integrate artificial intelligence into the IAM processor, the ISS Program could develop a cognizant communications system that could be adapted and transferred to future on-orbit avionics designs.

Jackson, Dan↗

NASA NTRS2016

Position Paper: Designing Complex Systems to Support Interdisciplinary Cognitive Work

The paper argues that the field we can call cognitive science of interdisciplinary collaboration is an important area of study for improving design of Large-Scale Complex Systems (LaCES) and supporting cognitive work. The paper mostly raised questions that have been documented in earlier qualitative analysis studies, and provided possible avenues of exploration for addressing them. There are likely further contributions from additional disciplines beyond those mentioned in this paper that should be considered and integrated into such a cognitive science framework. Knowledge and awareness of various perspectives will help to inform the types of interventions available for improving LaCES design and functionality. For example, a cognitive interpretation of interdisciplinary collaborations in LaCES elucidated the need for a "translator" or "mediator" in helping subject matter experts to transcend language boundaries, mitigate single discipline bias, support integrative activities, and correct misaligned objectives. Additional research in this direction is likely to uncover similar gaps and opportunities for improvements in practice.

Greene, Melissa T.↗

NASA NTRS2018

Validation and Norms of Cognition and Spaceflight Assessments with Senior Military Leaders

Spaceflight hazards include risks to cognitive performance. NASA requires sensitive neuropsychological screening tools to determine changes to cognitive risks to crew health and performance. Currently, NASA uses WinSCAT (Spaceflight Cognitive Assessment Tool for Windows) for neurocognitive screening and to monitor the status of astronauts during spaceflight. The CognitionTest Battery was recently developed by NASA-funded researchers at UPenn as part of the Behavioral Health and Performance component of NASA Standard Measures. This research provides independently validated norms and psychometric properties to compare WinSCAT to Cognition using a military population who operates in high demand, extreme environments similar to those experienced by astronauts (deployments, isolation, extreme environments).

Williams, T. J.↗

NASA NTRS2019

Cognitive Communications and Networking Technology Infusion Study Report

As the envisioned next-generation SCaN Network transitions into an end-to-end “system of systems” with new enabling capabilities, it is anticipated that the introduction of machine learning, artificial intelligence, and other cognitive strategies into the network infrastructure will result in increased mission science return, improved resource efficiencies, and increased autonomy and reliability. This enhanced set of cognitive capabilities will be implemented via a “space cloud” concept to achieve a service-oriented architecture with distributed cognition, de-centralized routing, and shared, on-orbit data processing. The enabling cognitive communications and networking capabilities that may facilitate the desired network enhancements are identified in this document, and the associated enablers of these capabilities, such as technologies and standards, are described in detail.

Knoblock, Eric J.↗

NASA NTRSDate not supplied

Effect of Carbon Dioxide Exposure on Physical and Cognitive Performance in A Simulated Spaceflight Contingency Scenario

INTRODUCTION: Carbon dioxide (CO2) produced by astronauts inside space suits can accumulate to levels that may affect health and performance. The human health and performance risks associated with different levels of CO2 exposure in the flight environment remain an area of debate. The purpose of this study is to characterize the limit of acceptable performance (cognitive and physical) decrements and symptom severity for mission operations when subjected to elevated inspired CO2 levels in simulated contingency lunar extravehicular activity (EVA) scenarios. METHODS: This study will create a simulation of a 1-hour, ~2km contingency EVA walk back to a habitat, incorporating a passive treadmill and a fully immersive virtual reality (VR) simulation of a lunar EVA environment. Subjects will be asked to walk on a treadmill while breathing partial pressures of CO2 of 0, 5, 10, 15, 20, 25, 30mmHg for 1 hour at a time. Cognitive performance will be quantified using validated cognitive tests and measures of functional task performance embedded within the VR environment. Test subject symptoms and self-assessment of performance will be evaluated via survey. RESULTS: This study currently has approval from NASA’s Institutional Review Board and has completed the Test Readiness Review process. Next steps for this research study include test subject recruitment, data collection, and analysis. Data collection is planned for calendar year 2023 and 2024. DISCUSSION: This study will provide valuable information regarding how various partial pressures of CO2 exposure impact acute health, as well as cognitive and physical performance during simulated contingency lunar EVA. This information will enable assessment of health and performance risk associated with spacesuit systems and operations concepts for future exploration missions as well as informing definition of future standards and requirements.

