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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Future Operations

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the future of GCS control stations and need for Human Systems Integration.

multi-vehicle control↗
NASA NTRS2022

Automation & Autonomy Standards and Guidelines for Space Vehicles

The NASA Office of the Chief Health and Medical Officer (OCHMO) requested HRP conduct a survey of industry and government standards and guidelines for the interaction of automation/autonomy with humans. Guidance for verification testing of human-automation designs was also requested. A rapid response was requested so that the deliverables could be provided as part of a future NASA solicitation for automated systems for HLS. The project began with a broad review and analysis of prior NASA work in this area, as well as external standards related to human interaction with automation/autonomy. The team reviewed approximately 200 documents, and interviewed multiple industry and government subject matter experts (SMEs). “Gold standard” documents were selected for focus and key categories were identified. A cross-walk of standards was created, and common standards and guidelines were selected and rephrased (where necessary) to be information-rich, concise, and usable. Verification guidelines and examples were also developed. The final report includes an introduction to automation and autonomy, selected standards and guidelines, verification method guidance, and research gaps. In addition to the report, the team delivered a list of candidate standards for autonomous vehicles, as well as a list of candidate standards for a future NASA-STD-3001 update on automation/autonomy. The presentation will describe the rapid response project approach and methods, examples of standards and guidelines identified and delivered to OCHMO, and research needed to develop additional automation/autonomy design guidance where gaps currently exist.

automation↗
NASA NTRS2022

m:N Working Group Status Report

This document reports on the status of the m:N working group including the barriers, sub-group status, DAA white paper and roadmapping exercise.

multi-vehicle control↗
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 NTRS1994

Continuum spectral characteristics of bursts measured with BATSE large area detectors

The continuum spectra of bursts are studied with particular emphasis placed on the search for spectral subcomponents. The spectra observed in individual bursts show significant evolution throughout their durations. The degree to which hard and soft spectra are combined in bursts and the effect this has on the complexity of the observed spectra are addressed. In particular the presence of broad cusps in spectra in the 50-100 keV range are studied to determine both the degree to which these features are present in the LAD data and whether superposition of hard and soft spectra simultaneously can account for them. Also the time histories of the power output (ergs/sec) from bursts are studied to investigate the dynamic coupling between hard and soft spectra in bursts as a probe of the autonomy of the hard and soft emission mechanisms.

Pendleton, Geoffrey N.↗
NASA NTRSDate not supplied

A New Control Paradigm: Multiple Aircraft Controlled by Multiple Operators

Remotely piloted aircraft systems (RPAS) are becoming more and more prevalent in the aerospace operations. This is true in a number of diverse domains; urban air mobility, medical product delivery, infrastructure inspection, high altitude pseudo-satellites, search and rescue, auto cargo and several other applications. One aspect that all of these share in common is the need for scalability to be viable and continue to grow. The Association of Uncrewed Vehicle Systems International (AUVSI) develops an annual economic report. They project that in the first three years of integration more than 70,000 jobs will be created in the US alone, with an economic impact of more than $13.6 billion. This benefit will grow through 2025 when we foresee more than 100,000 jobs created and economic impact of $82 billion. For many of these domains to reach these levels and have the scalability needed, they will require a remote pilot to control multiple aircraft (1:N) or the extension of that, multiple pilots controlling multiple aircraft (m:N). This is a new control paradigm that raises multiple issues in various areas. The issues include regulatory, technical, safety, community acceptance and Human Factors. Human factors issues include displays, pilot workload, pilot situation awareness just to name a few. This panel brings together researchers, developers and operators that have been working in the area of m:N. They will discuss the need, the issues and some potential solutions.

multi-vehicle control↗
NASA NTRSDate not supplied

m:N: m Operators Controlling N Vehicles

UAS are growing quickly and the promise of economic growth is large and real. However, for many domains to realize this potential, multi-vehicle control by a single operator (or m:N) is required. NASA has stood up an industry/gov't working group to identify issues and barriers. This work will be reviewed.

multi-vehicle control↗
NASA NTRS2006

AERCam Autonomy: Intelligent Software Architecture for Robotic Free Flying Nanosatellite Inspection Vehicles

The NASA Johnson Space Center has developed a nanosatellite-class Free Flyer intended for future external inspection and remote viewing of human spacecraft. The Miniature Autonomous Extravehicular Robotic Camera (Mini AERCam) technology demonstration unit has been integrated into the approximate form and function of a flight system. The spherical Mini AERCam Free Flyer is 7.5 inches in diameter and weighs approximately 10 pounds, yet it incorporates significant additional capabilities compared to the 35-pound, 14-inch diameter AERCam Sprint that flew as a Shuttle flight experiment in 1997. Mini AERCam hosts a full suite of miniaturized avionics, instrumentation, communications, navigation, power, propulsion, and imaging subsystems, including digital video cameras and a high resolution still image camera. The vehicle is designed for either remotely piloted operations or supervised autonomous operations, including automatic stationkeeping, point-to-point maneuvering, and waypoint tracking. The Mini AERCam Free Flyer is accompanied by a sophisticated control station for command and control, as well as a docking system for automated deployment, docking, and recharge at a parent spacecraft. Free Flyer functional testing has been conducted successfully on both an airbearing table and in a six-degree-of-freedom closed-loop orbital simulation with avionics hardware in the loop. Mini AERCam aims to provide beneficial on-orbit views that cannot be obtained from fixed cameras, cameras on robotic manipulators, or cameras carried by crewmembers during extravehicular activities (EVA s). On Shuttle or International Space Station (ISS), for example, Mini AERCam could support external robotic operations by supplying orthogonal views to the intravehicular activity (IVA) robotic operator, supply views of EVA operations to IVA and/or ground crews monitoring the EVA, and carry out independent visual inspections of areas of interest around the spacecraft. To enable these future benefits with minimal impact on IVA operators and ground controllers, the Mini AERCam system architecture incorporates intelligent systems attributes that support various autonomous capabilities. 1) A robust command sequencer enables task-level command scripting. Command scripting is employed for operations such as automatic inspection scans over a region of interest, and operator-hands-off automated docking. 2) A system manager built on the same expert-system software as the command sequencer provides detection and smart-response capability for potential system-level anomalies, like loss of communications between the Free Flyer and control station. 3) An AERCam dynamics manager provides nominal and off-nominal management of guidance, navigation, and control (GN&C) functions. It is employed for safe trajectory monitoring, contingency maneuvering, and related roles. This paper will describe these architectural components of Mini AERCam autonomy, as well as the interaction of these elements with a human operator during supervised autonomous control.

