Abstract

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.

Notes

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Graduation Date

2019

Semester

Summer

Advisor

Elgohary, Tarek

Degree

Master of Science in Aerospace Engineering (M.S.A.E.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Aerospace Engineering; Space System Design and Engineering

Format

application/pdf

Identifier

CFE0007623

URL

http://purl.fcla.edu/fcla/etd/CFE0007623

Language

English

Release Date

8-15-2019

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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