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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Human Systems Integration and Automation Issues in Small Unmanned Aerial Vehicles

The goal of this report is to identify Human System Integration (HSI) and automation issues that contribute to improved effectiveness and efficiency in the operation of U.S. military Small Unmanned Aerial Vehicles (SUAVs). HSI issues relevant to SUAV operations are reviewed and observations from field trials are summarized. Short-term improvements are suggested research issues are identified and an overview is provided of automation technologies applicable to future SUAV design.

DRONES↗
NASA NTRS1996

Knowledge Acquisition, Validation, and Maintenance in a Planning System for Automated Image Processing

A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintainting the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. This paper describes a planning application of automated imaging processing and our overall approach to knowledge acquisition for this application.

artificial intelligence image processing knowledge↗
NASA NTRS1993

Automated Precision Orbit Determination for TOPEX/Poseidon with GPS

A highly automated GPS data processing system for the orbit determination of TOPEX/Poseidon is described. The orbit is recovered to an estimated accuracy of better than 4 cm in altitude, 6 cm crosstrack, and 11 cm down track. The RMS postfit residuals on the ionospherically calibrated carrier phase observable are less than 5 mm. The RMS difference over a 4.5-hour overlap period between two 30-hour data arcs is 1 cm in altitude, 5 cm cross track, and 4 cm down track. These results can be obtained within two days of onboard GPS data collection. Most of the data processing for a 30-hour arc of GPS data can be performed on a single workstation in less than 6 hours of CPU time. The estimation scenarios are explained, the automated data processing steps are described, and means to assess solution quality are discussed.

automation GPS data processing orbit determination↗
NASA NTRS1998

Automated Planning and Scheduling for Goal-Based Autonomous Spacecraft

Automated planning and scheduling technology - we'll call it automated planning systems, for the sake of brevity-is applicable to a wide spectrum of spaceflight missions, from those with limited onboard computational capabilities, such as Lunar Prospector, to those with highly sophisticated software, such as Cassini.

automated planning systems command mission operati↗
NASA NTRS2010

Automating the Generation of the Cassini Tour Atlas Database

The Tour Atlas is a large database of geometrical tables, plots, and graphics used by Cassini science planning engineers and scientists primarily for science observation planning. Over time, as the contents of the Tour Atlas grew, the amount of time it took to recreate the Tour Atlas similarly grew--to the point that it took one person a week of effort. When Cassini tour designers estimated that they were going to create approximately 30 candidate Extended Mission trajectories--which needed to be analyzed for science return in a short amount of time--it became a necessity to automate. We report on the automation methodology that reduced the amount of time it took one person to (re)generate a Tour Atlas from a week to, literally, one UNIX command.

Cassini↗
NASA NTRS2012

Composite Wavelet Filters for Enhanced Automated Target Recognition

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

automated target recognition↗
NASA NTRS2011

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

image processing↗
NASA NTRS2009

Exploring the Use of a Test Automation Framework

It is known that software testers, more often than not, lack the time needed to fully test the delivered software product within the time period allotted to them. When problems in the implementation phase of a development project occur, it normally causes the software delivery date to slide. As a result, testers either need to work longer hours, or supplementary resources need to be added to the test team in order to meet aggressive test deadlines. One solution to this problem is to provide testers with a test automation framework to facilitate the development of automated test solutions.

TAF STAF Test Automation↗
NASA NTRS2016

Moving NASA Beyond Low Earth Orbit: Future Human-Automation-Robotic Integration Challenges

This presentation will provide an overview of current human spaceflight operations. It will also describe how future exploration missions will have to adapt and evolve in order to deal with more complex missions and communication latencies. Additionally, there are many implications regarding advanced automation and robotics, and this presentation will outline future human-automation-robotic integration challenges.