D. M. Nusbaum↗

NASA NTRSDate not supplied

The Development and Application of Cognitive Performance Assessment for Exploration Class Mission EVAs

The objectives of our research are to: 1) Define cognitive performance requirements for future Lunar and Mars mission EVAs, 2) Characterize cognitive performance in subjects conducting simulated EVAs in analog studies, and 3) Develop and test unobtrusive EVA cognitive performance metrics. Our long-term goal is to develop exploration class cognitive performance monitoring capabilities for EVA and to provide data that can inform EVA planning for Lunar and Mars missions.

S R Anderson↗

NASA NTRS2024

Assessments of Physiology and Cognition in Hybrid-Reality Environments (APACHE) – A VR “Sandbox” for Planetary Spacewalk Research

NASA is planning to return to the Moon in the mid-2020s as a steppingstone to Mars missions in the 2030s. Spacewalks, or extravehicular activities (EVAs), performed on the Moon and Mars will differ in a variety of ways from those that have been performed in decades past. NASA has identified multiple risks to human health and performance associated with a crewed mission to Mars, especially those associated with exploration EVAs which are expected to be a primary mission activity. Crew may be expected to conduct up to 24 hours of EVA per person per week, where the likelihood of injury and/or mental mistakes are increased compared to ground-based training or current microgravity EVAs and the consequences of which can be catastrophic. Current test environments for exploration EVA research and technology development are large, costly facilities that are limited in their availability or capabilities. Spacesuit testing in a reduced gravity environment such as NASA’s Neutral Buoyancy Laboratory, while a good representation of the crew’s physical workload during exploration EVAs, typically has small datasets and is difficult to integrate physiological sensors or other types of crew performance measures. Meanwhile, scientific field-based testing such as NASA’s Desert Research and Technology Studies offers an operationally relevant environment for exploration EVAs, particularly for cognitive workload, but is also limited by small datasets, lack of a pressurized spacesuit, and obtrusive measures. The limitations of current analogs for exploration EVAs identify a need for a new test environment that can approximate both the physical and cognitive demands associated with exploration EVAs to enable rapid, controlled, and repeatable evaluations of human health and performance risks of exploration missions. In response, the Human Physiology, Performance, Protection, and Operations Laboratory (H-3PO) at NASA Johnson Space Center has developed a hybrid reality exploration EVA analog named the Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE)[ to address these limitations using a combination of virtual, physical, and hybrid reality techniques. The APACHE facility resides at NASA Johnson Space Center and serves as a large “sandbox” for EVA research and simulation. At its center is a roughly 15x20ft space surrounded by a 14” tall sandbox partially filled with lunar regolith simulant to emulate the physical feeling of walking on a planetary surface and to allow for simulated geology operations. Nearby, a curved passive treadmill (Skillmill Connect, Technogym, Fairfield, NJ) and an omnidirectional treadmill (Infinadeck, Infinadeck, Rocklin, CA) are included to enable exploration of these large virtual environments while also imposing the physical demands, representative timelines, and cognitive burdens required to navigate and traverse these distances during exploration EVA. A 6DOF motion platform is used to simulate rover operations and supports various human performance evaluations and associated risks. Lastly, APACHE can support two extravehicular (EV) crewmembers working in tandem. A computer workstation is located nearby and also supports an intravehicular (IV) crewmember as part of a full mission simulation. The IV crewmember has direct video and audio communication with the EV crew in VR to provide operational and procedural support.

Human Performance↗

NASA NTRS2025

Time Course of Effects of Carbon Dioxide Exposure on Physical and Cognitive Performance in a Simulated Surface Extravehicular Activity Contingency Scenario

INTRODUCTION Carbon dioxide (CO 2 ) is a metabolic byproduct produced by humans that must be scrubbed from the atmosphere by environmental control and life support systems in space vehicles and spacesuits to prevent buildup. Current nominal CO 2 limits are set by NASA Standard 3001, however, limits during contingency situations are not specified. The purpose of this study was to characterize and quantify the physiological, cognitive, and self-assessed symptom and performance metrics as a function of inspired CO 2 (P I CO 2 ) during a simulated 1-hour contingency lunar EVA scenario in virtual reality (VR). METHODS Fifteen healthy subjects completed 7 simulated EVA scenarios walking on a passive treadmill for one hour breathing dry, ambient air mixed with added CO 2 in a blind, counterbalanced manner (P I CO 2 : 0, 5, 10, 15, 20, 25, 30 mmHg) simulating a 2.0 km contingency EVA walkback to a habitat on the lunar surface in the event of a CO 2 scrubber malfunction. Subjects were immersed in a Lunar VR environment and asked to maintain their walking speed between 2.1 km/h and an upper limit individually calibrated to a fixed metabolic workload of 1.3 L/min of O 2 consumption, and performed periodic cognitive tasks and continuous cardiopulmonary measurements. Self-assessments of symptoms and task performance were measured via verbal Likert-scale survey at the onset of the walkback and at 10-minute intervals thereafter. RESULTS The time course of cardiopulmonary, cognitive, and self-reported responses at each level of P I CO 2 will be presented. All 15 subjects were able to complete the 1-hr walk back task at all CO 2 levels. Physiological responses were rapid, typically within 1 to 5 minutes. No effects of time were detected for cognitive measures. Symptomatic ratings of headache, shortness of breath, and fatigue demonstrated a time-course development, reaching asymptote after 40 minutes of CO 2 exposure. DISCUSSION These findings will inform the posture of risk associated with CO 2 exposure on human health and performance during microgravity and planetary surface operations, This will help inform decisions related to space suit and environmental life support hardware requirements and EVA operational limits for contingency extravehicular activity (EVA) scenarios.