Fredrickson, Steven E.↗
NASA NTRS1985

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics.

Firschein, O.↗
NASA NTRS2004

Autonomous Navigation Results from the Mars Exploration Rover (MER) Mission

In January, 2004, the Mars Exploration Rover (MER) mission landed two rovers, Spirit and Opportunity, on the surface of Mars. Several autonomous navigation capabilities were employed in space for the first time in this mission. ]n the Entry, Descent, and Landing (EDL) phase, both landers used a vision system called the, Descent Image Motion Estimation System (DIMES) to estimate horizontal velocity during the last 2000 meters (m) of descent, by tracking features on the ground with a downlooking camera, in order to control retro-rocket firing to reduce horizontal velocity before impact. During surface operations, the rovers navigate autonomously using stereo vision for local terrain mapping and a local, reactive planning algorithm called Grid-based Estimation of Surface Traversability Applied to Local Terrain (GESTALT) for obstacle avoidance. ]n areas of high slip, stereo vision-based visual odometry has been used to estimate rover motion, As of mid-June, Spirit had traversed 3405 m, of which 1253 m were done autonomously; Opportunity had traversed 1264 m, of which 224 m were autonomous. These results have contributed substantially to the success of the mission and paved the way for increased levels of autonomy in future missions.

autonomous↗
NASA NTRS1985

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures.

Firschein, O.↗
DOE OSTI2024

Distributed Wind Guidebook [Slides]

Distributed wind energy technologies generate clean, carbon-free power close to the point of electrical consumption (i.e., close to people and their energy needs). Distributed wind energy can help individuals and communities meet their unique goals, such as reducing impacts on climate change, decreasing electricity bills, boosting energy independence or autonomy from the electric grid, and enhancing grid reliability and resiliency. This guidebook is designed to support individuals and communities in deploying distributed wind energy technologies by providing fundamental information needed for success. Each section is framed around a key question in the journey to deployment and offers resources to help you answer it.

17 WIND ENERGY↗
NASA NTRS1984

Psychological Issues in Online Adaptive Task Allocation

Adaptive aiding is an idea that offers potential for improvement over many current approaches to aiding in human-computer systems. The expected return of tailoring the system to fit the user could be in the form of improved system performance and/or increased user satisfaction. Issues such as the manner in which information is shared between human and computer, the appropriate division of labor between them, and the level of autonomy of the aid are explored. A simulated visual search task was developed. Subjects are required to identify targets in a moving display while performing a compensatory sub-critical tracking task. By manipulating characteristics of the situation such as imposed task-related workload and effort required to communicate with the computer, it is possible to create conditions in which interaction with the computer would be more or less desirable. The results of preliminary research using this experimental scenario are presented, and future directions for this research effort are discussed.

Morris, N. M.↗
NASA NTRS2019

Astrobiology Survey of a Lava Cave at Lava Beds National Monument by a Rover Carrying a Remote Sensing Instrument Payload

We report here on a survey of a lava tube cave by a rover that is instrumented for astrobiology missions. The NASA Ames testbed rover, CaveR, was deployed in Valentine Cave in Lava Beds National Monument (N. CA, USA) during August of 2018. The rover carried an instrument package consisting of Near Infrared and Visible Spectrometer System (NIRVSS) a point spectrometer operating in 1590-3400 nm range, sensitive to H2O and -OH bearing minerals, pyroxenes, and carbonates (Roush, et al 2018); the bore sighted Drill Operations Camera (DOC), a monochrome imager illuminated by LEDs at 410, 540, 640, 740, 905 and 940 nm; a Realsense ™ depth sensor system for 3D model generation; and a high resolution DSLR stereo camera. The payload was mounted on a tiltable instrument platform attached to the left side of the rover. The rover was driven manually in the cave by field operators, following instructions from a remote science operations team, and simulating a mission concept with science-guided autonomy. A simulated mission took place for 3 days with a team of 3 scientists selecting targets and interpreting data from the payload. To begin the mission, the rover drove along one wall of the cave imaging continuously with the Realsense in 20 m cave segments, three total. At the start of each day, the images from a 20m segment and a panorama stitched from them were provided to the science team to examine. The science team used these data to prioritize specific points along the cave wall for the collection of NIRVSS, DOC, and DSLR data. The objective of the data collection was to identify and study putative biological and mineralogical features in the cave. The data were delivered in xGDS, a customized mapping, planning, and data base management software developed at NASA Ames (Lee, et al 2013). Once the targets for further observations were selected, a plan for collecting the observations (positions in the cave and pointing for each requested observation) was constructed using xGDS and delivered to a rover team to execute the science data collection plan. Acquired data were delivered back to the science team for analysis. Preliminary results from the experiment illustrate the utility of the system (rover plus payload) to study the cave geology and mineralogy and its potential for identifying biomineral features.

Wong, Uland↗