human-robotic↗
NASA NTRS2014

Automating the SMAP Ground Data System to Support Lights-Out Operations

The Soil Moisture Active Passive (SMAP) Mission is a first tier mission in NASA's Earth Science Decadal Survey. SMAP will provide a global mapping of soil moisture and its freeze/thaw states. This mapping will be used to enhance the understanding of processes that link the terrestrial water, energy, and carbon cycles, and to enhance weather and forecast capabilities. NASA's Jet Propulsion Laboratory has been selected as the lead center for the development and operation of SMAP. The Jet Propulsion Laboratory (JPL) has an extensive history of successful deep space exploration. JPL missions have typically been large scale Class A missions with significant budget and staffing. SMAP represents a new area of JPL focus towards low cost Earth science missions. Success in this new area requires changes to the way that JPL has traditionally provided the Mission Operations System (MOS)/Ground Data System (GDS) functions. The operation of SMAP requires more routine operations activities and support for higher data rates and data volumes than have been achieved in the past. These activities must be addressed by a reduced operations team and support staff. To meet this challenge, the SMAP ground data system provides automation that will perform unattended operations, including automated commanding of the SMAP spacecraft.

GDS↗
NASA NTRS2017

Spaceport Command and Control System Automated Verification Software Development

For as long as we have walked the Earth, humans have always been explorers. We have visited our nearest celestial body and sent Voyager 1 beyond our solar system1 out into interstellar space. Now it is finally time for us to step beyond our home and onto another planet. The Spaceport Command and Control System (SCCS) is being developed along with the Space Launch System (SLS) to take us on a journey further than ever attempted. Within SCCS are separate subsystems and system level software, each of which have to be tested and verified. Testing is a long and tedious process, so automating it will be much more efficient and also helps to remove the possibility of human error from mission operations. I was part of a team of interns and full-time engineers who automated tests for the requirements on SCCS, and with that was able to help verify that the software systems are performing as expected.

Automation↗
NASA NTRS2016

Automated Commanding of the SMAP Spacecraft Enables Efficient, Reliable, and Responsive Operations

The Soil Moisture Active Passive (SMAP) mission developed and deployed a system to autonomously handle most routine commanding of the observatory. This system of ground software is able to build commands, validate them, and radiate the commands to the spacecraft, all without human interaction. In the case of an off -nominal scenario, the system will abort gracefully and notify the mission operations team of the problem. The system was phased into operations during the first three months of the SMAP mission and handles over 90% of the weekly commanding of the vehicle. The gradual introduction of the automation in flight, along with an extensive test campaign, was instrumental in the success of the software. The automation has enabled substantial efficiencies in operations team staffing and has improved reliability by removing the potential for human error. The system also allows the SMAP project to be more responsive which has shown significant benefits in areas of data latency and science accuracy.

automation↗
NASA NTRS2019

Automated Scenario Generation for Meeting Human-in-the-Loop Simulation Requirements

A two-step automated Multi-Aircraft Control System traffic scenario generation process for Human-in-the-Loop evaluations of air traffic management concepts is described. The first step of the two-step process employs the scenario generation capability currently available in NASA's Air Traffic Management Testbed. The second step refines the scenario by filtering flights from the traffic scenario based on route length, cruise speed, cruise altitude, entry time and the desired ratio of internal to external flights. A solution for achieving the desired ratio of internal to external flights, where internal flights are shorter flights and external flights are longer flights based on a distance threshold, is described. Finally, schedulers are described for shaping the hourly arrival traffic count as a function of time in response to airport capacity constraint or for increasing the traffic demand with respect to the available arrival capacity. Results generated for arrival traffic to the four major airports in the New York Metroplex on a busy day using the two-step procedure are discussed. These results show that traffic scenarios for Multi-Aircraft Control System that meet the Human-in-the-Loop and fast-time simulation requirements can be created automatically following the procedures described in the paper. The automated process will improve the accuracy and efficiency by eliminating the tedious manual process for scenario generation.

