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At least 145 records · Page 8

Identification of Safety Metrics for Airport Surface Operations

A large fraction of safety incidents occurs on the ground during airport surface operations. Although these incidents are mostly non-fatal with a few exceptions, they are high profile incidents that remain a source of concern for the National Transportation Safety Board (NTSB), the Federal Aviation Administration (FAA), major airlines, and other stakeholders of the National Airspace System (NAS). These incidents have historically been mitigated by implementing changes to regulations, policies, and procedures over time. This approach has minimized but not eliminated the risk of occurrences. It is thus important to develop integrated techniques to assess, model, and prevent these incidents by analyzing the risk and likelihood of occurrence and communicating results of the analysis to decision-making personnel who can mitigate and prevent incidents in real time. The research presented in this report builds on prior work of researchers at the NASA Ames Research Center who developed an automated framework, Real-Time Safety Monitoring (RTSM), to enable monitoring and prediction of the safety of the NAS. In the RTSM framework, hazards to flight are translated to safety metrics such as wake vortex encounters or loss of separation, that can be modeled and analyzed offline and also predicted and monitored in real time (online). The intent of this report is to integrate predictable incidents that occur during surface and ground operations into the safety portfolio of the RTSM project by (i) identifying suitable information sources from which ground incidents can be studied, (ii) developing safety metrics correlated with surface operations, and (iii) recommending suitable data sources that can be quantified and used for the computation of pertinent safety metrics.

ground operations↗

Propel: Tools and Methods for Practical Source Code Model Checking

The work reported here is an overview and snapshot of a project to develop practical model checking tools for in-the-loop verification of NASA s mission-critical, multithreaded programs in Java and C++. Our strategy is to develop and evaluate both a design concept that enables the application of model checking technology to C++ and Java, and a model checking toolset for C++ and Java. The design concept and the associated model checking toolset is called Propel. It builds upon the Java PathFinder (JPF) tool, an explicit state model checker for Java applications developed by the Automated Software Engineering group at NASA Ames Research Center. The design concept that we are developing is Design for Verification (D4V). This is an adaption of existing best design practices that has the desired side-effect of enhancing verifiability by improving modularity and decreasing accidental complexity. D4V, we believe, enhances the applicability of a variety of V&V approaches; we are developing the concept in the context of model checking. The model checking toolset, Propel, is based on extending JPF to handle C++. Our principal tasks in developing the toolset are to build a translator from C++ to Java, productize JPF, and evaluate the toolset in the context of D4V. Through all these tasks we are testing Propel capabilities on customer applications.

Mansouri-Samani, Massoud↗

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗

Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)

Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.

Pattern Recognition↗

Space Ground Sensorwebs for Volcano Monitoring

Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, weather, and many other phenomena. New Space ventures have produced significantly greater access to data and miniaturization of sensing has enabled cubesats and smallsats to deliver data of outstanding resolution. The advent of the internet of things has produced incredible amounts of relevant terrestrial data as well. Artificial Intelligence offers the potential to automate both data interpretation and resource allocation to best allocate sensing assets. We describe efforts to build and experiment with such “sensorweb” systems and offer some direction for the future sensorweb observation systems.

Zuleta, Ignacio↗

Beyond BioSentinel: Iterative Development of Automated Microfluidics

NASA Ames has flown a series of Bio-CubeSats that performed biology experiments supported by automated fluidic systems. Since Genesat-1 in 2006, these payloads have increased in complexity and functionality, building upon previous successes, and applying lessons learned. BioSentinel was the most recent of this series, launched into heliocentric orbit onboard Artemis-1 in 2022. This presentation will discuss how the fluidic technology developed for these Bio-CubeSat missions, including the multi-layer polycarbonate manifolds at the heart of the BioSentinel BioSensor, have spurred the development of several additional projects. Most directly is the modified BioSensor that will be a part of LEIA, which will perform its Lunar biology experiment onboard a Commercial Lunar Payload Services lander. Several search-for-life manifolds have been designed to prepare samples from icy moons for downstream analyses. Two early career Polaris projects are developing fluidics to perform genetic sequencing on samples from multigenerational cell culture and to extract and quantify target miRNAs to support astronaut radiation health assessment. Improving the readiness of these systems has been accelerated by adopting the established flight heritage and microgravity-compatibility of the Bio-CubeSat fluidic hardware and designs, while focusing development efforts on the integration of novel functionalities and components.

microfluidics↗

Automated Fiber Placement Through Thickness Defect Stacking Optimization

In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.

