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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

The Role of Synthetic Biology in NASA's Missions

The time has come to for NASA to exploit synthetic biology in pursuit of its missions, including aeronautics, earth science, astrobiology and most notably, human exploration. Conversely, NASA advances the fundamental technology of synthetic biology as no one else can because of its unique expertise in the origin of life and life in extreme environments, including the potential for alternate life forms. This enables unique, creative "game changing" advances. NASA's requirement for minimizing upmass in flight will also drive the field toward miniaturization and automation. These drivers will greatly increase the utility of synthetic biology solutions for military, health in remote areas and commercial purposes. To this end, we have begun a program at NASA to explore the use of synthetic biology in NASA's missions, particular space exploration. As part of this program, we began hosting an iGEM team of undergraduates drawn from Brown and Stanford Universities to conduct synthetic biology research at NASA Ames Research Center. The 2011 team (http://2011.igem.org/Team:Brown-Stanford) produced an award-winning project on using synthetic biology as a basis for a human Mars settlement.

synthetic biology↗
NASA NTRS2017

Qualitative Future Safety Risk Identification an Update

The purpose of this report is to document the results of a high-level qualitative study that was conducted to identify future aviation safety risks and to assess the potential impacts to the National Airspace System (NAS) of NASA Aviation Safety research on these risks. Multiple external sources (for example, the National Transportation Safety Board, the Flight Safety Foundation, the National Research Council, and the Joint Planning and Development Office) were used to develop a compilation of future safety issues risks, also referred to as future tall poles. The primary criterion used to identify the most critical future safety risk issues was that the issue must be cited in several of these sources as a safety area of concern. The tall poles in future safety risk, in no particular order of importance, are as follows: Runway Safety, Loss of Control In Flight, Icing Ice Detection, Loss of Separation, Near Midair Collision Human Fatigue, Increasing Complexity and Reliance on Automation, Vulnerability Discovery, Data Sharing and Dissemination, and Enhanced Survivability in the Event of an Accident.

future aviation safety risk↗
NASA NTRS2017

Autonomous Task Management and Decision Support Tools

For some time aircraft manufacturers and researchers have been pursuing mechanisms for reducing crew workload and providing better decision support to the pilots, especially during non-normal situations. Some previous attempts to develop task managers or pilot decision support tools have not resulted in robust and fully functional systems. However, the increasing sophistication of sensors and automated reasoners, and the exponential surge in the amount of digital data that is now available create a ripe environment for the development of a robust, dynamic, task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This presentation will discuss the many significant challenges and issues associated with the development and functionality of such a system for use on the aircraft flight deck.

decision support↗
NASA NTRS2019

The Role of Synthetic Biology in NASA's Missions

The time has come to for NASA to exploit the nascent field of synthetic biology in pursuit of its mission, including aeronautics, earth science, astrobiology and notably, human exploration. Conversely, NASA advances the fundamental technology of synthetic biology as no one else can because of its unique expertise in the origin of life and life in extreme environments, including the potential for alternate life forms. This enables unique, creative "game changing" advances. NASA's requirement for minimizing upmass in flight will also drive the field toward miniaturization and automation. These drivers will greatly increase the utility of synthetic biology solutions for military, health in remote areas and commercial purposes. To this end, we have begun a program at NASA to explore the use of synthetic biology in NASA's missions, particularly space exploration. As part of this program, we began hosting an iGEM team of undergraduates drawn from Brown and Stanford Universities to conduct synthetic biology research at NASA Ames Research Center. The 2011 team (http://2011.igem.org/Team:Brown-Stanford) produced an award-winning project on using synthetic biology as a basis for a human Mars settlement and the 2012 team has expanded the use of synthetic biology to estimate the potential for life in the clouds of other planets (http://2012.igem.org/Team:Stanford-Brown; http://www.calacademy.org/sciencetoday/igem-competition/). More recent projects from the Stanford-Brown team have expanded our ideas of how synthetic biology can aid NASA's missions from "Synthetic BioCommunication" (http://2013.igem.org/Team:Stanford-Brown) to a "Biodegradable UAS (drone)" in collaboration with Spelman College (http://2014.igem.org/Team:StanfordBrownSpelman#SBS%20iGEM) and most recently, "Self-Folding Origami" (http://2015.igem.org/Team:Stanford-Brown), the winner of the 2015 award for Manufacturing.

