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At least 199 records · Page 11

National Institute for Rocket Propulsion Systems 1st Annual Workshop

The National Institute for Rocket Propulsion Systems (NIRPS) is a Government -wide initiative that seeks to ensure the resiliency of the Nation fs rocket propulsion community in order for the enterprise to remain vibrant and capable of providing reliable and affordable propulsion systems for the nation fs defense, civil and commercial needs. Recognizing that rocket propulsion is a multi-use technology that ensures the nation fs leadership in aerospace, the Government has a vested interest in maintaining this strategic capability through coordinated and synchronized acquisition programs and continual investments in research and development. NIRPS is a resource for collaboration and integration between all sectors of the U.S. propulsion enterprise, supporting policy development options, identifying technology requirements, and offering solutions that maximize national resources while ensuring that capability exists to meet future demand. NIRPS functions as a multi ]agency organization that our nation fs decision makers can look to for comprehensive information regarding all issues concerning the propulsion enterprise.

Doreswamy, Rajiv↗

Modeling and Simulation of Upset-Inducing Disturbances for Digital Systems in an Electromagnetic Reverberation Chamber

This report describes a modeling and simulation approach for disturbance patterns representative of the environment experienced by a digital system in an electromagnetic reverberation chamber. The disturbance is modeled by a multi-variate statistical distribution based on empirical observations. Extended versions of the Rejection Samping and Inverse Transform Sampling techniques are developed to generate multi-variate random samples of the disturbance. The results show that Inverse Transform Sampling returns samples with higher fidelity relative to the empirical distribution. This work is part of an ongoing effort to develop a resilience assessment methodology for complex safety-critical distributed systems.

Torres-Pomales, Wilfredo↗

AAM Supply Chain Working Group: Aerospace Supply Chain and Manufacturing

This presentation provides an overview of the aerospace supply chain and how the AAM Supply Chain Working Group will help to make it more sustainable and resilient. The urgent needs are building a tiered system, a modeling and simulation platform, and an electronic exchange platform.

AAM↗

Hyperspectral Detail Meets Radiometric Accuracy: A Foundational Reflected Solar Capability for the Coming Decade

Hyperspectral Earth-reflected solar measurements contain rich, detailed information about clouds, aerosols, and the atmosphere. This information compounds when paired with high, SI-traceable radiometric accuracy. Unlike spectral resolution, which can be resampled to coarser spectral resolutions, radiometric accuracy cannot be recovered once a measurement is made, making the choice to invest in this capability now a decision with lasting consequences for the climate record. CLARREO Pathfinder is demonstrating that this combination of hyperspectral sampling and benchmark-quality accuracy is achievable today, but no follow-on mission is currently planned within NASA's Earth Science Division portfolio, and the recent cancellation of the European Space Agency TRUTHS mission has heightened the urgency of sustaining this capability. We recommend prioritizing investment in a follow-on to CLARREO Pathfinder, along with dedicated support for the science community to fully exploit these measurements. This capability would reduce long-standing uncertainties in cloud feedback, aerosol forcing, and global energy imbalance, while providing a degradation-resistant, gap-resilient calibration foundation for the broader Earth observing system. Building on flight-proven technology and an engaged science community, this is a near-term, fiscally realistic opportunity to secure a foundational Earth observation capability for the coming decade.

satellite↗

Rising to the Challenge: Producing and Sustaining a Nutrient-Dense and Climate-Resilient Food Basket for All

Rapidly destabilizing climatic and other biophysical conditions, competition for natural resources, geopolitical conflicts, and market pressures will continue to impose constraints and shocks to the food system, the greatest impacts of which will be felt by nutritionally vulnerable populations. Creating a just, nutritious, and sustainable food system that feeds the world requires a paradigm shift on where, what, and how we grow food while ensuring equitable channels of distribution. Moving forward, we must generate new science, reorient policies, and invest in a suite of environmental, technological, and sociocultural transformations from farm to fork.

