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NASA NTRS2016

Final Report of the NASA Technology Readiness Assessment (TRA) Study Team

The material in this report covers the results on the NASA-wide TRA team, who are responsible for ascertaining the full extent of issues and ambiguities pertaining to TRATRL and to provide recommendations for mitigation. The team worked for approximately 6 months to become knowledgeable on the current TRATRL process and guidance and to derive recommendations for improvement.The team reviewed the TRA processes of other government agencies (OGA), including international agencies, and found that while the high-level processes are similar, the NASA process has a greater level of detail. Finally, NASA’s HQ OCT continues to monitor the GAO’s efforts to produce a TRA Best Practices Guide, a draft of which was received in February 2016. This Guide could impact the recommendations of this report.

Technology readiness assessment↗
NASA NTRS2017

Introduction to the Navigation Team: Johnson Space Center EG6 Internship

The EG6 navigation team at NASA Johnson Space Center, like any team of engineers, interacts with the engineering process from beginning to end; from exploring solutions to a problem, to prototyping and studying the implementations, all the way to polishing and verifying a final flight-ready design. This summer, I was privileged enough to gain exposure to each of these processes, while also getting to truly experience working within a team of engineers. My summer can be broken up into three projects: i) Initial study and prototyping: investigating a manual navigation method that can be utilized onboard Orion in the event of catastrophic failure of navigation systems; ii) Finalizing and verifying code: altering a software routine to improve its robustness and reliability, as well as designing unit tests to verify its performance; and iii) Development of testing equipment: assisting in developing and integrating of a high-fidelity testbed to verify the performance of software and hardware.

Gualdoni, Matthew↗
NASA NTRS2017

WCRP Task Team for the Intercomparison of Reanalyses (TIRA): Motivation and Progress

Reanalyses have proven to be an important resource for weather and climate related research, as well as societal applications at large. Several centers have emerged to produce new atmospheric reanalyses in various forms every few years. In addition, land and ocean communities are producing disciplinary uncoupled reanalyses. Current research and development in reanalysis is directed at (1) extending the length of reanalyzed period and (2) use of coupled Earth system models for climate reanalysis. While WCRPs involvement in the reanalyses communities through its Data Advisory Council (WDAC) has been substantial, for example in organizing international conferences on reanalyses, a central team of reanalyses expertise is not in place in the WCRP structure. The differences among reanalyses and their inherent uncertainties are some of the most important questions for both users and developers of reanalyses. Therefore, a collaborative effort to systematically assess and intercompare reanalyses would be a logical progression that fills the needs of the community and contributes to the WCRP mission. The primary charge to the TIRA is to develop a reanalysis intercomparison project plan that will attain the following objectives.1)To foster understanding and estimation of uncertainties in reanalysis data by intercomparison and other means 2)To communicate new developments and best practices among the reanalyses producing centers 3)To enhance the understanding of data and assimilation issues and their impact on uncertainties, leading to improved reanalyses for climate assessment 4)To communicate the strengths and weaknesses of reanalyses, their fitness for purpose, and best practices in the use of reanalysis datasets by the scientific community. This presentation outlines the need for a task team on reanalyses, their intercomparison, the objectives of the team and progress thus far.

WDA↗
NASA NTRS2016

Independent Navigation Team Contribution to New Horizons' Pluto System Flyby

The New Horizons spacecraft made it closest approach to Pluto on 14 July 2015. The most significant challenge of this mission was that the Pluto system ephemeris was initially known with a precision of ~1000 km. This needed to be improved significantly on approach in order to meet the science requirements. During the final six months leading to the flyby, a JPL Independent Navigation (INAV) Team was included in the ephemeris knowledge update process as a cross-check on the Project Navigation (PNAV) Team's results. This paper discusses the INAV team's experiences and challenges navigating New Horizons through the Pluto planetary system encounter.

orbit determination↗
NASA NTRS2019

Mission Operations Working Group (MOWG) Report to the Aura Science Team

This presentation provides mission operations status for the Earth Observing System (EOS) Aura satellite to the Aura Science Team community. It is a summary of the splinter meeting held between the Aura Flight Operations Team (FOT) and the Instrument Operations Teams (IOT). The material covers highlights of activities, any significant issues or concerns, and future plans of the mission and instruments.

