Oruc, O., & Singleton, E., & Williams, A. B., & Skenes, K. (2025, March), Development Of A New Course: Control Design for Autonomous Vehicles Using A Quadcopter As The Learning Platform  Paper presented at 2025 ASEE Southeast Conference , Mississippi State University, Mississippi. 10.18260/1-2--54157

\bibitem{asee_peer_54157}
  Oruc, O., \& Singleton, E., \& Williams, A. B., \& Skenes, K. (2025, March), \emph{Development Of A New Course: Control Design for Autonomous Vehicles Using A Quadcopter As The Learning Platform}
  Paper presented at 2025 ASEE Southeast Conference , Mississippi State University, Mississippi.
  10.18260/1-2--54157

Oguzhan Oruc, Eva Singleton, Andrew B. Williams, and Kevin Skenes.  "Development Of A New Course: Control Design for Autonomous Vehicles Using A Quadcopter As The Learning Platform".  2025 ASEE Southeast Conference , Mississippi State University, Mississippi, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/54157 Internet.  15 Apr, 2025

\bibitem{asee_peer_54157}
  Oguzhan Oruc, Eva Singleton, Andrew B. Williams, and Kevin Skenes.
  "Development Of A New Course: Control Design for Autonomous Vehicles Using A Quadcopter As The Learning Platform".
  \emph{2025 ASEE Southeast Conference , Mississippi State University, Mississippi, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/54157 Internet.  15 Apr, 2025

@INPROCEEDINGS{asee_peer_54157
author = "Oguzhan Oruc, Eva Singleton, Andrew B. Williams, and Kevin Skenes"
title = "Development Of A New Course: Control Design for Autonomous Vehicles Using A Quadcopter As The Learning Platform"
booktitle = "2025 ASEE Southeast Conference "
year = "2025"
month = "March"
address = "Mississippi State University, Mississippi"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/54157}
number = {10.18260/1-2--54157}
}

TY  - CPAPER
AB  - Modeling, simulation, control system design, navigation, and guidance of autonomous vehicles
(AVs) have become highly sought research areas in the mechanical engineering community 
due to the advancement in microelectronics, computational technologies, and machine perception.
Because of the increasing popularity of self-driving cars, autonomous vehicle refers to
self-driving cars in public perception although it covers a wider research area. AVs can be
examined under, aerial, underwater, surface, and terrestrial robotics. Unmanned Aerial Vehicles
(UAVs), Autonomous Underwater Vehicles (AUVs), Autonomous Surface Vehicles (ASVs), and
Unmanned Ground Vehicles (UGVs) are the respective examples of the AVs in robotics.
Recent developments and advanced research findings in robotics enabled the use of UAVs in
commercial, military, and scientific areas for many different purposes such as inspection and
maintenance of urban and transportation infrastructure, surveillance and security, traffic control
systems, law enforcement, mapping and surveying large areas. A rapid advancement in AUVs and
ASVs were achieved with the developments in oceanic control, guidance, and navigation
technologies. UAVs, AUVs, and ASVs are used in military, commercial, and research areas
such as unexploded ordnance (UXO) detection, oil and gas pipeline monitoring, sea-floor
mapping, naval surveillance and coastal security, hydrographic-oceanographic surveys, marine
biology, and environmental monitoring. Advancements in tracking control, specifically steering
and longitudinal control, satellite communication, and localization technology provided a fast
development in UGV design and manufacturing. UGVs are also used in military and
commercial areas, interplanetary research, and transportation of humans and commercial
goods.
The increasing interest from commercial, scientific, and military investors and expanding
application areas of AV research, development and manufacturing attract engineers from almost
all disciplines. Considering its revolutionary effects on vast engineering areas such as automation
and robotics, logistics, aviation, healthcare, mapping and surveying,
artificial intelligence (AI) also revolutionizes AVs in terms of intelligent control, visual
recognition, and object detection, environmental perception, simultaneous localization and
mapping (SLAM), guidance and navigation and mission planning. AI involvement with AV
research will constantly provoke curious learners and attract future engineers. One of the most
important responsibilities of the engineering education providers is preparing engineers of the
future for the jobs of tomorrow. Therefore, a course about AV fundamentals such as
mathematical modeling and control system design is crucial to include in engineering
curricula.
In this study, the authors described the development of a new undergraduate elective course
(Advanced Topics in Engineering-400 level) module in modeling and control system design of
AVs, which is offered in the Mechanical Engineering Department of a national university.
Considering the multi-disciplinary nature of the AV study, the course offered to undergraduate
students studying Mechanical Engineering, Electrical Engineering, Computer Engineering and
Computer Science majors. The structure of the course comprises 3 main components: theory,
simulation, and application.
The theory component aims to review the necessary mathematics related to the control system
design and a brief review of the software packages to be used in simulations and applications.
Then, it covers the topics to obtain linear and nonlinear 6 degrees of freedom (DOF)
mathematical models of a UAV. For navigation and guidance purposes, reference coordinate
frames, principal rotations, and the transformation between coordinate reference frames are
extensively taught. Three different control strategies, angular velocity, attitude/altitude and
translational position controller are also taught. Weekly research paper reviews are required to
connect students with the current, trends, sources, and development in the area.
The simulation component is taught in parallel with the theory. After every theory component, a
simulation study is performed by using numerical simulation software to reinforce the subjects
taught and to see the effects of them on the AVs.
To apply the theory that is taught during the course and to compare simulation studies to real-life control, navigation and guidance problems, a UAV, a quadcopter in particular, is used as the
learning and testing platform. The mathematical model parameters, control algorithms, and all the
simulation scenarios are to be applied to the quadcopter.
The reasons behind choosing a quadcopter as the testing and learning platform are that they have
open hardware-open software, reachable in terms of finances, versatile, usability in various areas
and vast amounts of research material due to the high interest in the scientific area.
The unique contributions of this course to the engineering education are as follows,
• Supporting undergraduate research with mathematical control theory in AV research area.
• Providing the necessary software knowledge for both simulation and application in the
control system design for AVs.
• Using specific vehicle, control, and perception instruments to apply the theory taught during
the course and providing various opportunities for the students to realize unique projects in
AV research.
• Discussing the current developments in AV research by reviewing the research papers
providing scientific literacy on the topic.

AU  - Oguzhan Oruc
AU  - Eva Singleton
AU  - Andrew B. Williams
AU  - Kevin Skenes
CY  - Mississippi State University, Mississippi
DA  - 2025/03/09
PB  - ASEE Conferences
TI  - Development Of A New Course: Control Design for Autonomous Vehicles Using A Quadcopter As The Learning Platform
UR  - https://peer.asee.org/54157
DO  - 10.18260/1-2--54157
ER  -