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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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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

A Cockpit-Based Application for Traffic Aware Trajectory Optimization

The Traffic Aware Planner (TAP) is a cockpit-based advisory tool designed to be hosted on a Class 2 Electronic Flight Bag and developed to enable the concept of Traffic Aware Strategic Aircrew Requests (TASAR). This near-term concept provides pilots with optimized route changes that reduce fuel burn or flight time, avoids interactions with known traffic, weather and restricted airspace, and may be used by the pilots to request a trajectory change from air traffic control. TAP's internal architecture and algorithms are derived from the Autonomous Operations Planner, a flight-deck automation system developed by NASA to support research into aircraft self-separation. This paper reviews the architecture, functionality and operation of TAP.

TASAR↗
NASA NTRS2021

Power Autonomy Research and Development Environment (PARDE) User’s Guide Version 0.1.2

This document is a user's guide for the Power Autonomy Research and Development Environment (PARDE) software package. PARDE is a version of NASA's Autonomous Power Control (APC) software that can be used to evaluate fault management and automatic power system reconfiguration algorithms in a relevant system without having to fully develop all the supporting software. Software items included are a set of C++ class source files representing simplified fault management and reconfiguration logic, a power system simulation representing a notional architecture for NASA's Gateway vehicle, a web-based graphical user interface for running and testing the simulation and APC, a Docker-based automatic setup script for a development environment, and a user's guide.

autonomous power control↗
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Experiential Learning

The DEVELOP Program, part of NASA’s Applied Sciences’ Capacity Building Program, connects decision makers and early career individuals through rapid 10-week feasibility studies. These projects utilize Earth observation assets to address environmental issues and are conducted by small, interdisciplinary teams who work autonomously under the guidance of a cohort of science advisors. The teams tailor their research to create end products that support partner decision making processes. The connection to real-world decision making and direct communication with partner organizations drives meaningful experiences for DEVELOP participants that are directly applicable to future work in the geosciences. This poster will share DEVELOP’s model for offering meaningful experiences to its participants, address lessons learned over the past 20+ years, and highlight a variety of testimonials.

Capacity Building↗
NASA NTRSDate not supplied

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembly engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗
NASA NTRSDate not supplied

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembled engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗
NASA NTRS2023

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembly engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗
DOE OSTI2019

GROVER: Ground-based Robotic Omnidirectional Vehicle for Electric Research

Oak Ridge National Laboratory has developed a living laboratory to accelerate the development of electric vehicles and connected and automated vehicle (CAV) technologies. GROVER is a small autonomous bus that uses pulsed laser sensors to move in any direction, with four wheels operating independently, rotating a full 360 degrees.

3-D↗
NASA NTRS1990

Evolution and advanced technology

The NASREM architecture with its standard interfaces permits development and evolution of the Flight Telerobotic Servicer to greater autonomy. Technologies in control strategies for an arm with seven DOF, including a safety system containing skin sensors for obstacle avoidance, are being developed. Planning and robotic execution software includes symbolic task planning, world model data bases, and path planning algorithms. Research over the last five years has led to the development of laser scanning and ranging systems, which use coherent semiconductor laser diodes for short range sensing. The possibility of using a robot to autonomously assemble space structures is being investigated. A control framework compatible with NASREM is being developed that allows direct global control of the manipulator. Researchers are developing systems that permit an operator to quickly reconfigure the telerobot to do new tasks safely.

Ollendorf, Stanford↗
NASA NTRS2017

Effects on Task Performance and Psychophysiological Measures of Performance During Normobaric Hypoxia Exposure

Human-autonomous systems have the potential to mitigate pilot cognitive impairment and improve aviation safety. A research team at NASA Langley conducted an experiment to study the impact of mild normobaric hypoxia induction on aircraft pilot performance and psychophysiological state. A within-subjects design involved non-hypoxic and hypoxic exposures while performing three 10-minute tasks. Results indicated the effect of 15,000 feet simulated altitude did not induce significant performance decrement but did produce increase in perceived workload. Analyses of psychophysiological responses evince the potential of biomarkers for hypoxia onset. This study represents on-going work at NASA intending to add to the current knowledge of psychophysiologically-based input to automation to increase aviation safety. Analyses involving coupling across physiological systems and wavelet transforms of cortical activity revealed patterns that can discern between the simulated altitude conditions. Specifically, multivariate entropy of ECG/Respiration components were found to be significant predictors (p< 0.02) of hypoxia. Furthermore, in EEG, there was a significant decrease in mid-level beta (15.19-18.37Hz) during the hypoxic condition in thirteen of sixteen sites across the scalp. Task performance was not appreciably impacted by the effect of 15,000 feet simulated altitude. Analyses of psychophysiological responses evince the potential of biomarkers for mild hypoxia onset.The potential for identifying shifts in underlying cortical and physiological systems could serve as a means to identify the onset of deteriorated cognitive state. Enabling such assessment in future flightdecks could permit increasingly autonomous systems-supported operations. Augmenting human operator through assessment of cognitive impairment has the potential to further improve operator performance and mitigate human error in safety critical contexts. This study represents ongoing work at NASA intending to add to the current knowledge of psychophysiologically-based input to automation to increase aviation safety.

