SmartCT Projects

The SmartCT Projects initiative is part of an NSF-funded research program through the Innovative Technology Experiences for Students and Teachers (ITEST) grant, led by Dr. Lisa Thomas and Dr. Yan Sun in the Department of Technology, Leadership, and Design.

Projects

Undergraduate researchers design and build hands-on robotics kits powered by machine learning then bring those same projects to K-12 students through summer camps and yearlong mentorship at Mississippi State University.

Each project challenges students to go beyond consuming AI and become creators of intelligent systems, wiring hardware, training custom machine learning models, writing code, and documenting their process from first prototype to finished product.

Project guides are coming soon. Check back for updates.

Sorting Machine

An automated device that identifies and sorts red and yellow balls in real time. A DC motor feeds balls down an Archimedes spiral while a Raspberry Pi Camera V2 captures images. A custom-trained Google Teachable Machine model processes each image and signals two servo motors to physically sort the objects into separate buckets.

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Smart Door

An intelligent access control prototype that uses image recognition to grant entry only to authorized users. A Raspberry Pi 4 feeds live camera images into a Teachable Machine model; when an authorized face is recognized, a NEMA 17 stepper motor drives a 3D-printed rack-and-pinion mechanism to open the door for five seconds.

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Balloon Pop

An interactive, projection-based game where players pop virtual balloons by throwing a real ball at a screen. A Raspberry Pi 4 and 1080p webcam capture live footage; a Teachable Machine model detects when the ball makes contact, removes the balloon, and updates the player's score in real time — combining computer vision, real-time data processing, and physical hardware interaction.

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TurtleBot

An autonomous mobile robot that navigates its environment by reading visual cues. A Raspberry Pi Camera captures a live video feed that a machine learning model uses to identify directional labels — forward arrows, turn signals — and translates those into movement commands via ROS2, demonstrating intelligent, image-based navigation.

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This material is based upon work supported by the
National Science Foundation.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

SmartCT Projects