Role: Team Leader, Software & Electronics Engineer
This project explores the use of computer vision in robotics, and includes the full stack of robotics from software to firmware to electronics to hardware. It was iteratively done over the course of 3 phases, over multiple Robocup competitions:
1. 2022 National Champions
- Using OpenCV on Raspberry Pi 4B to detect and navigate a line and obstacles.
- PCB (only THT) custom ordered to act as a base.
- Arduino Mega for motor control and sensor readings.
- Mechanical design using Fusion 360 and 3D printing.
2. 2022 Regional Champions
- Upgraded to Teensy 4.1 as microcontroller
- Iterated on OpenCV method by exploring different functions.
- Created and added a light sensor array PCB on top of the camera to improve line detection in different lighting conditions.
3. 2023 National Champions
- PCB revised to include SMT components.
- Another camera (top down) was added to improve depth perception and victim detection.
- 3D Printed gearbox for a mechanical 2 motor but 4 wheel drive was created to reduce footprint and improve reliability in navigating through narrow spaces. (e.g. entrance).
- Created and 3D Printed Omniwheels to improve turning on the rectangular speed bumps.
- Added more TOF sensors for better victim, obstacle detection and wall tracking in the rescue area.
First Robot
This was the first iteration of the robot, featuring a simple design and basic functionality. This robot went on to win the Robocup Rescue Open Singapore U19 Rescue Line category with more than x4 points of the second place winner.
Second Robot
This robot went on to win the Robocup Rescue Open Asia Pacific U19 Rescue Line category, sweeping Champion, Best Educational Value and Judges' Award.
Final Robot
Model of the final robot