Arduino is an open-source microcontroller platform commonly used for robotics, electronics, and automation projects. In this project, we programmed an Arduino-based robot to autonomously follow a line and avoid obstacles. By combining a line follower sensor with a touch sensor, the robot can recognize the path, maintain its movement along the designated route, and respond to physical obstacles by changing its direction or adjusting its movement.

Zumi is an AI-powered educational robot that uses sensors and Python programming to perform autonomous navigation and decision-making tasks. In this project, we programmed Zumi to simulate a rescue robot escaping from a damaged building environment. We developed movement functions, obstacle detection logic, and decision-making strategies that allow the robot to detect objects in different directions, avoid blocked paths, and choose alternative routes to continue navigating safely.

A Tamiya tracked vehicle is a robotic platform that uses tank-like tracks to move across different terrains and can be modified with sensors for autonomous navigation. In this project, we built and modified the vehicle to simulate a rescue robot operating in a damaged building. We integrated components including a Red board, ultrasonic sensor, and bumper sensor, then used vibe coding methods to create programs that allow the vehicle to detect obstacles and adjust its movement.

An AI humanoid robot is a robot designed with a human-like structure that can use artificial intelligence to perceive and interact with its environment. In this project, we programmed an AI-based humanoid robot to simulate a rescue mission inside a damaged building. The robot was designed to locate and identify victims by using sensing, recognition, and navigation capabilities, demonstrating how intelligent robots can assist in emergency response situations.

After completing the simulations, we built a single-stage rocket and launched it with a small Wi-Fi camera attached. During the launch, we recorded the rocket’s flight and collected data such as maximum altitude, flight path, and overall stability. We then compared the experimental results with the simulation predictions from RockSim and the NASA Rocket Modeler. By analyzing the similarities and differences, we evaluated the accuracy of the simulation and discussed how factors such as wind, construction quality, and weight distribution affected the rocket’s real-world performance.

Our group programmed a RoboLink drone in Python to autonomously detect different colored surfaces using a continuous while True loop. The drone kept flying while the detect variable remained False. Inside the loop, an if statement constantly checked the color sensor readings. When a target color was detected, the condition became True, causing the drone to stop, land safely, and display the detected color on the screen. After waiting for a short period, the drone took off again, reset the detection status to False, and continued searching. This logic allowed the drone to repeatedly patrol, detect, respond, and resume its mission without user intervention.

We modified a foam propeller plane to maximize both flight distance and altitude when launched from a five-degree incline. We experimented with different design changes, including adjusting the wing position, balancing the center of gravity, and reducing unnecessary weight. After each modification, we tested the plane multiple times and compared its flight performance. Based on the results, we refined the design to improve stability and efficiency. Through this iterative engineering process, we learned how small structural changes can significantly influence aerodynamic performance and overall flight capability.