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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment

Many envisioned technological and conceptual innovations focus on allocating more functions to automation, relegating the human as an afterthought if not a nuisance. Yet, until complete autonomy is realized, the human will remain “in the loop”. The National Aeronautics and Space Administration is supporting research for the development and maturation of automated technologies and architectures for the future of advanced air mobility. Standing up a remote operations center used to control, manage, and monitor multiple highly automated vehicles is an important step towards realizing the advanced air mobility vision. At the National Aeronautics and Space Administration’s Langley Research Center, a remote operation center exists and has been tested using humans piloting simulated vehicles. In this paper, we explore the human element within live flight operations that rely on increasingly automated technologies. Four ground control station operators performed multiple live flight operations. We employed a naturalistic approach and relied on qualitative data such as interviews and discussions with subject matter experts to help facilitate discovery. The present work evaluates the functions that the human and the automation had during the live operations, lists psychological constructs that may have promoted or reduced task performance, and provides recommendations on design of the remote operations environment and training of future ground control station operators.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Exploring Self-Scheduling Strategies and Heuristics in Novice Schedulers

Missions beyond low Earth orbit will require crews to act with greater autonomy. Increasing communication delays will require crews to take on tasks currently supported by Mission Control, including the involved process of scheduling and rescheduling their own complex spaceflight timelines to fit a variety of restrictions and constraints. Astronauts, unlike Mission Control, are not expert planners, however, and determining simple strategies and heuristics that enable them to schedule successfully can increase the range of problems they can solve successfully. In two human-in-the-loop scheduling experiments, we analyzed 1) common strategies among novice schedules, and 2) the development of self-scheduling heuristics. We find that, even when participants are instructed to follow a given strategy, they rapidly develop their own self-scheduling heuristics as they learn to successfully complete the scheduling task. Participants conducting naturally learn to schedule activities with the most constraints first, while participants vary in heuristics in the rescheduling task.

self-scheduling↗
NASA NTRSDate not supplied

Exploring Self-Scheduling Strategies and Heuristics in Novice Schedulers

Missions beyond low Earth orbit will require crews to act with greater autonomy. Increasing communication delays will require crews to take on tasks currently supported by Mission Control, including the involved process of scheduling and rescheduling their own complex spaceflight timelines to fit a variety of restrictions and constraints. Unlike Mission Control, astronauts are not expert planners, and determining strategies and heuristics that enable crews to schedule successfully may increase the range of problems they can solve. In two human-in-the-loop scheduling experiments, we analyzed 1) common strategies among novice schedulers and 2) the development of self-scheduling heuristics. We find that, even when participants are instructed to follow a given strategy, they rapidly develop their own self-scheduling heuristics as they learn to successfully complete the scheduling task. While scheduling, participants naturally learn to arrange activities with the most constraints first, whereas while rescheduling, they display greater variability in their heuristics.

self-scheduling↗
NASA NTRSDate not supplied

Airspace Integration Considerations for Increasingly Autonomous Flight and Operations

There is much interest in autonomous systems and their operations. When it comes to autonomous systems, it is critical to understand various levels of proposed autonomy, reasons for selecting autonomous capabilities, certification approaches and various challenges or research needs associated with the integration of autonomous systems in the National Airspace System (NAS). Furthermore, there are levels of autonomous systems proposed for different types of aircraft including: single pilot operations, remotely piloted operations, fully autonomous operations, and multiple aircraft managed by a single, remote pilot. This preliminary article aims to provide a broad overview rather than concentrate on a specific element of autonomous flight. Hence, the focus of this article is on enabling various types of increasingly autonomous aircraft and their operations routinely within the next five to ten years, consistent with the FAA’s Info-Centric NAS (ICN) vision. Under ICN, a fully integrated information environment supports collaboration across diverse traffic management services and shifts decision-making to the most appropriate actor supported by automation. However, the roles and responsibilities of Air Traffic Services (ATS) personnel providing traditional services are likely to be very similar to today in that humans will still be responsible for majority of decision making. There is another NASA research effort beyond ICN is underway called Sky For All which is not covered in this article as it is in the planning stages.

autonomy↗
NASA NTRSDate not supplied

Architecting Safer Autonomous Aviation Systems

The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.

