2023 ASEE Annual Conference & Exposition

Development of a Raspberry PI-Controlled VEX Robot for a Robotics Technology Course

Presented at Engineering Technology Division (ETD) Technical Session 9

This paper describes the development of a Raspberry PI-controlled VEX robot for an undergraduate robotic course. The Raspberry PI controls the mobile base built using the VEX robotics kit without using the Cortex micro-controller that comes with the kit. The aim is to create a physical robot that is manageable, easily replicable, and capable of performing advanced robotic control tasks such as vision-based control.

The constructed robot adopts the great features of the PI and the VEX hardware. Firstly, the VEX hardware consists of various sensors and actuators for students to practice the construction and assembly of an autonomous robot. Secondly, the Raspberry PI provides a Linux environment for programming and implementing advanced algorithms. As a result, the robot can assist effective teaching of many STEM subjects involving robotics, image processing, and artificial intelligence. It can also facilitate undergraduate research outside of the classroom.

The paper describes the development of the PI-controlled VEX robot, providing details of its construction, electronics wiring, low-level motion control, onboard image processing, and closed-loop vision-based control.

Authors

Dr. Lili Ma New York City College of Technology [biography]

Professor Lili Ma received her Ph.D. in Electrical Engineering from Utah State University focusing on autonomous ground vehicles. After that she did three-year post-doctoral training at Virginia Tech working with unmanned aerial vehicles (UAVs). Prior to joining the Dept. of Computer Engineering Technology at CUNY New York City College of Technology, she taught at Wentworth Institute of Technology for many years. Her research interests are in designing coordinated control schemes for a group of autonomous robots. Her teaching interests are in designing robotic projects that promote undergraduate research and integrate interdisciplinary areas (robotics, artificial intelligence, IoT, electronics, and image processing).
Justin Bartholomew New York City College of Technology [biography]

Justin Bartholomew is a senior student in the department of Computer Engineering Technology (CET) at CUNY-New York City College of Technology. He is interested in circuit design, embedded systems, robotics, artificial intelligence, and Internet of Things (IoT). Justin is currently working as an undergraduate research assistant on an NSF collaborative research project “CISE-MSI: DP: CNS: An Edge-Based Approach to Robust Multi-Robot Systems in Dynamic Environments”.
Dr. Yu Wang New York City College of Technology [biography]

Dr. Yu Wang received her Ph.D. degree in Electrical Engineering from the Graduate Center of the City University of New York in 2009. She is an associate professor in the Department of Computer Engineering Technology at New York City College of Technology. Her research areas of interest are engineering education, biomedical sensors, modeling real-time systems, embedded system design, and machine learning.
Dr. Xiaohai Li New York City College of Technology [biography]

Xiaohai Li received his M.S. degree in Electrical Engineering from Polytechnic Institute of New York University, New York, in 2004 and Ph.D. degree
in Electrical Engineering from the Graduate Center of the City University of New York, New York, in 2010. He worked as a Post-doc in the PRISM
Research Center in the Department of Electrical Engineering at the City College of New York in 2010. He is currently an Associate Professor in the
Department of Compute Engineering Technology at NYC College of Technology of the City University of New York (CUNY). He founded the City Tech Robotics Research Lab and is a co-founder of the City Tech Experiential Arts and Technology Lab (EAT Lab) at NYC College of Technology of CUNY. His current research interests include applied control systems, robotics, swarms, wireless sensor networks, computer vision and perceptual computing, and IoT/IoRT.

Download paper (16.5 MB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

» Download paper

« View session