Automated Scenario Generation↗
NASA NTRS2018

2nd Workshop of Human-Automation Interaction Considerations for UAS Integration

NASA's Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project (UAS-NAS) and the UAS Executive Committee (EXCOM) Science and Research Panel (SARP) invite you to attend the 2nd Workshop on Human-Automation Interaction Considerations for UAS Integration. A follow on to the workshop hosted by the National Academies of Science, Engineering and Medicine, this two-day workshop aims to tackle two critical issues for UAS integration in the NAS being addressed by NASA and the SARP: control of multiple UAS by a single, or multiple, operators (multi-UAS), and automatic collision avoidance (auto-CA). Attendees will be asked to generate real human-automation architecture and human machine interface solutions for these problems during interactive breakout sessions. Attendance is limited to select government and academia invitees only. This presentation is outlines the objectives of the workshop.

human-automation interaction↗
NASA NTRS2019

Automated Scenario Generation for Meeting Human-In-The-Loop Simulation Requirements

A two-step automated Multi-Aircraft Control System traffic scenario generation process for Human-in-the-Loop evaluations of air traffic management concepts is described. The first step of the two-step process employs the scenario generation capability currently available in NASA's Air Traffic Management Testbed. The second step refines the scenario by filtering flights from the traffic scenario based on route length, cruise speed, cruise altitude, entry time and the desired ratio of internal to external flights. A solution for achieving the desired ratio of internal to external flights, where internal flights are shorter flights and external flights are longer flights based on a distance threshold, is described. Finally, schedulers are described for shaping the hourly arrival traffic count as a function of time in response to airport capacity constraint or for increasing the traffic demand with respect to the available arrival capacity. Results generated for arrival traffic to the four major airports in the New York Metroplex on a busy day using the two-step procedure are discussed. These results show that traffic scenarios for Multi-Aircraft Control System that meet the Human-in-the-Loop and fast-time simulation requirements can be created automatically following the procedures described in the paper. The automated process will improve the accuracy and efficiency by eliminating the tedious manual process for scenario generation.

Automated Scenario Generation↗
NASA NTRS2019

Employing Automated Experimental Evolution to Understand Survival Strategies of Lab-Grown Extremophiles

Experimental evolution (EE) exposes microbes to intentional stressors to improve resistance through artificial mutation. The resulting changes to metabolic pathways, protein structure, and genetic sequences, along with traditional genetic engineering tools, to can help understand the mechanisms of improved tolerance. An automated experimental set-up -- the Automated Adaptive Directed Evolution Chamber (AADEC) -- with minimal scope for human interference was developed at NASA Ames. A second- generation device integrating more real-time biochemical sensors has been developed recently. Added sensors include pH for indicating metabolic products, oxidation-reduction potential (ORP) for indicating available/consumed metabolic energy, dissolved oxygen (DO) for indicating aerobic/anaerobic growth cycles, and electrical conductivity (EC) as an additional indicator of metabolic products. With four additional sensors, the system is biochemically more informative in real-time. More importantly, each sensor parameter can be used as a selection pressure, individually or in combination with others, to artificially create and control inhospitable environments analogous to extremophile habitats for microbial growth in the lab. Potential stressors to be added in the future include thermal, reactive oxygen species, metal-ion concentrations, and varying nutrient availability.

Automated↗
NASA NTRS2020

Deep Space Human-Systems Research Recommendations for Future Human-Automation/Robotic Integration

Appropriate integration between automation and robotics systems and their human operators is essential for future space exploration. The Human Factors and Behavioral Performance Element of NASA’s Human Research Program requires a systematic understanding of the critical human-automation/robotic (HAR) integration, or HARI, design challenges for future space exploration. This document reports the results of a systematic assessment of the spaceflight-relevant HARI technologies and research topics addressing critical gaps in spaceflight-relevant HARI knowledge, and prioritizes research required for successful human performance and HAR integration. We reviewed relevant literature across the past ten years and interviewed ten subject matter experts to investigate the current state of HARI technology, challenges facing development, the state of HARI research across a wide range of fields, and opportunities for advancing the state of the art through directed research. This information was used to identify relevant HARI technologies and research topics, as well as factors to assess relative priority of HARI technologies. We worked with NASA stakeholders to weight the factors relevant to assessing HARI specific technologies. A multi-dimensional trade analysis was performed to objectively score HARI research topics and specific technologies to recommended investment priorities for NASA.

human-automation interaction↗