Rothschild, Lynn J.↗
NASA NTRSDate not supplied

On the Probabilistic Analysis of Neural Networks

Neural networks are powerful tools for automated decision-making,seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale,reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.We investigate here the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We demonstrate the potential of the proposed technique in a case study involving the analysis of ACAS-Xu, a collision avoidance system for unmanned aircraft control.

neural nets↗
NASA NTRS2006

Integrated human-robotic missions to the Moon and Mars : mission operations design implications

For most of the history of space exploration, human and robotic programs have been independent, and have responded to distinct requirements. The NASA Vision for Space Exploration calls for the return of humans to the Moon, and the eventual human exploration of Mars; the complexity of this range of missions will require an unprecedented use of automation and robotics in support of human crews. The challenges of human Mars missions, including roundtrip communications time delays of 6 to 40 minutes, interplanetary transit times of many months, and the need to manage lifecycle costs, will require the evolution of a new mission operations paradigm far less dependent on real-time monitoring and response by an Earthbound operations team. Robotic systems and automation will augment human capability, increase human safety by providing means to perform many tasks without requiring immediate human presence, and enable the transfer of traditional mission control tasks from the ground to crews. Developing and validating the new paradigm and its associated infrastructure may place requirements on operations design for nearer-term lunar missions. The authors, representing both the human and robotic mission operations communities, assess human lunar and Mars mission challenges, and consider how human-robot operations may be integrated to enable efficient joint operations, with the eventual emergence of a unified exploration operations culture.

Lee, Young↗
NASA NTRSDate not supplied

Harnessing Large Language Models for Scientific Endeavors

The rapid proliferation of Large Language Models (LLMs) such as GPT, Bard, and Llama has revolutionized various sectors, including the scientific community. These models, with their potential to automate and augment tasks, are increasingly being recognized as both a valuable asset and a potential challenge in the realm of scientific research and data management. However, the current LLMs, primarily trained on general corpora, exhibit a limited understanding of scientific concepts and terminologies due to the lack of scientific corpus in their training data. Recognizing this gap, several groups are now advocating for the development of LLMs specifically tailored for scientific applications. A notable initiative in this direction is the Large Language Model effort initiated by NASA's CSDO. This endeavor aims to align LLM efforts across NASA’s Science Mission Directorate, develop a science-specific corpus and validation test set for model training, and create an encoder-only model for various downstream tasks. Moreover, the initiative also plans to develop a decoder-only model to explore the potential benefits and risks associated with a generative LLM for science. Lastly, the project aims to create a science evaluation suite, encompassing various categories of downstream scientific tasks, to serve as a benchmark for assessing the value of any LLM for future use. This presentation will provide an overview and current status of this ongoing initiative, highlighting its potential to reshape the use of LLMs in the scientific domain.

Rahul Ramachandran↗
DOE OSTI2025

Continuous manufacturing of carbon foam structures at atmospheric pressure

A method of manufacturing essentially crack-free carbon foam structures and carbon foam structures made in single-piece flow through a continuous kiln under inert atmosphere at atmospheric pressure. This process greatly reduces the cost to manufacture by reducing manufacturing time and increasing the ability to integrate automation equipment.

Olson, III, Rudolph Andrew↗
DOE OSTI2024

Assessment of Different Application Grades of Post-Consumer Recycled (PCR) Polyolefins from Material Recovery Facilities (MRFs) in the United States

The recycling of plastics has been gaining traction in the following years as many companies, organizations, and governments are pushing toward a more circular economy. However, plastic recycling rates are still small compared to those of plastic production, especially for polyolefins, such as high-density polyethylene (HDPE) and polypropylene (PP). With the goal of finding applications for recycled plastic resin, recyclers can face some challenges associated with the heterogeneity in the polymer properties of plastic waste streams, which could hinder the full potential of recycling due to the lack of a consistent feedstock. Here, this study assessed how an additional sorting step of recycled HDPE and PP streams, separating them into two categories based on the previous application, impacts the recycled polymer properties, such as physical, molecular, and thermal. It was demonstrated that manually separating HDPE articles previously processed by extrusion blow molding (EBM) or injection molding (IM) and PP articles by thermoforming (TF) or IM could yield recycled resins with more consistent properties, considering the significant differences between them. This highlights that an additional sorting step would allow recyclers to target more premium or currently unattainable plastic processing applications with postconsumer recycled (PCR) resins, which could significantly promote an increase in plastic recycling rates. Nevertheless, the development of new technologies is needed to automate this extra sorting step, as with the increase in scale manual sortation could not be feasible and cost-effective, reducing the attractiveness of using PCR resins in comparison to virgin ones.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Vision-Guided Automation and Assistance

