Publication Date
2021
Document Type
Thesis
Committee Members
Yong Pei, Ph.D. (Advisor); Mateen M. Rizki, Ph.D. (Committee Member); Nicholas A. Speranza, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Today there is a large market for Unmanned Aerial Systems. Although most current systems are remotely piloted by operators on the ground, increasingly, many of these systems will use some sort of automatic flight controller to help mitigate new challenges, due to their deployment at growing scale. These challenges include, but are not limited to, shortage of FAA-certified UAS pilots, transmission bandwidth and delay constraints and cyber security threats associated with wireless networking, profitability of operations constrained by energy capacity and efficiency and air dynamics planning, and etc. In order to address these rising challenges, this thesis is a part of an effort to develop and test an on-board Sense and Avoid system for assisted and/or autonomous UAS operations. In particular, this work focuses on applying OpenCV and established computer vision algorithms to implement an object detection capability, which is a critical component of an Adaptive Two-Stage Edge-Centric Sense-and-Avoidance system. Additional efforts were made for integrating this capability into the overall system operations. Furthermore, two implements of the detection system are completed: one in C/C++ and the other in Python with an aim to compare their efficiency. It is found that both implements meet the real-time operation requirements, and experimental studies show little to no difference in processing time for object detection.
Page Count
35
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2021
Copyright
Copyright 2021, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
ORCID ID
0000-0002-8314-1583