Stephens, Chad↗
NASA NTRS1990

NASA Center for Intelligent Robotic Systems for Space Exploration

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE.

Source record↗
DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗
NASA NTRS2023

Exploration Medical Capability Clinical Decision Support System Architecture Recommendation

A new era in space exploration has arrived with the goal of establishing a long-term presence on the Moon and using those learning to take the next giant leap: sending the first astronauts to Mars. These ambitious goals will require significant changes in in-flight medical care due to constraints on mass, volume, power, crew time, and medical evacuation capabilities. Furthermore, while crews currently rely on real-time communications with ground-based medical providers, as distance from Earth increases, so do communication delays and disruptions. The crew will need to autonomously detect, diagnose, treat, and prevent medical events. These constraints require development of transformative solutions and new technologies. Through participation in the Human Research Program’s (HRP) Exploration Medical Capability (ExMC) research, NASA has developed and demonstrated a Medical Data Architecture (MDA), a platform upon which a robust capability of Clinical Decision Support (CDS) can be built. As a result, NASA is now positioned to build an advanced autonomous CDS System (CDSS) which will aid in crew health decision-making. The CDSS combines data management aspects of a system that would lead to the autonomy required by NASA-STD-3001, Section 3, Health and Medical Care Standards. The project addresses the ExMC gap Medical-701: We need to increase inflight medical capabilities and identify new capabilities that (a) maximize benefit and/or (b) reduce “costs” on human system/mission/vehicle resources. This architecture recommendation document introduces an integrated data architecture that provides self-sufficient medical care for crews on long-duration spaceflight missions.

ExMC↗
NASA NTRS1992

Propellant production and useful materials: Hardware data from components and the systems

Research activities at the University of Arizona/NASA Space Engineering Research Center are described; the primary emphasis is on hardware development and operation. The research activities are all aimed toward introducing significant cost reductions through the utilization of resources locally available at extraterrestrial sites. The four logical aspects include lunar, Martian, support, and common technologies. These are described in turn. The hardware realizations are based upon sound scientific principles which are used to screen a host of interesting and novel concepts. Small scale feasibility studies are used as the screen to allow only the most promising concepts to proceed. Specific examples include: kg/day-class oxygen plant that uses CO2 as the feed stock, spent stream utilization to produce methane and 'higher' compounds (using hydrogen from a water electrolysis plant), separation of CO from the CO2, reduction of any iron bearing silicate (lunar soils), production of structural components, smart sensors and autonomous controls, and quantitative computer simulation of extraterrestrial plants. The most important feature of all this research continues to be the training of high-quality students for our future in space.

Ramohalli, Kumar↗
NASA NTRSDate not supplied

Biocybernetic Closed-Loop System for Mitigating Hazardous States of Awareness

The past century of passenger flight has seen continuous improvement in aviation safety by the aerospace industry. However, while commercial aviation accident rates have continued to decline, human error-related incident and accident rates remain remarkably constant across all types of aviation (Shappell, et al., 2007). Unfortunately, this level of human error is unacceptable when considering projections for increased traffic volume (FAA, 2009), and is likely to yield more incidents and accidents unless a more complete understanding of operator error is achieved and remediations are implemented. One area of interest highlighted by researchers is Hazardous States of Awareness (HSAs) that can result from deficiencies in the design and inappropriate use of human-machine interfaces. Identifying and mitigating HSAs is critical for reducing operator errors. One promising approach uses psychophysiological measures which enable automated systems to adapt to the operator?s state and modify modes of operation to support optimal human performance (Scerbo, 2007). This paper will survey previous research and describe future directions for the application of psychophysiological measures of operators derived from cortical and autonomic assessment to perform real-time adaptive modulation of human-automation task mode mixes. The authors will present a summary of previous work done at NASA LaRC and Old Dominion University using a Psychophysiologically Adaptive System (PAS) in which the level of automation of the NASA Multi-Attribute Task Battery was modulated based on Engagement Indices derived from the users? electroencephalogram (Pope, Bogart, & Bartolome, 1995; for review see, Scerbo, Freeman, & Mikulka, 2003). Future theoretical and methodological directions for this type of closed-loop research will be discussed. Specifically, the capacity for this type of PAS to maintain effective operator state and to enable validation of candidate physiological indices will be described. Consideration will also be given to critical system characteristics (e.g., engagement indices, methods for invoking changes among system states, individual differences among users, etc.) that have been or still need to be studied. The potential of the PAS approach for interactive system design and prototyping will also be described. Examples of adaptive automation flight deck concepts in recent experiments will be highlighted and discussed.

