Search NASA⌕ Search

SEARCH · Search NASA

Results for “Automation and Autonomy”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Science Goal Driven Observing: A Step Towards Maximizing Science Returns and Spacecraft Autonomy

In the coming decade, the drive to increase the scientific returns on capital investment and to reduce costs will force automation to be implemented in many of the scientific tasks that have traditionally been manually overseen. Thus, spacecraft autonomy will become an even greater part of mission operations. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. The new generation of space-based telescopes/observatories will see deeper, with greater clarity, and they will generate data at an unprecedented rate. Yet, while onboard data processing and storage capability will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant bottleneck and cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will not meet the science goals of variability or minimum brightness, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground systems - both human and machine - for confirmation and/or further high priority analyses.

Koratkar, Anuradha↗
NASA NTRS1997

Team play with a powerful and independent agent: operational experiences and automation surprises on the Airbus A-320

Research and operational experience have shown that one of the major problems with pilot-automation interaction is a lack of mode awareness (i.e., the current and future status and behavior of the automation). As a result, pilots sometimes experience so-called automation surprises when the automation takes an unexpected action or fails to behave as anticipated. A lack of mode awareness and automation surprises can he viewed as symptoms of a mismatch between human and machine properties and capabilities. Changes in automation design can therefore he expected to affect the likelihood and nature of problems encountered by pilots. Previous studies have focused exclusively on early generation "glass cockpit" aircraft that were designed based on a similar automation philosophy. To find out whether similar difficulties with maintaining mode awareness are encountered on more advanced aircraft, a corpus of automation surprises was gathered from pilots of the Airbus A-320, an aircraft characterized by high levels of autonomy, authority, and complexity. To understand the underlying reasons for reported breakdowns in human-automation coordination, we also asked pilots about their monitoring strategies and their experiences with and attitude toward the unique design of flight controls on this aircraft.

NASA Discipline Space Human Factors↗
NASA NTRS2017

A Human-Autonomy Teaming Approach for a Flight-Following Task

Managing aircraft is becoming more complex with increasingly sophisticated automation responsible for more flight tasks. With this increased complexity, it is becoming more difficult for operators to understand what the automation is doing and why. Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human-autonomy teaming (HAT) in aviation, a part-task study was conducted to demonstrate, evaluate and refine proposed critical aspects of HAT. These features were built into an automated recommender system on a ground station available from previous studies. Participants performed a flight-following task once with the original ground station (i.e., No HAT condition) and once with the HAT features enabled (i.e., HAT condition). Behavioral and subjective measures were collected; subjective measures are presented here. Overall, participants preferred the ground station with HAT features enabled compared to the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task and reduced workload relative to the No HAT baseline.

ACFP↗
NASA NTRS2016

Transformational Autonomy and Personal Transportation: Synergies and Differences Between Cars and Planes

Highly automated cars have undergone tremendous investment and progress over the past ten years with speculation about fully-driverless cars within the foreseeable, or even near future, becoming common. If a driverless future is realized, what might be the impact on personal aviation? Would self-piloting airplanes be a relatively simple spin-off, possibly making travel by personal aircraft also commonplace? What if the technology for completely removing human drivers turns out to be further in the future rather than sooner; would such a delay suggest that transformational personal aviation is also somewhere over the horizon or can transformation be achieved with less than full automation? This paper presents a preliminary exploration of these questions by comparing the operational, functional, and implementation requirements and constraints of cars and small aircraft for on-demand mobility. In general, we predict that the mission management and perception requirements of self-piloting aircraft differ significantly from self-driving cars and requires the development of aviation specific technologies. We also predict that the highly-reliable control and system automation technology developed for conditionally and highly automated cars can have a significant beneficial effect on personal aviation, even if full automation is not immediately feasible.