Fisher, Dominic M.↗
NASA NTRS2019

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.↗
NASA NTRSDate not supplied

Analyzing Natural Language Context in Human-Machine Teaming using Supervised Machine Learning

Building a foundation for trustworthiness and trust verification in multi-asset teaming is the research challenge of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Design Reference Mission (DRM) for ATTRACTOR is a search and rescue mission objective governed by a multi-member team consisting of human and machine operators. A crucial component to the effort is the communication between humans and autonomous agents throughout both planning and execution stages of the mission. Intuitive communication methods and modalities are posited as critical enablers for certifying trust and trustworthiness. This paper reports on the data collection and analysis conducted in support of the Human Informed Natural-language GANs Evaluation (HINGE)project to attain explainable and trusted communication between human-machine assets. Two identically curated image description datasets were acquired for HINGE, both consisting of two unique input modalities (typed vs. verbal) and retrieved in two distinct contexts (general vs. specific). The gathered datasets were assessed and compared using Parts-of-Speech (POS)features, sentence similarity metrics, and linguistic analysis. Then, the datasets were modeled and tested separately and in combination with one another using machine learning algorithms. The comparison and testing results reveal a superior dataset, by which a preferred context and input is understood, for generating image representations of missing persons using a Generative Adversarial Network (GAN).

Bryan A Barrows↗
NASA NTRSDate not supplied

Enabling Advanced Air Mobility Operations through Appropriate Trust in Human-Autonomy Teaming: Foundational Research Approaches and Applications

Emerging Advanced Air Mobility(AAM)operations will be enabled by increasingly autonomous systems, requiring technologies to take on more responsibilities and fundamentally altering traditional human-automation interaction paradigms. The growing reliance on higher levels of automation will necessitate research to identify capabilities and principles that facilitate humans and machines working and thinking better together, i.e., human-autonomy teaming (HAT). Trust is an inherent requirement in effective teams because when members work interdependently, those agents (human, automation) must be willing to accept a level of risk to rely upon each other to reach goals and contribute to team tasks. This work provides an initial approach to enabling AAM operations through appropriate trust within HAT. The main contributions of this approach resides in connecting the construct of trust to mental models. Using the outlined mental model approach, we propose novel HAT strategies, such as Adaptive Trust Calibration, and preview planned research activities derived from this approach. Additionally, we propose several practical applications that can currently be employed by AAM development communities.

Eric T Chancey↗
NASA NTRSDate not supplied

Intercenter Systems Analysis Team (ISAT) Overview

This presentation provides an overview of the Intercenter Systems Analysis Team (ISAT). It summarizes the team mission, types of studies conducted by the team, and the members.

Systems Analysis↗
NASA NTRS2021

NASA Academy Summer 2021 Equipment Development Team Report

Wildfires have become increasingly frequent, widespread, intense, and destructive. The 2021 NASA Academy at Langley Research Center was given the task of applying NASA technology to the challenges faced by wildland firefighting professionals. The Equipment Development Team focused on developing tools that could be deployed to make the work of wildland firefighters safer and more effective. The team developed both a conceptual design and a prototype of a lightweight respirator with air cooling capabilities. The team also researched body-worn biometric and environmental sensors, identifying a suite of sensors that could predict and alert firefighters to dangerous physical and environmental conditions. This report outlines the results of these activities, and analyzes each component of the systems, identifying challenges, possible technological solutions, and their feasibility.

Kilarendha Sundling↗
NASA NTRS2022

Lithological variation of asteroid Ryugu samples returned by the Hayabusa 2 spacecraft: Assessment from the 18 particles distributed to the initial analysis “Stone” team

JAXA’s Hayabusa2 spacecraft successfully returned~5.4g of C-type asteroid Ryugu materials on Dec. 2020 [e.g., 1] and the recovered samples were extensively analyzed by initial analysis teams and Phase2 curation teams. The reported results show that Ryugu samples are similar to CI chondritesin chemistry and mineralogy, showing evidence for aqueous alteration in the parent body [e.g., 2,3]. As a “stone” team of the initial analysis, we received 18coarse particles (>1 mm) of Ryugu samples to characterize their mineralogy and petrology. We found that the samples were breccias of mm-to-sub mm size clasts. All of the clasts are mainly composed of Mg-Fe phyllosilicates, but the alteration degree appears slightly different from one clast to another. Here we report a lithological variation of Ryugu samples to propose their reasonable lithological classification based upon different mineral assemblages and to discuss the formation and evolution of the Ryugu parent body.

Michael E Zolensky↗
NASA NTRSDate not supplied

The NASA DEVELOP Model: Multidisciplinary Teams Conduct Interdisciplinary Projects to Produce Transdisciplinary Outcomes

The DEVELOP Program, part of NASA’s Earth Applied Sciences’ Capacity Building Program, conducts 50-70 feasibility studies each year that utilize Earth observations to address local decision-making challenges and help inform action. DEVELOP uses these projects as the mechanism to build skills in its participants and partner organizations to assess and apply satellite data insights to environmental decision-making processes. While organized by thematic focus (ex. water resource management, ecological forecasting, disaster management), projects use a multidisciplinary team approach with teams of students, recent graduates, early career professionals, and transitioning career professionals, bringing different disciplines, life experience, and perspectives to execute projects that have been collaboratively designed with end-user partner organizations (federal agencies, state and local governments, non-profit and for-profit organizations, and international organizations). Projects are interdisciplinary in nature as they integrate methods from multiple disciplines, with a focus on the incorporation of satellite data with other data sources such as socioeconomic and demographic data, in situ measurements, model outputs, and partners’ knowledge, and take place under the guidance of science advisors from NASA, academia, and other partner organizations. The culmination of these multidisciplinary teams working on projects that draw from interdisciplinary methods and approaches, is a transdisciplinary solution for the partner organizations to explore further for potential adoption. This presentation will highlight the DEVELOP model of co-production and collaboration, lessons learned integrating people and project methodologies, and impact assessment activities surrounding the program’s efforts.