Food supply↗

Simulation and Modeling Concepts for Secure Airspace Operations

This paper examines cyber security vulnerabilities of Urban Air Mobility operations. With the expected advent of new entrants including Unmanned Aerial Systems, Commercial Launch Vehicles and Urban Air Mobility aircraft, the future United States National Airspace System will have to evolve to include their operations along with the current commercial, general aviation and military operations. The National Aeronautics and Space Administration and the Federal Aviation Administration are working together to provide a vision for aviation operations in the future—2045 and beyond. Their National Airspace System Horizons initiative seeks to provide stakeholders a list of operational scenarios and technologies, concepts and strategies needed for supporting that vision. They have identified cybersecurity as one of the seven strategic interest areas for realizing this vision. Consequently, NASA is studying cyber resiliency for secure airspace operations. While there are many pathways to attack a cyber physical system such as Urban Air Mobility, their effect is expressed in modification or corruption of data/information used for controlling vehicles and making operational decisions. The paper describes cybersecurity technologies of Encryption, Blockchain, Virtual Information Fabric Infrastructure, Trusted Platform Module and Anomaly Detection for protecting the data, and the cyber resiliency of the current and future air traffic management system.

Urban Air Mobility↗

Simulation and Modeling Concepts for Secure Airspace Operations

This paper examines cyber security vulnerabilities of Urban Air Mobility operations. With the expected advent of new entrants including Unmanned Aerial Systems, Commercial Launch Vehicles and Urban Air Mobility aircraft, the future United States National Airspace System will have to evolve to include their operations along with the current commercial, general aviation and military operations. The National Aeronautics and Space Administration and the Federal Aviation Administration are working together to provide a vision for aviation operations in the future—2045 and beyond. Their National Airspace System Horizons initiative seeks to provide stakeholders a list of operational scenarios and technologies, concepts and strategies needed for supporting that vision. They have identified cybersecurity as one of the seven strategic interest areas for realizing this vision. Consequently, NASA is studying cyber resiliency for secure airspace operations. While there are many pathways to attack a cyber physical system such as Urban Air Mobility, their effect is expressed in modification or corruption of data/information used for controlling vehicles and making operational decisions. The paper describes cybersecurity technologies of Encryption, Blockchain, Virtual Information Fabric Infrastructure, Trusted Platform Module and Anomaly Detection for protecting the data, and the cyber resiliency of the current and future air traffic management system.

Urban Air Mobility↗

Design and Implementation of a Scenario Development Process for a 2040 Trajectory-Based Operations Simulation

The National Aeronautics and Space Administration (NASA) is supporting research to transition from a legacy, air traffic management (ATM) system to Trajectory-Based Operations (TBO) environment targeting the 2035-2045 timeframe by creating simulation scenarios for current and future studies. TBO in the National Airspace System (NAS) focuses on modernizing the current operating paradigm to increase efficiency, predictability, resilience, and flexibility while migrating toward greater operational autonomy across the airspace. These simulation scenarios offer a wide dissemination and use of system-level constraints and aircraft state/intent data, routine use of data communications to request complex trajectory modifications and to receive clearances, and aircraft that are flying on 4-D trajectories with flexibility when desired, but structure where required. This report describes the end-to-end development of scenarios for the NASA Langley Research Center simulation environment to represent a 2040 Trajectory-Based Operations airspace for air transport operations research.

Trajectory-Based Operations↗

SafeMAP: Safe Multi-Agent Planning Framework Based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Mohammad Hejase↗

SafeMAP: Safe Multi-Agent Planning framework based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Mohammad Hejase↗

Investigation, Modeling, and Analysis of Integrated Metroplex Arrival and Departure Coordination Concepts

This work involves the development of a concept that enhances integrated metroplex arrival and departure coordination, determines the temporal (the use of time separation for aircraft sharing the same airspace resources) and spatial (the use of different routes or vertical profiles for aircraft streams at any given time) impact of metroplex traffic coordination within the National Airspace System (NAS), and quantifies the benefits of the most desirable metroplex traffic coordination concept. Researching and developing metroplex concepts is addressed in this work that broadly applies across the range of airspace and airport demand characteristics envisioned for NextGen metroplex operations. The objective of this work is to investigate, formulate, develop models, and analyze an operational concept that mitigates issues specific to the metroplex or that takes advantage of unique characteristics of metroplex airports to improve efficiencies. The concept is an innovative approach allowing the NAS to mitigate metroplex interdependencies between airports, optimize metroplex arrival and departure coordination among airports, maximize metroplex airport throughput, minimize delay due to airport runway configuration changes, increase resiliency to disruptions, and increase the tolerance of the system to degrade gracefully under adverse conditions such as weather, traffic management initiatives, and delays in general.