AFP↗

Tools for Designing, Evaluating, and Certifying NextGen Technologies and Procedures: Automation Roles and Responsibilities

Barbara Kanki from NASA Ames Research Center will discuss research that focuses on the collaborations between pilots, air traffic controllers and dispatchers that will change in NextGen systems as automation increases and roles and responsibilities change. The approach taken by this NASA Ames team is to build a collaborative systems assessment template (CSAT) based on detailed task descriptions within each system to establish a baseline of the current operations. The collaborative content and context are delineated through the review of regulatory and advisory materials, policies, procedures and documented practices as augmented by field observations and interviews. The CSAT is developed to aid the assessment of key human factors and performance tradeoffs that result from considering different collaborative arrangements under NextGen system changes. In theory, the CSAT product may be applied to any NextGen application (such as Trajectory Based Operations) with specified ground and aircraft capabilities.

Kanki, Barbara G.↗

Machine Learning for Extravehicular Mobility Unit (EMU) Glove Inspections

The Extravehicular Mobility Unit (EMU) Glove Machine Learning Inspection project utilizes machine learning to expedite the inspection, analysis, and recommendation for continued use of space suit gloves post spacewalks. Today, ISS glove photos are individually reviewed by a team of experts to determine the conditions of space suit gloves. For this project the Microsoft Azure platform is used to perform Automated Machine Learning (AutoML) to detect issues with tagged images from previous Extravehicular Activities (EVA’s) to build a predictive model. The model analyzes a test image and deems the glove GO or NO-GO for additional EVA’s. The goal for this ML project is to decrease the time spent reviewing images by ground personnel and crewmembers in high frequency EVA locations such as the Moon and Mars. For destinations such as the Moon and Mars the goal is to give crew autonomy in determining glove conditions with limited support from Earth. This paper will outline the results to date and future work needed to expand the capability for in-situ recommendations.

EVA↗

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗

Preliminary Radiation Testing of a State-of-the-Art Commercial 14nm CMOS Processor - System-on-a-Chip

Hardness assurance test results of Intel state-of-the-art 14nm Broadwell U-series processor System-on-a-Chip (SoC) for total dose are presented, along with first-look exploratory results from trials at a medical proton facility. Test method builds upon previous efforts by utilizing commercial laptop motherboards and software stress applications as opposed to more traditional automated test equipment (ATE).

14nm↗

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

Davila, Carlos G.↗

Discrete Assemblers Utilizing Conventional Motion Systems

An alternative to additive manufacturing is disclosed, introducing an end-to-end workflow in which discrete building blocks are reversibly joined to produce assemblies called digital materials. Described is the design of the bulk-material building blocks and the devices that are assembled from them. Detailed is the design and implementation of an automated assembler, which takes advantage of the digital material structure to avoid positioning errors within a large tolerance. To generate assembly sequences, a novel CAD/CAM workflow is described for designing, simulating, and assembling digital materials. The structures assembled using this process have been evaluated, showing that the joints perform well under varying conditions and that the assembled structures are functionally precise.

Langford, William Kai↗

Artificial intelligence and design: Opportunities, research problems and directions

The issues of industrial productivity and economic competitiveness are of major significance in the U.S. at present. By advancing the science of design, and by creating a broad computer-based methodology for automating the design of artifacts and of industrial processes, we can attain dramatic improvements in productivity. It is our thesis that developments in computer science, especially in Artificial Intelligence (AI) and in related areas of advanced computing, provide us with a unique opportunity to push beyond the present level of computer aided automation technology and to attain substantial advances in the understanding and mechanization of design processes. To attain these goals, we need to build on top of the present state of AI, and to accelerate research and development in areas that are especially relevant to design problems of realistic complexity. We propose an approach to the special challenges in this area, which combines 'core work' in AI with the development of systems for handling significant design tasks. We discuss the general nature of design problems, the scientific issues involved in studying them with the help of AI approaches, and the methodological/technical issues that one must face in developing AI systems for handling advanced design tasks. Looking at basic work in AI from the perspective of design automation, we identify a number of research problems that need special attention. These include finding solution methods for handling multiple interacting goals, formation problems, problem decompositions, and redesign problems; choosing representations for design problems with emphasis on the concept of a design record; and developing approaches for the acquisition and structuring of domain knowledge with emphasis on finding useful approximations to domain theories. Progress in handling these research problems will have major impact both on our understanding of design processes and their automation, and also on several fundamental questions that are of intrinsic concern to AI. We present examples of current AI work on specific design tasks, and discuss new directions of research, both as extensions of current work and in the context of new design tasks where domain knowledge is either intractable or incomplete. The domains discussed include Digital Circuit Design, Mechanical Design of Rotational Transmissions, Design of Computer Architectures, Marine Design, Aircraft Design, and Design of Chemical Processes and Materials. Work in these domains is significant on technical grounds, and it is also important for economic and policy reasons.