Capacity Building↗
NASA NTRSDate not supplied

Artemis Internal Science Team: Mission Planning Update

As eloquently outlined in the Artemis III Science Definition Team (A3SDT) Report, the definitive statement of science priorities for the first crewed landing of the Artemis program, the return of astronauts to the surface of the Moon is expected to yield paradigm-shifting scientific advances across a wide array of scientific disciplines while facilitating the creation of a thriving cislunar economy. To help establish a cohesive management for the Artemis campaign, the NASA Science Mission Directorate (SMD) and the Exploration Systems Development Mission Directorate (ESDMD) have established an Artemis Internal Science Team to complement future competed surface geology and payload science teams while executing critical programmatic functions at the point of hardware element interface decisions. Here, we describe aspects of Artemis III mission planning, specifically the current progress towards selection of candidate exploration regions for the Artemis III mission.

Artemis↗
NASA NTRS2018

Human Automation Teaming: Lessons Learned and Future Directions

Full autonomy seems to be the goal for system developers in almost every area of the economy. However, as we move from automated systems to autonomous systems, designers have needed to insert humans to oversee automation that has traditionally been brittle or incomplete. This creates its own problems as the operator is usually out of the loop when the automation hands over problems that it cannot handle. To better handle these situations, it has been proposed that we develop human automation teams that have shared goals and objectives to support task performance. This paper will describe an initial model of Human Automation Teaming (HAT) which has three elements: transparency, bi-directional communications, and human-directed execution. Transparency in our model is a method for giving insight into the reasoning behind automated recommendations and actions, bi-directional communication allows the operator to communicate directly with the automation, and finally the automation defers execution to the human. The model was implemented through a number of features on an electronic flight bag (EFB) which are described in the paper. The EFB was installed in a mid-fidelity flight simulator and used by 12 airline pilots to support diversion decisions during off-nominal flight scenarios. Pilots reported that working with the HAT automation made diversion decisions easier and reduced their workload. They also reported that the information provided about diversion airports was similar to what they would receive from ground dispatch, thus making coordination with dispatch easier and less time consuming. These HAT features engender more trust in the automation when appropriate, and less when not, allowing improved supervision of automated functions by flight crews.

Human-Autonomy Teaming↗
NASA NTRSDate not supplied

Problem Complexity and LLM: H-M Team Reliability in Challenging Environments

In traditional human-machine operations, the functional decomposition of actions and responsibilities among various agents is assigned a priori. For instance, in current air traffic operations, although assisted by software, human pilots have the ultimate control of aircraft. Multiagent human-machine and machine-machine systems will face problems of varying and potentially unpredicted complexity in future challenging scenarios of planetary, en route, and orbital activities. Hence, it is important to enable dynamic transfer of decision-making to appropriate team members, human or machine, depending on which agent is best equipped to solve that specific problem on a time budget. In this paper, we consider aspects of problem-solving and its modeling that affect the outcomes of decision-making as a function of solution quality and the likelihood of solving the problem on a required time budget. We focus on Large Language Models (LLM) as potential machine teammates and conclude that practical, predictive modeling of their performance, at the current stage of their development, is infeasible. Simple examples help us illustrate that current LLM will require fundamental advancements to provide reliable support in team decision-making, especially in safety-critical and time-critical domains. The study is not meant to diminish the value of the remarkable capabilities of LLM, but rather to gain a better understanding of the technology’s appropriate use and the needed additions.

function allocation↗
NASA NTRS1974

Astronomical activities of the Apollo orbital science photographic team

A partial accounting of Apollo Orbital Science Photographic Team (APST) work is presented as reported by one of its members who provided scientific recommendations for, guidance in, and reviews of photography in astronomy. Background on the formation of the team and its functions and management are discussed. It is concluded that the APST clearly performed the overall objective for which it was established - to improve the scientific value of the Apollo lunar missions. Specific reasons for this success are given.

Mercer, R. D.↗
NASA NTRS1984

Viking lander: Creating the science teams

The teams of scientists and the contractors involved in designing and fabricating the Viking lander spacecraft for Mars exploration are discussed. The requirements established for each member of the research teams are enumerated and the personnel selection process is described. Two instruments for the lander, a gas chromatograph-mass spectrometer and a biology life detection instrument, are addressed in terms of costs, design, planning, and subcontractors. The challenges and ultimate success of the research management efforts are related in detail.

Source record↗