Clarke, John-Paul B.↗

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗

Localization of Ad-Hoc Lunar Constellations in Communication Failure Modes for Distributed Spacecraft Autonomy

As lunar missions increase in complexity inspired by NASA’s Artemis Program, they will require reliable and sufficient capability of the Position, Navigation, and Timing (PNT) system to support their scientific objectives. In addition, NASA's Commercial Lunar Payload Services (CLPS) program initiates the proliferation of public and private exploration partnerships using small satellites from commercial and private organizations, expanding traditionally confined low Earth orbit to be used for missions beyond geosynchronous orbit (Zucherman et al., 2022). Therefore, the Lunar PNT system is also required to provide navigation services compatible with the smaller platforms being sent by the public and private sectors, like CubeSats. However, traditional approaches to deep space missions’ navigation based on ground radio facilities have difficulties in providing sufficient support for the increasing number of users and communication at a distance from the Earth (Kaplev et al., 2022). In particular, the existing Lunar navigation technologies such as weak signal global positioning system (GPS) and deep space network (DSN) are not able to ensure operations of the upcoming small-scale Lunar missions due to their limitations in localization performance as well as capacity aspects. Another way to provide Lunar PNT service is to create a dedicated Lunar global navigation satellite system (GNSS) constellation, like GNSS systems on Earth. Space agencies like NASA, ESA, and JAXA are now developing the lunar communications relay and navigation systems (LCRNS) and Lunar navigation satellite systems (LNSS). In their systems, satellites will be deployed in moon orbits to provide the communication, positioning, navigation, and timing (CPNT) service at the lunar south pole region where the Artemis base camp will be expected (Murata et al., 2022). Meanwhile, common challenges considered in lunar PNT research arise from poor geometry of the terrestrial GNSS satellites when seen from the lunar user, highly perturbed lunar orbits, and limitations in power, size, and cost of the equipment on lunar satellites (Iiyama et al., 2023). It is also not clear if there will be enough Lunar users to support the cost and resources this would require as the Low-cost surface missions may not be able to support the large power, mass, and weight requirements that these navigation solutions entail (Niemoeller et al., 2022). As an alternative, existing Lunar science and exploration assets could be used to create a low-cost, autonomous, ad-hoc, and on-demand mission-centric Lunar PNT swarm capable of providing PNT services to these low-cost lunar missions (Hagenau et al., 2021). Introducing the non-dedicated and ad-hoc Lunar navigation constellation gives a way to provide PNT services on-demand. The non-dedicated swarm assets of Lunar constellations are designed to localize themselves with minimal interaction with Earth by adding cooperative autonomous localization to lunar missions, freeing up valuable bandwidth and ground segment resources. An autonomous localization of Lunar constellations is based on the concept of the decentralized PNT system with a distributed extended Kalman filter (DEKF) approach to state estimation for minimal onboard operating costs. In the distributed data processing algorithm, computation is broken down and assigned to each satellite, resulting in a considerably decreased computational amount while maintaining the accuracy of the orbit ephemeris and clock offsets as the result of centralized data processing (Wen et al., 2019). The DEKF requires spacecraft to perform two-way ranging operations with each other to communicate simultaneously, leveraging neighbor two-way intersatellite link (ISL) measurements such as pseudoranges to, and relative velocities between, visible satellites as sensor values (Frank et al., 2021). The Lunar autonomous PNT simulation (LAPS) demonstrated the feasibility of orbital asset localization among ad-hoc Lunar small-sat constellations based on the DEKF in Hagenau et al. (2021) and evaluated the matching algorithm proposed by Frank et al. (2021) in scheduling position estimation updates. In previous papers, all assets and measurements are assumed to be always available without consideration of the impact of intermittent and permanent communication failure. This study presents localization performance with increasing levels of network degradation for swarm assets and users to demonstrate the robustness of the decentralized Lunar PNT service in more realistic scenarios. Main issues arising from communication failure include spacecraft permanent or transient loss, antenna failures, message delays, etc. We tested four possible reasons for network degradation for 7 days in 21 satellites frozen with an altitude of 5500 km, evenly spaced around 3 circular, 40 inclination orbital planes where each spacecraft has two directional antennas. As anchor nodes with an independent estimate of their position are required in the DEKF approach, two ground nodes in each pole and one node in the gateway were implemented in the simulation. First, the most probable failure scenario involves the loss of a single spacecraft due to solar interference and technical malfunctions of the assets. Losing the availability of a single spacecraft means losing the two-way ISL measurement of the asset in the DEKF update. In order to provide the best possible quality of PNT service with limited time and resources, the distributed Lunar constellations must schedule the communication activities. The scheduler leverages mixed-integer linear programming (MILP) for the coordination and scheduling of the desired “as-needed” localization service (Niemoeller et al., 2022). We assume the scheduler has completely excluded the spacecraft information before the DEKF update in the failure scenario. When a random spacecraft has been turned off at a specific time, the robustness of the autonomous Lunar PNT system is evaluated. The simulation results give an 11.5% degradation in median position accuracy compared to the idealized performance excluding the asset loss. Second, a large number of assets may vanish due to major hardware problems or meteor strikes around the moon. A multiple spacecraft loss can degrade the localization performance very fast by losing the communication ability to do cross-plane measurements and in-plane measurements in a 3-plane constellation. When the matching-based scheduler is aware of ISL availability, we investigate a large number of in-plane and cross-plane asset vanishments both in close proximity and equally spaced throughout the orbital plane. According to the simulations, the loss of in-plane measurements gives 40.2% degradation while cross-plane measurements degrade 50.5% of asset localization performance among available assets. Therefore, it is concluded that cross-plane measurements are more important in improving the position estimation accuracy. Third, spacecraft failure information can be lost due to the internal message delay, resulting in the DEKF update scheduler to solve the matching problem with unavailable assets. The DEKF update cycle is comprised of network setup, communication, and computations where a global broadcast network and a 2-way ISL network setup take 6 minutes in total (Frank et al., 2021). Once the broadcast network successfully transmits and receives information, a random spacecraft may lose its availability right before solving the matching problem. This means the matching solution is no longer optimal, resulting in degradation in the localization performance. A numerical assessment shows the matching-based scheduler with knowing failure holds 11.5% of position accuracy degradation, whereas the scheduler without knowing failure gives 34% degraded localization performance without asset loss. Fourth, a transient loss of a single or multiple spacecraft may occur due to their antenna outages. After losing the two-way ISL availability for a few DEKF update cycles, the availability of spacecraft can easily be recovered as their states have been independently updated using measurements from anchor nodes. It is likely that the longer failure will result in worse localization performance. We have tested the transient failure of a random single asset for 30 min in the simulation, which is losing 3 update cycles in the DEKF system. From the simulation results, the position accuracy has been degraded to 4.84% which is better than the degraded localization performance of 11.5% from the permanent loss scenario among available assets. In conclusion, the autonomous Lunar PNT system based on the DEKF approach shows the ability to maintain resilience and robustness in the possible communication failure scenarios, ensuring that localization accuracy is preserved across various network degradation and outages. Future studies on investigating user localization performance near the South Pole and the broadcast network system will be continued in the following months.

