Date of Graduation
5-2024
Document Type
Thesis
Degree Name
Bachelor of Science in Industrial Engineering
Degree Level
Undergraduate
Department
Industrial Engineering
Advisor/Mentor
Rainwater, Chase E.
Committee Member/Reader
Cothren, Jackson
Abstract
Unmanned Aerial Vehicles (UAVs), more commonly known as drones, serve various purposes, notably in military applications. Consequently, there arises a need for navigation methods impervious to intercepted signals [1]. Previous research has explored numerous solutions, including machine learning. This paper delves into a specific machine learning approach employing a Convolutional Neural Network (CNN) to discern image locations [2]. It elucidates the conversion of a CNN model between two machine learning libraries and presents results from multiple experiments examining parameters and factors influencing the approach's efficacy. These experiments encompass testing different data sources, image quantities, and processing pipelines to gauge their impact on CNN performance using datasets from the geographically diverse Northwest Arkansas region [3,4].
Keywords
Convolutional Neural Network; Machine Learning; Autonomous Navigation; GPS; PyTorch; TensorFlow
Citation
Jowers, J. (2024). Using Convolutional Neural Networks for Autonomous Drone Navigation. Industrial Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/ineguht/97