Architecture patterns↗
NASA NTRSDate not supplied

Facilitating Crew-Computer Collaboration During Mixed-Initiative Space Mission Planning

As NASA looks toward longer duration missions, there will inevitably be a stronger emphasis on crew autonomy, particularly in the domains of mission planning. Ensuring that astronauts, while subject to lengthy periods of communication delay with Earth-based mission support personnel, are able to independently adapt their schedules to rapidly changing environments is a critical aspect of deep-space exploration. This task will likely require the assistance of computer support systems, as the task of mission planning is complex and currently requires dedicated console operators. A mixed-initiative approach can help alleviate some of the more workload-heavy aspects of planning by offloading the intricate task of constraint management to a computer, while still allowing the crew member to maintain overall control of the plan. Playbook is a mission planning tool that has been developed specifically to support this type of mixed-initiative scheduling. This paper examines: 1) the operational evidence of the challenges and viability of autonomous crew planning, and 2) the novel scheduling capabilities in Playbook that are meant to address those findings.

human-computer interaction↗
NASA NTRSDate not supplied

Development of Urban Air Mobility (UAM) Vehicles for Ease of Operation

To date the air transportation system has been developed with the in-cremental introduction of new technology and with highly experienced air transport pilots and air traffic controllers overseeing flight operations. Thus, we currently have one of the safest commercial aviation systems in the world. General Aviation (GA) in the United States, however, has not always followed the same cautious and monitored approach to implementation; consequently, the GA safety record does not meet the high standards of commercial aviation. Recently, a new system known as Urban Air Mobility (UAM), is attracting considerable interest and investment from industry and government agencies. UAM refers to a system of passenger and small-cargo air transportation vehicles within an urban area with the goal of reducing the number of times we need to use our cars, thus improving urban traffic by moving people and cargo from crowded single pas-senger vehicles on our roads to personal and on-demand air vehicles. These UAM vehicles will be small and based on electric, Vertical-Take-Off-and-Landing (eV-TOL) systems. A significant component of UAM is offloading of flight-man-agement responsibilities from human pilots to newly-developed autonomy. Cur-rently, over 100 UAM vehicles are either in development or production. Most, if not all, have a goal of fully autonomous vehicle operations, but fully autonomous flying vehicles are not expected in the near future. Therefore, we are de-veloping concepts for UAM vehicles that will be easy to fly and/or manage by operators with minimal pilot training. In this paper we will discuss our human-automation teaming approach to develop an easy-to-operate VTOL aircraft, and some of the fly-by-wire technology needed to stabilize the vehicle so that a sim-ple ecological mental model of the flying task can be implemented. We will discuss the requirements for a stability augmentation system that must be developed to support our simple pilot input model, and also present design guidelines and requirements based on a pilot input and management model. Finally, our ap-proach to vehicle development will involve considerable operator testing and evaluation: improving pilot model, inceptors, displays and also work on a plan for how a UAM vehicle can be integrated with terminal area air traffic control airspace with minimal impact on controller workload.

UAM↗
NASA NTRSDate not supplied

Autonomous Constrained Control for Arbitrary Thruster Configurations of Gimbaling Thrusters in SE(3)

In order to develop robust autonomy in spacecraft, it is desirable to develop methods for guiding and controlling arbitrarily-configured spacecraft with any combination of thrusters of various types, i.e. either static reaction control system thrusters and gimbaling thrusters. Scenarios in which autonomous selection of thrusters may be needed include the case of a stuck or inhibited thruster that restricts the motion of the vehicle, vehicles with changing mass properties such as logistics modules or tugs, or vehicles that have imposed constraints on thrusters during docking in order to avoid plume impingement on a space station or adjacent spacecraft. To that end, a spacecraft must be able to rapidly and autonomously reconfigure thruster firing histories and update guidance protocols in accordance with newly imposed constraints. In this paper, a methodology is presented that enables a spacecraft to autonomously select thrusters of any configuration and type in order to optimally match a desired 6-degree-of-freedom navigation and control within the special Euclidean SE(3) framework. The residual motion imposed by off-nominal thruster configurations or thrusters that are not fully controllable is identified by the spacecraft and solved for over time, both for static reaction control systems and for the case of gimbaling thrusters.

GN&C↗
NASA NTRS2021

Strategies for the Design and Operation of Resilient Extraterrestrial Habitats

An Earth-independent permanent extraterrestrial habitat system must function as intended under continuous disruptive conditions, and with significantly limited Earth support and extended uncrewed periods. Designing for the demands that extreme environments such as wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts (direct or indirect), vibrations, and solar particle events, will place on long-term deep space habitats represents one of the greatest challenges in this endeavor. This context necessitates that we establish the know-how and technologies to build habitat systems that are resilient. Resilience is not simply robustness or redundancy: it is a system property that accounts for both anticipated and unanticipated disruptions via the design choices and maintenance processes and adapts to them in operation. We currently lack the frameworks and technologies needed to achieve a high level of resilience in a habitat system. The Resilient ExtraTerrestrial Habitats Institute (RETHi) has the mission of leveraging existing novel technologies to provide situational awareness and autonomy to enable the design of habitats that are able to adapt, absorb and rapidly recover from expected and unexpected disruptions. We are establishing both fully virtual and coupled physical-virtual simulation capabilities that will enable us to explore a wide range of potential deep space SmartHab configurations and operating modes.