Goodrich, Kenneth H.↗
NASA NTRS2018

Crew Resource Management for Automated Teammates (CRM-A)

Crew Resource Management (CRM) is the application of human factors knowledge and skills to ensure that teams make effective use of all resources. This includes ensuring that pilots bring in opinions of other teammates and utilize their unique capabilities. CRM was originally developed 40 years ago in response to a number of airline accidents in which the crew was found to be at fault. The goal was to improve teamwork among airline cockpit crews. The notion of "team" was later expanded to include cabin crew and ground resources. CRM has also been adopted by other industries, most notably medicine. Automation research now finds itself faced with similar issues to those faced by aviation 40 years ago: how to create a more robust system by making full use of both the automation and its human operators. With advances in machine intelligence, processing speed and cheap and plentiful memory, automation has advanced to the point that it can and should be treated as a teammate to fully take advantage of its capabilities and contributions to the system. This area of research is known as Human-Autonomy Teaming (HAT). Research on HAT has identified reusable patterns that can be applied in a wide range of applications. These patterns include features such as bi-directional communication and working agreements. This paper will explore the synergies between CRM and HAT. We believe that HAT research has much to learn from CRM and that there are benefits to expanding CRM to cover automation.

Crew Resource Management↗
NASA NTRSDate not supplied

Human Capabilities Assessments for Autonomous Missions: A Multi-Team Research Effort to Reduce Risk in the Human-System Integration Architecture for Future Deep-Space Missions

In future exploration missions beyond low earth-orbit, crew will have to execute complex operations and respond to off-nominal events, without real-time support from Mission Control. It is anticipated that increased reliance on automated systems, including human-centric vehicle and information architecture, will need to be designed to support the crew; increased risk to performance, health, and safety may occur if these are not implemented appropriately. The Human Factors and Behavioral Performance Element (HFBP) in the NASA Human Research Program supports research to characterize and mitigate such human health and performance risks, including the Risk of Adverse Outcome Due to Inadequate Human Systems Integration Architecture (HSIA). The HSIA risk addresses the integration of onboard capability and the crew roles and responsibilities necessary to enable the crew to respond effectively and efficiently in the increasingly autonomous mission operations environment. In 2017, HFBP released the “Human Capabilities Assessments for Autonomous Missions” (HCAAM) research topic to address HSIA related questions. HCAAM is a major NASA research effort that has assembled a multidisciplinary team from seven institutions to work closely with design and engineering efforts on research towards developing and refining human performance standards, guidelines and automation tools. The scientific focus is on quantitative assessment of human capabilities relevant to future deep-space missions during which earth/spacecraft communication is so delayed and intermittent that the crew must be able to function autonomously. The integrated strategy of the HCAAM project characterizes human capabilities and limitations related to potential performance decrements during long duration exploration mission spaceflight as relevant to both routine and complex task performance; defines system characteristics that reduce the likelihood or impact of potential decrements in human performance capabilities; performs integrated assessment of intelligent system responses within the context of an operational environment with relevant NASA tools, systems, and data structures in order to determine positive or negative interactions and validate recommended approaches; and proposes specific updates to existing standards and guidelines for inclusion in NASA handbooks for the design of future spacecraft intelligent systems that provide crew performance assessment/feedback, and to also serve as decision-support aids for the onboard crew (i.e., NASA-STD-3001, and NASA/SP-Human Integration Design Handbook (HIDH)). The scientific research vectors being addressed by the seven HCAAM teams include: - crew task performance (accuracy, efficiency) (crew + automation) - crew performance (accuracy, efficiency) - crew Situation Awareness - procedure design and multi-modal enhancement - concurrent tasking (mixed manual + some level of autonomy) - task handover - crew self-planning and time-lining - task design - trust in automation, real-time calibration - human multi-sensory feedback and guidance - human trust in on-board software-based intelligent assistants - virtual assistants The presentation will highlight plans and progress made in each of these research areas as well as the methods by which surrogate astronaut crews in the NASA JSC HERA spaceflight analog facility will function as human test subjects for all of the HCAAM research projects.

HCAAM VNSCOR↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1988

Expert systems for MSFC power systems

Future space vehicles and platforms including Space Station will possess complex power systems. These systems will require a high level of autonomous operation to allow the crew to concentrate on mission activities and to limit the number of ground support personnel to a reasonable number. The Electrical Power Branch at NASA-Marshall is developing advanced automation approaches which will enable the necessary levels of autonomy. These approaches include the utilization of knowledge based or expert systems.

Weeks, David J.↗
NASA NTRS2005

Situation Awareness of Onboard System Autonomy

We have developed intelligent agent software for onboard system autonomy. Our approach is to provide control agents that automate crew and vehicle systems, and operations assistants that aid humans in working with these autonomous systems. We use the 3 Tier control architecture to develop the control agent software that automates system reconfiguration and routine fault management. We use the Distributed Collaboration and Interaction (DCI) System to develop the operations assistants that provide human services, including situation summarization, event notification, activity management, and support for manual commanding of autonomous system. In this paper we describe how the operations assistants aid situation awareness of the autonomous control agents. We also describe our evaluation of the DCI System to support control engineers during a ground test at Johnson Space Center (JSC) of the Post Processing System (PPS) for regenerative water recovery.