carbon dioxide↗

NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗

NASA NTRS2004

Stress, Cognition, and Human Performance: A Literature Review and Conceptual Framework

The following literature review addresses the effects of various stressors on cognition. While attempting to be as inclusive as possible, the review focuses its examination on the relationships between cognitive appraisal, attention, memory, and stress as they relate to information processing and human performance. The review begins with an overview of constructs and theoretical perspectives followed by an examination of effects across attention, memory, perceptual-motor functions, judgment and decision making, putative stressors such as workload, thermals, noise, and fatigue and closes with a discussion of moderating variables and related topics. In summation of the review, a conceptual framework for cognitive process under stress has been assembled. As one might imagine, the research literature that addresses stress, theories governing its effects on human performance, and experimental evidence that supports these notions is large and diverse. In attempting to organize and synthesize this body of work, I was guided by several earlier efforts (Bourne & Yaroush, 2003; Driskell, Mullen, Johnson, Hughes, & Batchelor, 1992; Driskell & Salas, 1996; Haridcock & Desmond, 2001; Stokes & Kite, 1994). These authors should be credited with accomplishing the monumental task of providing focused reviews in this area and their collective efforts laid the foundation for this present review. Similarly, the format of this review has been designed in accordance with these previous exemplars. However, each of these previous efforts either simply reported general findings, without sufficient experimental illustration, or narrowed their scope of investigation to the extent that the breadth of such findings remained hidden from the reader. Moreover, none of these examinations yielded an architecture that adequately describes or explains the inter-relations between information processing elements under stress conditions.

Stress↗

NASA NTRS2015

Daytime Cognitive Performance in Response to Sunlight or Fluorescent Light Controlling for Sleep Duration

Light is the primary synchronizer of the human circadian rhythm and also has acute alerting effects. Our study involves and comparing the alertness, performance and sleep of participants in the NASA Ames Sustainability Base, which uses sunlight as its primary light source, to in a traditional office building which uses overhead florescent lighting and varying exposure to natural light. The purpose of this study is to determine whether the use of natural lighting as a primary light source improves daytime cognitive function and promotes nighttime sleep. Participants from the Sustainability Base will be matched by gender and age to individuals working in other NASA buildings. In a prior study we found no differences in performance between those working in the Sustainability Base and those working in other buildings. Unexpectedly, we found that the average sleep duration among participants in both buildings was short, which likely obscured our ability to detect a difference the effect of light exposure on alertness. Given that such sleep deprivation has negative effects on cognitive performance, in this iteration of the study we are asking the participants to maintain a regular schedule with eight hours in bed each night in order to control for the effect of self-selected sleep restriction. Over the course of one week, we will ask the participants to wear actiwatches continuously, complete a psychomotor vigilance task (PVT) and digit symbol substitution task (DSST) three times per day, and keep daily sleepwork diaries. We hope that this study will provide data to support the idea that natural lighting and green architectural design are optimal to enhance healthy nighttime sleep patterns and daytime cognitive performance.

cognitive performance↗

NASA NTRS2016

Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications

Previous research on cognitive radios has addressed the performance of various machine-learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different cross-layer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions.

software defined radio↗

NASA NTRS2017

Wideband Reconfigurable Harmonically Tuned GaN SSPA for Cognitive Radios

The paper presents the architecture of a wideband reconfigurable harmonically-tuned Gallium Nitrate (GaN) Solid State Power Amplifier (SSPA) for cognitive radios. When interfaced with the physical layer of a cognitive communication system, this amplifier topology offers broadband high efficiency through the use of multiple tuned input/output matching networks. This feature enables the cognitive radio to reconfigure the operating frequency without sacrificing efficiency. This paper additionally presents as a proof-of-concept the design, fabrication, and test results for a GaN inverse class-F type amplifier operating at X-band (8.4 GHz) that achieves a maximum output power of 5.14-W, Power Added Efficiency (PAE) of 38.6, and Drain Efficiency (DE) of 48.9 under continuous wave (CW) operation.