Space habitats↗
NASA NTRS2024

Concept, Design, & Implementation of a Remote Vehicle Operations Center for Autonomous Missions

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. The prototype facility known as the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center is being used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. ROAM provides a key capability to enable full end-to-end hardware- and human-in-the-loop simulation testing, connecting with simulated small-UAS and creating a seamless Live-Virtual-Constructive (LVC) environment. This report describes the development of the ROAM UAS Operations Center from concept through design, culminating in the current implementation at NASA’s Langley Research Center.

CERTAIN↗
NASA NTRSDate not supplied

Lunar Command and Control Interoperability (LuCCI) Project Overview

The Lunar Command and Control Interoperability (LuCCI) project was formed to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS.

Communications↗
NASA NTRSDate not supplied

Autonomous RPOD for Arbitrarily Configured Spacecraft with Anomaly Detection

Autonomous GN&C is a necessary component for a sustainable deep-space logistics architecture. The challenges for establishing robust autonomy are numerous, from state uncertainty, to anomaly detection and recovery. In this work, previous work investigating autonomous GN&C for arbitrary thruster configurations and mass properties is expanded to include state uncertainty and anomaly detection. Logistics vehicles with off-center-of-mass thruster configurations and in the presence of large but realistic state uncertainties are simulated in a Rendezvous, Proximity Operations and Docking scenario. Furthermore, stuck and non-functional thrusters are simulated, demonstrating the vehicle's ability to identify and overcome thruster anomalies. The simulations demonstrate that even with these realistic ambiguities, the vehicle is able to converge to the desired pose.

GN&C↗
NASA NTRSDate not supplied

Design and Applications of Tethered Spacecraft Simulators

Small, tethered spacecraft, or space tugs, are a promising space capability. They can be deployed on tethers and used for a wide variety of in-space activities; a large portion of which fall under in-space servicing, assembly, manufacturing (ISAM) functions. This includes functions such as structural mating and assembly, servicing, proximity operations, capture, docking, mating, and relocation. However, there is a great deal of work to be done to raise the Technology Readiness Level (TRL) of the capabilities needed to fully realize these capabilities. The Flat Floor Robotics Lab (FFRL) at NASA’s Marshall Spaceflight Center (MSFC) has been working on space tug development for several years. The eponymous floor is 44 by 86 feet, made from a self-leveling epoxy. This creates an extremely smooth surface that air bearings can float on with very little friction, creating a simulation of zero gravity in a two-dimensional plane. The lab has several platforms of various sizes that act as vehicle simulators for testing sensors, control algorithms, mechanisms, etc. One of these platforms is a small spacecraft simulator, which can be tethered to simulate a space tug. In the last several months, the FFRL team has been working with MSFC’s welding group to conduct a laser beam welding demonstration on air bearing platforms. This demo is utilizing a space tug simulator to fly up to a floating weld platform, where two parts are clamped together and laser welded. The space tug simulator has an air bearing to provide float, as well as several actuated air thrusters to provide propulsion and make the simulator maneuverable. Currently, flights of this simulator are completed by a human operator. An RF hand controller sends signals to both actuate and fire three sets of thrusters on the simulator. This makes any maneuvers of the space tug highly subject to human error, and operators must have dedicated practice time before being able to perform maneuvers with any sufficient reliability. For the current laser welding demonstration, human operated performance is sufficient, but future implementations of in-space welding and other ISAM efforts will require higher precision, reliability, and autonomy. MSFC has funded a project that will allow a small team in the FFRL to develop the next generation of space tug simulator, which will be automated. Closed loop control will allow a user to command a position, or set of positions, that the simulator will be able to “fly” to on its own. This Maneuverable Automated Tethered Spacecraft (MATS) simulator will have configurable design, to allow a variety of payloads and mission configurations to be demonstrated on the air bearing floor. This will be an invaluable test bed for low and mid TRL advancement for space tether mission subsystems, such as more advanced demonstrations of in-space welding. Once one MATSS is constructed and demonstrated effectively, several more can be built, enabling more advanced tether missions; Electric Sail test beds, sensor arrays, and other science-enabling missions. This paper will detail the design and test of the FFRL’s MATS simulator, including efforts to establish closed loop control and autonomous capabilities. It will also detail use cases for this technology, and how it will further develop both develop science and exploration missions.