Yeji Kim↗

Assessing and Promoting Functional Resilience in Flight Crews During Exploration Missions

NASA plans to send humans to Mars in about 20 years. The NASA Human Research Program supports research to mitigate the major risks to human health and performance on extended missions. However, there will undoubtedly be unforeseen events on any mission of this nature - thus mitigation of known risks alone is not sufficient to ensure optimal crew health and performance. Research should be directed not only to mitigating known risks, but also to providing crews with the tools to assess and enhance resilience, as a group and individually. We can draw on ideas from complexity theory and network theory to assess crew and individual resilience. The entire crew or the individual crewmember can be viewed as a complex system that is composed of subsystems (individual crewmembers or physiological subsystems), and the interactions between subsystems are of crucial importance for overall health and performance. An understanding of the structure of the interactions can provide important information even in the absence of complete information on the component subsystems. This is critical in human spaceflight, since insufficient flight opportunities exist to elucidate the details of each subsystem. Enabled by recent advances in noninvasive measurement of physiological and behavioral parameters, subsystem monitoring can be implemented within a mission and also during preflight training to establish baseline values and ranges. Coupled with appropriate mathematical modeling, this can provide real-time assessment of health and function, and detect early indications of imminent breakdown. Since the interconnected web of physiological systems (and crewmembers) can be interpreted as a network in mathematical terms, we can draw on recent work that relates the structure of such networks to their resilience (ability to self-organize in the face of perturbation). There are many parameters and interactions to choose from. Normal variability is an established characteristic of a healthy physiological response. Healthy coupling has been investigated less extensively, but there are cases in which too tight or too loose coupling can be problematic. This might be in inter-individual behaviors, such as sleep cycles, coordination of work and meal times, and coupled motions during communication. Less apparent are couplings of physiological systems, nevertheless examples abound of coupled systems which might be monitored: cardio-respiratory rhythms; circadian rhythms, body temperature, and sleep; stress markers and cognition, sleep, and performance; profiles of biochemical markers related to immune function and nutritional status; sensorimotor aspects such as motion sickness, ataxia, reaction time, and manual control. Tools for resilience are then the means to measure and analyze these parameters, incorporate them into appropriate models of normal variability and interconnectedness, and recognize when parameters or their couplings are outside of normal limits. What to do when a problem is identified depends on its nature. Changes can be made to crew procedures, work pacing, interpersonal interactions, sleep cycles, meal timing and content, as guided by the model. Use and continued development of these methods could not only provide tools for resilience, but also meaningful autonomous work for the crew on an extended flight.