Chad L Stephens↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Control of a free-flying robot manipulator system

The goal of the research is to develop and test control strategies for a self-contained, free flying space robot. Such a robot would perform operations in space similar to those currently handled by astronauts during extravehicular activity (EVA). The focus of the work is to develop and carry out a program of research with a series of physical Satellite Robot Simulator Vehicles (SRSV's), two-dimensionally freely mobile laboratory models of autonomous free-flying space robots such as might perform extravehicular functions associated with operation of a space station or repair of orbiting satellites. The development of the SRSV and of some of the controller subsystems are discribed. The two-link arm was fitted to the SRSV base, and researchers explored the open-loop characteristics of the arm and thruster actuators. Work began on building the software foundation necessary for use of the on-board computer, as well as hardware and software for a local vision system for target identification and tracking.

Alexander, H.↗
NASA NTRS1997

Feasibility Study For A Spaceborne Ozone/Aerosol Lidar System

Because ozone provides a shield against harmful ultraviolet radiation, determines the temperature profile in the stratosphere, plays important roles in tropospheric chemistry and climate, and is a health risk near the surface, changes in natural ozone layers at different altitudes and their global impact are being intensively researched. Global ozone coverage is currently provided by passive optical and microwave satellite sensors that cannot deliver high spatial resolution measurements and have particular limitations in the troposphere. Vertical profiling DIfferential Absorption Lidars (DIAL) have shown excellent range-resolved capabilities, but these systems have been large, inefficient, and have required continuous technical attention for long term operations. Recently, successful, autonomous DIAL measurements have been performed from a high-altitude aircraft (LASE - Lidar Atmospheric Sensing Experiment), and a space-qualified aerosol lidar system (LITE - Laser In-space Technology Experiment) has performed well on Shuttle. Based on the above successes, NASA and the Canadian Space Agency are jointly studying the feasibility of developing ORACLE (Ozone Research with Advanced Cooperative Lidar Experiments), an autonomously operated, compact DIAL instrument to be placed in orbit using a Pegasus class launch vehicle.

Campbell, Richard E.↗
NASA NTRSDate not supplied

Intelligent Change Detection System: Autonomous Intelligent Machine Agent Model Development

NASA’s significant role in facilitating the harmonious integration of unmanned aircraft systems (UAS), with other aerial vehicles operating in the National Airspace System (NAS), has revealed a need for more advanced technological tools than are being utilized currently. This technology would lend itself to significantly assisting Direct-Action Aviation Personnel (DAAP) with the ingress and egress of UAS operations within the NAS. Providing research findings that would reduce technical barriers, associated with UAS-NAS integration, has been a persistent effort by both NASA and the FAA. One such research effort, pursued by NASA’s Transformative Tools and Technologies – Revolutionary Aviation Mobility (T3-RAM) project, is the development of an autonomous intelligent machine (AIM) agent that would aid DAAP functioning as implemented in remote ground control stations (RGCS). This evolution of the “human-machine” symbiosis, within the aviation environment, is necessary for many reasons. For example, there are projections of large increases to the 864,000 registered UAS and 45,000 aviation operations taking place in the NAS each day. With a data output range from 1 to 20 terabytes per flight or each aerial vehicle, which is projected to have proportional rate increase to that of registered UASs. It is evident, that due to the projected increase of UASs and their generated data, the human-agent’s data managing capabilities will be quickly overwhelmed by the enormous amounts of data emanating in the NAS. The research efforts presented in this paper puts forward results from the development, assessment, and verification of a previously conceptualized AIM-Agent that combats actionable-data (information) errors resulting from the visual perception phenomenon known as “change blindness” (CB). CB has been identified as one of the main culprits of information erroring encountered within the ground control station operator (GCSO) community.

Change Blindness↗