Space Tethers↗
DOE OSTI2022

Learning-based Anticipatory Control of Microreactors

Learning-based anticipatory control advances the level of autonomy of microreactor control systems, a key consideration for the unattended operation of fission batteries. In this work, an anticipatory control system is shown to provide accurate load-following for a microreactor by utilizing a model predictive control framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

BEAM CORE: A Flexible Ecosystem for Freight, Demographics, and Vehicle Analysis

The Behavior, Energy, Autonomy, and Mobility Comprehensive Regional Evaluator (BEAM CORE) is an open-source, modular ecosystem of highly refined, agent-based modeling tools developed by Lawrence Berkeley National Laboratory and the National Laboratory of the Rockies. Organizations such as metropolitan planning organizations, agencies, and companies can use BEAM CORE to analyze freight movement and optimize logistics solutions, assess the impacts of emerging freight technologies or e-commerce trends, model dynamic population growth and evolution, and understand the drivers and impacts of electric vehicle adoption across households in a given region. Users can choose from a flexible suite of modeling modules according to their needs and priorities. The modules integrate with travel demand models to support enhanced analysis of diverse scenarios involving freight movement, vehicle technologies, and other key factors relevant to regional planning.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, in this work, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Advice for emerging researchers on research program development: A personal case study

Yearly, hundreds of new scientists and engineers start their independent research programs at universities, government laboratories or other institutions. Every individual embarks on this pathway with some idea about what research directions they wish to undertake, but often, with little insight into how their research career may evolve over time. Additionally, there are relatively few descriptions of how the research programs of other scientists and engineers have developed over time. In most cases where such overviews are available, they are written towards the end of a researcher's career, at a point far from the early development of the program. In this work, I provide my personal thoughts and observations on the development and evolution of the first 16 years of my research program as a case study. This overview does not intend to suggest this particular case as an exemplar, but rather, provides a single example of how one program evolved. In the course of the description, I try to identify general observations and suggestions that might aid a new researcher in the chemical sciences or engineering in the development of their research program. Specifically, the focus is on a program where the newly independent investigator has significant autonomy to select research topics of her/his choice (e.g., many universities, some government or corporate labs), although it is recognized that in many organizations, a researcher must fit into and adapt to the specific needs of the host institution. A particular aspect of my research program is that it blends research on (1) materials chemistry, (2) catalysis and reaction engineering, and (3) separations. As such, I will draw from specific examples of my own work in these fields as part of the discussion. However, the reader should note that this perspective is not a traditional overview of the full breadth of the title research topic, although my group has written such reviews and perspectives on a variety of the subjects covered here.1-6 Instead, here I discuss how my program evolved in stages, including (1) the search for a position, (2) launching your laboratory, (3) development of research themes and finishing with (4) a summary of advice for researchers starting their independent laboratories.

36 MATERIALS SCIENCE↗
DOE OSTI2022

3D LiMn 2 O 4 nThin Film Deposited by ALD: A Road toward High-Capacity Electrode for 3D Li-Ion Microbatteries

Miniaturized electronics suffer from a lack of energy autonomy. In that context, the fabrication of lithium-ion solid-state microbatteries with high performance is mandatory for powering the next generation of portable electronic devices. Here, the fabrication of a thin film positive electrode for 3D Li-ion microbatteries made by the atomic layer deposition (ALD) method and in situ lithiation step is demonstrated. The 3D electrodes based on spinel LiMn 2 O 4 films operate at high working potential (4.1 V vs Li/Li + ) and are capable of delivering a remarkable surface capacity (approximate to 180 mu Ah cm -2 ) at low C-rate while maintaining more than 40 mu Ah cm -2 at C/2 (time constant = 2 h). Both the thickness of the electrode material and the 3D gain of the template are carefully tuned to maximize the electrode performance. Further, advanced characterization techniques such as transmission electron and X-ray transmission microscopies are proposed as perfect tools to study the conformality of the deposited films and the interfaces between each layer: no interdiffusion or segregation are observed. This work represents a major issue towards the fabrication of 3D-lithiated electrode by ALD-without any prelithiation step by electrochemical technique-making it an attractive solution for the fabrication of 3D Li-ion solid-state microbatteries with semiconductor processing methods.

3D Li-ion micro-batteries↗