Schreckenghost, Debra↗
NASA NTRSDate not supplied

Hazard Perception & Avoidance (HPA): Part Task 1 - Results Outbrief

n April 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This part task HITL began the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals were to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation tested manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This was conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study included levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected included response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, and perceived workload. Additional details and future anticipations are also discussed.

air taxis↗
NASA NTRS2017

Human-Autonomy Teaming: Supporting Dynamically Adjustable Collaboration

This presentation is a technical update for the NATO-STO HFM-247 working group. Our progress on four goals will be discussed. For Goal 1, a conceptual model of HAT is presented. HAT looks to make automation act as more of a teammate, by having it communicate with human operators in a more human, goal-directed, manner which provides transparency into the reasoning behind automated recommendations and actions. This, in turn, permits more trust in the automation when it is appropriate, and less when it is not, allowing a more targeted supervision of automated functions. For Goal 2, we wanted to test these concepts and principles. We present findings from a recent simulation and describe two in progress. Goal 3 was to develop pattern(s) of HAT solution(s). These were originally presented at HCII 2016 and are reviewed. Goal 4 is to develop a re-usable HAT software agent. This is an ongoing effort to be delivered October 2017.

Human-Autonomy Teaming↗
NASA NTRSDate not supplied

ACAS Xr Part Task Sim, Preliminary Experiment Design

In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) will conduct a manned, human-in-the-loop (HITL) simulation. This part task HITL will begin the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals will be to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation will test manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This will be conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study include levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected will include response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, perceived workload, and meaningful human control. Additional details and future anticipations are also discussed.

air taxis↗
NASA NTRS1998

Onboard Science Data Analysis: Opportunities, Benefits, and Effects on Mission Design

Much of the initial focus for spacecraft autonomy has been on developing new software and systems concepts to automate engineering functions of the spacecraft: guidance, navigation and control, fault protection, and resources management. However, the ultimate objectives of NASA missions are science objectives, which implies that we need a new framework for perfoming science data evaluation and observation planning autonomously onboard spacecraft.

spacecraft autonomy observation planning science d↗
NASA NTRS2004

Autonomous Multi-Sensor Coordination: The Science Goal Monitor

Many dramatic earth phenomena are dynamic and coupled. In order to fully understand them, we need to obtain timely coordinated multi-sensor observations from widely dispersed instruments. Such a dynamic observing system must include the ability to Schedule flexibly and react autonomously to sciencehser driven events; Understand higher-level goals of a sciencehser defined campaign; Coordinate various space-based and ground-based resources/sensors effectively and efficiently to achieve goals. In order to capture transient events, such a 'sensor web' system must have an automated reactive capability built into its scientific operations. To do this, we must overcome a number of challenges inherent in infusing autonomy. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe dynamic phenomena. The SGM system enables users to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of data to identify occurrences of the key events previously specified by the scientisther. When an event occurs, the system autonomously coordinates the execution of the users' desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems' Aqua/Terra spacecrafts' MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM was used to investigate forest fires, floods and volcanic eruptions. We are now identifying new Earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Koratkar, Anuradha↗
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 NTRS1987

Diagnosis: Reasoning from first principles and experiential knowledge

Completeness, efficiency and autonomy are requirements for suture diagnostic reasoning systems. Methods for automating diagnostic reasoning systems include diagnosis from first principles (i.e., reasoning from a thorough description of structure and behavior) and diagnosis from experiential knowledge (i.e., reasoning from a set of examples obtained from experts). However, implementation of either as a single reasoning method fails to meet these requirements. The approach of combining reasoning from first principles and reasoning from experiential knowledge does address the requirements discussed above and can possibly ease some of the difficulties associated with knowledge acquisition by allowing developers to systematically enumerate a portion of the knowledge necessary to build the diagnosis program. The ability to enumerate knowledge systematically facilitates defining the program's scope, completeness, and competence and assists in bounding, controlling, and guiding the knowledge acquisition process.

Williams, Linda J. F.↗