Amarel, Saul↗

Display and Automation Considerations for the Airborne Collision Avoidance System Xu

In this paper we examine several display and automation considerations of a collision avoidance system that is currently under development: the Airborne Collision Avoidance System (ACAS) Xu. This study builds on previous work conducted as part of NASA’s Unmanned Aircraft Systems (UAS) Integration into the National Airspace System (NAS) project. ACAS Xu represents the next-generation successor to the Traffic Alert and Collision Avoidance System (TCAS II), wherein the Xu variant is intended for UAS applications. Whereas TCAS II exclusively issues RAs in the vertical dimension, a major distinction between ACAS Xu and previous collision avoidance (CA) systems is the introduction of horizontal and “blended” RAs (i.e., RAs with both horizontal and vertical components). This present work was conducted as an engineering analysis involving two parts. In Part 1, a two-by-two, within-subjects study was performed that manipulated how RAs were presented to a pilot situated at a UAS ground control station. Five participants experienced four experimental trials in which text and aural alerting characteristics were manipulated. In Part 2, another five participants experienced four trials in which the levels of automation were manipulated with regard to the CA and return-to-course (RTC) tasks. The results for Part 1 found no effect of display or alerting configuration on pilot performance. However, it was discovered that pilot response time to RAs greatly depended on the RA type. In particular, pilots were quicker to respond to vertical RAs (M = 4.52 seconds) than horizontal (M = 7.42 seconds) and blended (M = 9.68 seconds) RAs in which both dimensions were issued simultaneously. For Part 2 of the study, pilots found both auto-CA and auto-RTC functions equally useful. Most pilots were comfortable with the automation, however responses were mixed. Three of five participants indicated high levels of comfort with the auto-CA function, while two rated their comfort as low. Pilots’ comfort for the auto-RTC functionality was slightly higher: four out of five pilots gave high ratings, while one pilot gave a low rating. Overall, pilots ordinally ranked their preference for automated functions as auto-CA together with auto-RTC (when an aural alert announces a change between CA and RTC states), auto-CA, and auto-CA and RTC (without the aural state-change announcement). Recommendations for improving the display of automation are also discussed.

collision avoidance↗

Display and Automation Considerations for the Airborne Collision Avoidance System Xu

In this presentation we examine several display and automation considerations of a collision avoidance system that is currently under development: the Airborne Collision Avoidance System (ACAS) Xu. This study builds on previous work conducted as part of NASA’s Unmanned Aircraft Systems (UAS) Integration into the National Airspace System (NAS) project. ACAS Xu represents the next-generation successor to the Traffic Alert and Collision Avoidance System (TCAS II), wherein the Xu variant is intended for UAS applications. Whereas TCAS II exclusively issues RAs in the vertical dimension, a major distinction between ACAS Xu and previous collision avoidance (CA) systems is the introduction of horizontal and “blended” RAs (i.e., RAs with both horizontal and vertical components). This present work was conducted as an engineering analysis involving two parts. In Part 1, a two-by-two, within-subjects study was performed that manipulated how RAs were presented to a pilot situated at a UAS ground control station. Five participants experienced four experimental trials in which text and aural alerting characteristics were manipulated. In Part 2, another five participants experienced four trials in which the levels of automation were manipulated with regard to the CA and return-to-course (RTC) tasks. The results for Part 1 found no effect of display or alerting configuration on pilot performance. However, it was discovered that pilot response time to RAs greatly depended on the RA type. In particular, pilots were quicker to respond to vertical RAs (M = 4.52 seconds) than horizontal (M = 7.42 seconds) and blended (M = 9.68 seconds) RAs in which both dimensions were issued simultaneously. For Part 2 of the study, pilots found both auto-CA and auto-RTC functions equally useful. Most pilots were comfortable with the automation, however responses were mixed. Three of five participants indicated high levels of comfort with the auto-CA function, while two rated their comfort as low. Pilots’ comfort for the auto-RTC functionality was slightly higher: four out of five pilots gave high ratings, while one pilot gave a low rating. Overall, pilots ordinally ranked their preference for automated functions as auto-CA together with auto-RTC (when an aural alert announces a change between CA and RTC states), auto-CA, and auto-CA and RTC (without the aural state-change announcement). Recommendations for improving the display of automation are also discussed.

collision avoidance↗

An integrated computer system for preliminary design of advanced aircraft.

A progress report is given on the first phase of a research project to develop a system of Integrated Programs for Aerospace-Vehicle Design (IPAD) which is intended to automate to the largest extent possible the preliminary and detailed design of advanced aircraft. The approach used is to build a pilot system and simultaneously to carry out two major contractual studies to define a practical IPAD system preparatory to programing. The paper summarizes the specifications and goals of the IPAD system, the progress to date, and any conclusion reached regarding its feasibility and scope. Sample calculations obtained with the pilot system are given for aircraft preliminary designs optimized with respect to discipline parameters, such as weight or L/D, and these results are compared with designs optimized with respect to overall performance parameters, such as range or payload.

Fulton, R. E.↗