Robotic automation and assistance is an excellent way to increase efficiency and safety when working in a potentially hazardous workspace. However, many traditional automation approaches rely on the precise placement of items to be manipulated. The scope of such automated tasks is also limited to the manipulation of one type of object since there is no way to distinguish between different objects. The addition of a vision pipeline, where robot vision is used to localize and recognize objects, can allow the robot to perform more complex tasks in a variable workspace. In my vision pipeline, a depth camera is used to generate a 3D point cloud of the workspace. Objects to be manipulated are isolated in the point cloud by filtering out shared surfaces and using Euclidean cluster extraction to group clusters of points. The locations of the objects are then estimated from the point cloud of the isolated objects. The localization of objects with robot vision allows for variable workspaces, reducing the overhead associated with requiring all objects to be precisely placed in the workspace. For object recognition, deep learning is used to train a neural network that classifies objects from the raw point clouds. More complex tasks that require multiple objects to be manipulated differently can be achieved through the vision pipeline. Through its ability to visualize the workspace, robot vision greatly improves efficiency and safety of automated and assistive tasks.

42 ENGINEERING↗
DOE OSTI2026

Interactions Between Climate Policy and Technology-influenced Travel Behavior: Mitigating Induced Demand from CACC

Advances in vehicle technology have influenced the development of automated vehicle systems, where vehicles that do not require human intervention are already deployed in the roadway networks. While these advances are proved to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional-level impacts on mobility, land use, energy consumption, and emissions. This study proposes a multi-model approach to analyze the effect of vehicle automation and deep decarbonization policies over a period from 2020 to 2040 in Austin, Texas. We use the Global Change Analysis Model (GCAM) to develop internally the scenarios that are then passed to the SMART Mobility modeling workflow, a large-scale simulation framework combining the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model and the UrbanSim land use simulator. Results suggest that the introduction of vehicles with advanced automation could increase fuel consumption when no decarbonization policies are implemented. Also, advances in vehicle technology research and development could lead to a decline in energy use in the long-term. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation. Finally, our analysis indicates the relevance of introducing land use processes in longterm vehicle automation studies.

land use↗
DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗
NASA NTRS1984

Cooperative control - The interface challenge for men and automated machines

The research issues associated with the increasing autonomy and independence of machines and their evolving relationships to human beings are explored. The research, conducted by Langley Research Center (LaRC), will produce a new social work order in which the complementary attributes of robots and human beings, which include robots' greater strength and precision and humans' greater physical and intellectual dexterity, are necessary for systems of cooperation. Attention is given to the tools for performing the research, including the Intelligent Systems Research Laboratory (ISRL) and industrial manipulators, as well as to the research approaches taken by the Automation Technology Branch (ATB) of LaRC to achieve high automation levels. The ATB is focusing on artificial intelligence research through DAISIE, a system which tends to organize its environment into hierarchical controller/planner abstractions.

Hankins, W. W., III↗
NASA NTRS1993

Automated System for Early Breast Cancer Detection in Mammograms

The increasing demand on mammographic screening for early breast cancer detection, and the subtlety of early breast cancer signs on mammograms, suggest an automated image processing system that can serve as a diagnostic aid in radiology clinics. We present a fully automated algorithm for detecting clusters of microcalcifications that are the most common signs of early, potentially curable breast cancer. By using the contour map of the mammogram, the algorithm circumvents some of the difficulties encountered with standard image processing methods. The clinical implementation of an automated instrument based on this algorithm is also discussed.

Bankman, Isaac N.↗
NASA NTRS2004

Secure, Mobile, Wireless Network Technology Designed, Developed, and Demonstrated

The inability to seamlessly disseminate data securely over a high-integrity, wireless broadband network has been identified as a primary technical barrier to providing an order-of-magnitude increase in aviation capacity and safety. Secure, autonomous communications to and from aircraft will enable advanced, automated, data-intensive air traffic management concepts, increase National Air Space (NAS) capacity, and potentially reduce the overall cost of air travel operations. For the first time ever, secure, mobile, network technology was designed, developed, and demonstrated with state-ofthe- art protocols and applications by a diverse, cooperative Government-industry team led by the NASA Glenn Research Center. This revolutionary technology solution will make fundamentally new airplane system capabilities possible by enabling secure, seamless network connections from platforms in motion (e.g., cars, ships, aircraft, and satellites) to existing terrestrial systems without the need for manual reconfiguration. Called Mobile Router, the new technology autonomously connects and configures networks as they traverse from one operating theater to another. The Mobile Router demonstration aboard the Neah Bay, a U.S. Coast Guard vessel stationed in Cleveland, Ohio, accomplished secure, seamless interoperability of mobile network systems across multiple domains without manual system reconfiguration. The Neah Bay was chosen because of its low cost and communications mission similarity to low-Earth-orbiting satellite platforms. This technology was successfully advanced from technology readiness level (TRL) 2 (concept and/or application formation) to TRL 6 (system model or prototype demonstration in a relevant environment). The secure, seamless interoperability offered by the Mobile Router and encryption device will enable several new, vehicle-specific and systemwide technologies to perform such things as remote, autonomous aircraft performance monitoring and early detection and mitigation of potential equipment malfunctions. As an additional benefit, team advancements were incorporated into open standards, ensuring technology transfer. Low-cost, commercial products incorporating the new technology are already available. Furthermore, these products are fully interoperable with legacy network technology equipment currently being used throughout the world.

Ivancic, William D.↗
NASA NTRS1992

Software design for automated assembly of truss structures

Concern over the limited intravehicular activity time has increased the interest in performing in-space assembly and construction operations with automated robotic systems. A technique being considered at LaRC is a supervised-autonomy approach, which can be monitored by an Earth-based supervisor that intervenes only when the automated system encounters a problem. A test-bed to support evaluation of the hardware and software requirements for supervised-autonomy assembly methods was developed. This report describes the design of the software system necessary to support the assembly process. The software is hierarchical and supports both automated assembly operations and supervisor error-recovery procedures, including the capability to pause and reverse any operation. The software design serves as a model for the development of software for more sophisticated automated systems and as a test-bed for evaluation of new concepts and hardware components.

Herstrom, Catherine L.↗
NASA NTRS2001

Alternative Approaches to Mission Control Automation at NASA's Goddard Space Flight Center

To meet its objective of reducing operations costs without incurring a corresponding increase in risk, NASA is seeking new methods to automate mission operations. This paper examines the state of the art in automating ground operations for space missions. A summary of available technologies and methods for automating mission operations is provided. Responses from interviews with several space mission FOTs (Flight Operations Teams) to assess the degree and success of those technologies and methods implemented are presented. Mission operators that were interviewed approached automation using different tools and methods resulting in varying degrees of success - from nearly completely automated to nearly completely manual. Two key criteria for successful automation are the active participation of the FOT in the planning, designing, testing, and implementation of the system and the relative degree of complexity of the mission.

Rackley, Michael↗
NASA NTRS2009

Effects of Automation Types on Air Traffic Controller Situation Awareness and Performance

The Joint Planning and Development Office has proposed the introduction of automated systems to help air traffic controllers handle the increasing volume of air traffic in the next two decades (JPDO, 2007). Because fully automated systems leave operators out of the decision-making loop (e.g., Billings, 1991), it is important to determine the right level and type of automation that will keep air traffic controllers in the loop. This study examined the differences in the situation awareness (SA) and collision detection performance of individuals when they worked with information acquisition, information analysis, decision and action selection and action implementation automation to control air traffic (Parasuraman, Sheridan, & Wickens, 2000). When the automation was unreliable, the time taken to detect an upcoming collision was significantly longer for all the automation types compared with the information acquisition automation. This poor performance following automation failure was mediated by SA, with lower SA yielding poor performance. Thus, the costs associated with automation failure are greater when automation is applied to higher order stages of information processing. Results have practical implications for automation design and development of SA training programs.

Sethumadhavan, A.↗