A prototype telerobotic platform for live transmission line maintenance: review of design and development.

This paper reports technical design of a novel experimental test facility, using haptic-enabled teleoperation of robotic manipulators, for live transmission line maintenance. The goal is to study and develop appropriate techniques in repair overhead power transmission lines by allowing linemen to wirelessly guide a remote manipulator, installed on a crane bucket, to execute dexterous maintenance tasks, such as twisting a tie wire around a cable. Challenges and solutions for developing such a system are outlined. The test facility consists of a PHANToM Desktop haptic device (master site), an industrial hydraulic manipulator (slave site) mounted atop a Stewart platform, and a wireless communication channel connecting the master and slave sites. The teleoperated system is tested under different force feedback schemes, while the base is excited and the communication channel is delayed and/or lossy to emulate realistic network behaviors. The force feedback schemes are: virtual fixture, augmentation force and augmented virtual fixture. Performance of each scheme is evaluated under three measures: task completion time, number of failed trials and displacement of the slave manipulator end-effector. The developed test rig has been shown to be successful in performing haptic-enabled teleoperation for live-line maintenance in a laboratory setting. The authors aim at establishing a benchmark test facility for objective evaluation of ideas and concepts in the teleoperation of live-line maintenance tasks

Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

NASA SBIR abstracts of 1992, phase 1 projects

The objectives of 346 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1992 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 346, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1992 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Future grid for a sustainable green airport: meeting the new loads of electric taxiing and electric aircraft.

Lao, Liyun - Associate SupervisorThis thesis proposes a novel electric grid in the airside to meet zero-emission targets for ground movement operations in future airports, as mandated by Aeronautics Research performance target in Europe's (ACARE) FlightPath 2050. The grid delivers power from a renewable energy source through a flexible powerline using an autonomous electric taxiing robot (A-ETR) based on the concept of Energy As A Service (EAAS) for taxiing large aircraft and charging stations for ground vehicles. Four layers of optimisation are required to realise the viability of this new grid. The first optimisation layer involves creating an analytical model of the A-ETR using real-world data from Cranfield University's Airport based solar PV system and its Boeing 737 research aircraft and optimising its performance and efficiency using vehicle-level data-driven machine learning- based optimisation. As a result, the proposed grid achieves zero-emission taxiing and a 91% reduction in fuel compared to a standard baseline. The second layer optimises energy management in the microgrid using machine learning-based forecasting models to predict PV output and optimise charging and discharging cycles of A-ETR batteries to match solar resources and electricity rates. The result shows that the support vector regression (SVR) model best predicted PV output and optimised BESS charge/discharge cycles to achieve zero-emission airport ground movement operations while reducing the microgrid operating costs. However, ground traffic and load profiles increase as the model expands to include commercial airports. Therefore, the third optimisation layer develops a machine learning-based data-driven energy prediction optimisation to ensure microgrid resilience under the increased load. The model employs the Facebook Prophet algorithm to enhance the precision of energy demand prediction for airport ground movement operations across three- time horizons. The results facilitate the generation of reliable forecasts for clean energy production and ground movement energy demand at the airport. A fourth layer of optimisation has been developed to address the limitations of solar PV energy, which depend on the weather and cannot be dispatched, as well iii as the increase in airport traffic. The layer uses wind power and data from a "green" airport to complement PV power output. This model uses the stochastic model predictive control-based cascade feedforward neural network (SMPC- CFFNN) to optimise power flow between the microgrid and RES sources and support V2G capabilities. The results demonstrate that a Zero-emission microgrid for ground movement at green airports can be achieved through optimal power flow management and time optimisation. Reliability and resilience are crucial for a proposed microgrid ecosystem. We consider different network configurations to connect the existing airport grid. Two microgrid architectures, LVAC and LVDC, are compared based on their point of common connections (PCC) to evaluate the technical and economic implications on the airport's distribution network. We verify and validate the model's performance in terms of power quality, short circuit fault levels, system protection requirements, voltage profile, power losses, and equipment/system overloading to determine the optimal architecture. The results indicate that the A-ETR can provide ancillary services to the grid and enable novel emergency response systems. The comprehensive results from the multi-layered system-level optimisation approach adopted in this thesis not only validate the novelty of the proposed study but also serve to provide compelling evidence for its potential to provide viable solutions to the electrification challenges for future green airports by creating an ecosystem between airport ground operations and on-site renewable energy generating sources.PhD in Energy and Powe

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen