960 research outputs found
Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping
Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD
MODELING OF INNOVATIVE LIGHTER-THAN-AIR UAV FOR LOGISTICS, SURVEILLANCE AND RESCUE OPERATIONS
An unmanned aerial vehicle (UAV) is an aircraft that can operate without the presence of pilots, either through remote control or automated systems. The first part of the dissertation provides an overview of the various types of UAVs and their design features. The second section delves into specific experiences using UAVs as part of an automated monitoring system to identify potential problems such as pipeline leaks or equipment damage by conducting airborne surveys.Lighter-than-air UAVs, such as airships, can be used for various applications, from aerial photography, including surveying terrain, monitoring an area for security purposes and gathering information about weather patterns to surveillance. The third part reveals the applications of UAVs for assisting in search and rescue operations in disaster situations and transporting natural gas. Using PowerSim software, a model of airship behaviour was created to analyze the sprint-and-drift concept and study methods of increasing the operational time of airships while having a lower environmental impact when compared to a constantly switched-on engine. The analysis provided a reliable percentage of finding the victim during patrolling operations, although it did not account for victim behaviour. The study has also shown that airships may serve as a viable alternative to pipeline transportation for natural gas. The technology has the potential to revolutionize natural gas transportation, optimizing efficiency and reducing environmental impact. Additionally, airships have a unique advantage in accessing remote and otherwise inaccessible areas, providing significant benefits in the energy sector. The employment of this technology was studied to be effective in specific scenarios, and it will be worth continuing to study it for a positive impact on society and the environment
MOBILITY21: Strategic Investments for Transportation Infrastructure & Technology
America's transportation infrastructure is the backbone of our economy. A
strong infrastructure means a strong America - an America that competes
globally, supports local and regional economic development, and creates jobs.
Strategic investments in our transportation infrastructure are vital to our
national security, economic growth, transportation safety and our technology
leadership. This document outlines critical needs for our transportation
infrastructure, identifies new technology drivers and proposes strategic
investments for safe and efficient air, ground, rail and marine mobility of
people and goods.Comment: A Computing Community Consortium (CCC) white paper, 4 page
Smart maintenance and inspection of linear assets: An Industry 4.0 approach
Linear assets have linear properties, for instance, similar underlying geometry and characteristics, over a distance. They show specific patterns of continuous inherent deteriorations and failures. Therefore, remedial inspection and maintenance actions will be similar along the length of a linear asset, but because as the asset is distributed over a large area, the execution costs are greater.
Autonomous robots, for instance, unmanned aerial vehicles, pipe inspection gauges, and remotely operated vehicles, are used in different industrial settings in an ad-hoc manner for inspection and maintenance. Autonomous robots can be programmed for repetitive and specific tasks; this is useful for the inspection and maintenance of linear assets.
This paper reviews the challenges of maintaining the linear assets, focusing on inspections. It also provides a conceptual framework for the use of autonomous inspection and maintenance practices for linear assets to reduce maintenance costs, human involvement, etc., whilst improving the availability of linear assets by effective use of autonomous robots and data from different sources
Automatic Pipeline Surveillance Air-Vehicle
This thesis presents the developments of a vision-based system for
aerial pipeline Right-of-Way surveillance using optical/Infrared sensors mounted
on Unmanned Aerial Vehicles (UAV). The aim of research is to develop a highly
automated, on-board system for detecting and following the pipelines; while
simultaneously detecting any third-party interference. The proposed approach
of using a UAV platform could potentially reduce the cost of monitoring and
surveying pipelines when compared to manned aircraft. The main contributions
of this thesis are the development of the image-analysis algorithms, the overall
system architecture and validation of in hardware based on scaled down Test
environment.
To evaluate the performance of the system, the algorithms were coded using
Python programming language. A small-scale test-rig of the pipeline structure,
as well as expected third-party interference, was setup to simulate the
operational environment and capture/record data for the algorithm testing and
validation.
The pipeline endpoints are identified by transforming the 16-bits depth data of
the explored environment into 3D point clouds world coordinates. Then, using
the Random Sample Consensus (RANSAC) approach, the foreground and
background are separated based on the transformed 3D point cloud to extract
the plane that corresponds to the ground. Simultaneously, the boundaries of the
explored environment are detected based on the 16-bit depth data using a
canny detector. Following that, these boundaries were filtered out, after being
transformed into a 3D point cloud, based on the real height of the pipeline for fast and accurate measurements using a Euclidean distance of each boundary
point, relative to the plane of the ground extracted previously. The filtered
boundaries were used to detect the straight lines of the object boundary (Hough
lines), once transformed into 16-bit depth data, using a Hough transform
method. The pipeline is verified by estimating a centre line segment, using a 3D
point cloud of each pair of the Hough line segments, (transformed into 3D).
Then, the corresponding linearity of the pipeline points cloud is filtered within
the width of the pipeline using Euclidean distance in the foreground point cloud.
Then, the segment length of the detected centre line is enhanced to match the
exact pipeline segment by extending it along the filtered point cloud of the
pipeline.
The third-party interference is detected based on four parameters, namely:
foreground depth data; pipeline depth data; pipeline endpoints location in the
3D point cloud; and Right-of-Way distance. The techniques include detection,
classification, and localization algorithms.
Finally, a waypoints-based navigation system was implemented for the air-
vehicle to fly over the course waypoints that were generated online by a
heading angle demand to follow the pipeline structure in real-time based on the
online identification of the pipeline endpoints relative to a camera frame
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