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A Smart UAV Platform for Railroad Inspection

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Date Issued:
2019
Abstract/Description:
Using quadcopters for analysis of an environment has been an intriguing subject of study recently. The purpose of this work is to develop a fully autonomous UAV platform for Railroad inspection The dynamics of the quadrotor is derived using Euler's and Newton's laws and then linearized around the hover position. A PID controller is designed to control the states of the quadrotor in a manner to effectively follow a vision-based path, using the down facing camera on a Parrot Mambo quadrotor. Using computer vision the distance from the position of the quadrotor to the position of the center of the path was found. Using the yaw controller to minimize this distance was found to be an adequate method of vision-based path following, by keeping the area of interest in the field of view of the camera. The downfacing camera is also simultaneously observing the path to detect defects using machine learning. This technique was able to detect simulated defects on the path with around 90% accuracy.
Title: A Smart UAV Platform for Railroad Inspection.
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Name(s): Debevec, Ryan, Author
Elgohary, Tarek, Committee Chair
Xu, Yunjun, Committee CoChair
Lin, Kuo-Chi, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2019
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Using quadcopters for analysis of an environment has been an intriguing subject of study recently. The purpose of this work is to develop a fully autonomous UAV platform for Railroad inspection The dynamics of the quadrotor is derived using Euler's and Newton's laws and then linearized around the hover position. A PID controller is designed to control the states of the quadrotor in a manner to effectively follow a vision-based path, using the down facing camera on a Parrot Mambo quadrotor. Using computer vision the distance from the position of the quadrotor to the position of the center of the path was found. Using the yaw controller to minimize this distance was found to be an adequate method of vision-based path following, by keeping the area of interest in the field of view of the camera. The downfacing camera is also simultaneously observing the path to detect defects using machine learning. This technique was able to detect simulated defects on the path with around 90% accuracy.
Identifier: CFE0007623 (IID), ucf:52555 (fedora)
Note(s): 2019-08-01
M.S.A.E.
Engineering and Computer Science, Mechanical and Aerospace Engineering
Masters
This record was generated from author submitted information.
Subject(s): A Smart UAV Platform for Railroad Inspection
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007623
Restrictions on Access: public 2019-08-15
Host Institution: UCF

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