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TRONOMEGA transforms a high-speed RC racing car into an autonomous robot capable of real-time image processing and path detection. Utilizing a robust hardware setup including a modified chassis, high-performance motors, and an Intel NUC Mini PC, alongside a sophisticated software architecture built on Python and OpenCV, this robot demonstrates advanced capabilities in autonomous navigation and control.
- Chassis: Customized from a 2.4 GHz RC Racing Car, equipped with adjustable rods for camera mounting.
- Motors: Includes a 1000 rpm brushed motor for wheel control and a servo motor for steering.
- Motor Driver: RKI Motor Driver, supporting 6V-24V motors, with a peak current capacity of 20A.
- Computing: Intel NUC Mini PC, facilitating onboard processing with comprehensive connectivity options.
- Power: Dual power systems featuring a 50000 mAh power bank for the Mini PC and additional batteries for motor and peripheral components.
- Communication: HC-05 Bluetooth Module for wireless control and data transmission.
- Main Programming: Developed in Python, leveraging the OpenCV library for image processing and path detection.
- Image Processing: Utilizes dual webcams for real-time image acquisition, employing techniques such as greyscale conversion, blurring, edge detection (Canny), and color thresholding for navigation and control.
- Control Algorithm: Incorporates PID (Proportional, Integral, Derivative) control for precise steering and speed regulation, enabling the robot to navigate complex courses with high accuracy.
The project involved extensive R&D, particularly in optimizing the Python-based control algorithm. The implementation of PID control significantly enhanced the robot's steering capabilities, allowing for sharp turns and optimal speed control.
Special thanks to the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, and our school administration for their invaluable support and guidance.