Shelhamer, Mark↗

Radiation Tolerance and Mitigation for Neuromorphic Processors

Neuromorphic processors are designed to execute Deep Neural Networks (DNNs) at very high speed using only a fraction of the electrical power needed to run a DNN on a traditional CPU or GPU. This unique capability makes Neuromorphic processors a prime candidate for space systems, where advanced computational tasks like image analysis, depth map reconstruction, or rover control need to be executed in a power-starved environment. In contrast to the growing number of applications of Neuromorphic processors in smart phones, the automotive and robotics domain, the space environment is unforgiving because of extreme temperatures and high levels of radiation. Any space system, operating beyond LEO requires computing hardware that is resilient against radiation effects. However, Neuromorphic processors have not yet been designed or tested for their radiation tolerance. In this report, we consider traditional methods of detection of radiation events and mitigation via redundancy and gauge their effectiveness on DNNs. In contrast to traditional flight software, however, neural networks represent a statistical algorithm, which might affect its resilience against radiation events. We will focus on the analysis of the tolerance of DNNs with respect to radiation events and discuss techniques to detect radiation hits using on-chip triple modular redundancy (TMR) on an Intel Loihi neuromorphic processor and to mitigate radiation damage. We describe an architecture for on-chip TMR for the Intel Loihi and present results of initial experiments.

Neural Networks↗

Autonomous Contingency Management In Urban Air Mobility: The Communication Network Awareness Machine System

Next Generation Air Transportation System (NextGen) has begun the modernization of the nation’s air transportation system (NAS), with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness [1]. The overall objective of the Air Traffic Management-eXploration (ATM-X) project is to facilitate the goals of NextGen by conducting research to enable the growing demand of new, mission variant, air vehicles with safe access to the NAS. The implementation and utilization of new and burgeoning technologies that are both flexible, scalable, and systematically user-focused are requisite for ATM-X to achieve its intention of NAS safe entry [2]. Researchers from NASA Langley’s Flight Deck Integration Team have developed a system architecture that would allow ATM-X to leverage the necessary capabilities of an Increasingly Autonomous System (IAS), machine-agent that will promote the safe access and operation of air vehicles within what has become the byproduct of NextGen modernization, a Net-Centric airspace architecture and an Urban Air Mobility (UAM) community. Conducting flight operations within this type of architecture constrains the human-agent’s natural ability by data management. When the massive volume of data, its types, and the acquisition speed at which the data is ingested is observed it becomes evident that the human-agent will be functioning at an operational disadvantage. Therefore, the development and integration of intelligent machine-agents into the flight deck are a necessary implementation to achieve ATM-X overall objective of safe access and operation in the NAS.

Urban Air Mobility↗

Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming

NextGen has begun the modernization of the nation’s air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two.

Houston, Vincent E.↗

Assessing and Promoting Functional Resilience in Flight Crews During Exploration Missions

The NASA Human Research Program works to mitigate risks to health and performance on extended missions. However, research should be directed not only to mitigating known risks, but also to providing crews with tools to assess and enhance resilience, as a group and individually. We can draw on ideas from complexity theory to assess resilience. The entire crew or the individual crewmember can be viewed as a complex system composed of subsystems; the interactions between subsystems are of crucial importance. Understanding the interactions can provide important information even in the absence of complete information on the component subsystems. Enabled by advances in noninvasive measurement of physiological and behavioral parameters, subsystem monitoring can be implemented within a mission and during training to establish baselines. Coupled with mathematical modeling, this can provide assessment of health and function. Since the web of physiological systems (and crewmembers) can be interpreted as a network in mathematical terms, we can draw on recent work that relates the structure of such networks to their resilience (ability to self-organize in the face of perturbation). Some of the many parameters and interactions to choose from include: sleep cycles, coordination of work and meal times, cardiorespiratory rhythms, circadian rhythms and body temperature, stress markers and cognition, sleep and performance, immune function and nutritional status. Tools for resilience are then the means to measure and analyze these parameters, incorporate them into models of normal variability and interconnectedness, and recognize when parameters or their couplings are outside of normal limits.

Shelhamer, M.↗