Cognitive↗

NASA NTRS2016

Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications

Previous research on cognitive radios has addressed the performance of various machine learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3:5 times for clear sky conditions and 6:8 times for rain conditions.

ionosphere↗

NASA NTRS2017

Implementation of a Space Communications Cognitive Engine

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).

reinforcement learning↗

NASA NTRS2017

Multiband Reconfigurable Harmonically Tuned Gallium Nitride (GaN) Solid-State Power Amplifier (SSPA) for Cognitive Radios

The paper presents the architecture of a wideband reconfigurable harmonically-tuned Gallium Nitride (GaN) Solid State Power Amplifier (SSPA) for cognitive radios. When interfaced with the physical layer of a cognitive communication system, this amplifier topology offers broadband high efficiency through the use of multiple tuned input/output matching networks. This feature enables the cognitive radio to reconfigure the operating frequency without sacrificing efficiency. This paper additionally presents as a proof-of-concept the design, fabrication, and test results for a GaN inverse Class-F type amplifier operating at X-band (8.4 GHz) that achieves a maximum output power of 5.14-W, Power Added Efficiency (PAE) of 38.6 percent, and Drain Efficiency (DE) of 48.9 percent under continuous wave (CW) operation.

Cognitive↗

NASA NTRS2017

Implementation of a Space Communications Cognitive Engine

reinforcement learning↗

NASA NTRSDate not supplied

HAT m:N Cognitive Task Analysis (CTA)

This Cognitive Task Analysis (CTA) study was designed to understand the capability of the m:N Tactical Operator (TO) interfaces developed by the Human-Autonomy Teaming Laboratory at NASA Ames to support operators responsible for simplified pilot operations of 100 independently operated small UAS (sUAS) in a constrained geographic area. The m:N sUAS TO interface includes a central Tactical Situation Display (TSD) digital map with moving icons reflecting the sUAS location and planned flight route. The interface also has two side panels. The left panel includes a tabular list of UAS assets and mission tasking, a list of recently viewed assets, and a list of events and alerts. The right panel includes a tabular list of UAS assets and their associated telemetry, text-based chat communication window, and a tabbed checklist window. This CTA was adapted from the incident-based applied cognitive task analysis (Militello & Hutton, 1998) and included demographics questions, scenario-based simulations, a task diagram and knowledge audit methods. In addition to examining the support provided by this m:N sUAS TO interface, this CTA study, conducted with aviation subject matter experts in analogous roles to the future tactical operator, was designed to illuminate and project likely cognitive requirements of the tactical operator. Interviewees participated in two scenario-based simulations using the m:N sUAS TO interfaces. In the first simulation, the interviewees supervised 12 sUAS operating in downtown San Diego, California transiting to and from a central sUAS Hive, restaurant locations, and customer drop off locations. Interviewees were asked to react to a UAS Volume Reservation (UVR) event with a two-phase impact on food delivery operations. In the second scenario, the interviewees supervised 100 sUAS operating in the same airspace and with the same mission. Interviewees used the interfaces to recognize and react to two sUAS air vehicle problems. After each scenario, we asked the interviewees a semi-structured list of questions to elicit their reflections about using the interfaces. Interviewees were confident in their ability to respond to two off-nominal situations in each simulation. Interviewees felt that, given high levels of automation on the sUAS, they would be able to manage the events without requiring additional support or handing off the sUAS to a colleague or supervisor in both the n=12 or n=100 sUAS settings. In the n=12 sUAS condition, interviewees used the center map to understand the asset location and progress along with mission tasking. An additional display window, Asset Telemetry, helped interviewees understand battery state and sUAS altitude. When the number of sUAS increased, interviewees altered their behavior. Rather than maintaining awareness of individual assets, interviewees appeared to become more reactive, managing exceptions. Interviewees reported that they spent less time looking at the nominal aircraft, and focused their attention primarily on the off-nominal aircraft. In addition, in the second simulation with n=100 sUAS, interviewees reported that they relied more on the side panels (Mission Timeline and Asset Telemetry) to gather information.

human-autonomy teaming↗