Full-mark scoring autonomous vehicle design for the WRO 2025 Future Engineers category, using Raspberry Pi based control platforms.
- Overview
- Repository Structure
- Team Members
- Hardware
- Software
- Assembly Instructions
- Potential Improvements
- License / Acknowledgements
This project is a autonomous vehicle designed to achieve full marks in the WRO Future Engineers 2025 challenge.
It serves as both a learning platform for participating teams and a benchmark bot for testing driving logic, wall-following algorithms, and challenge-specific maneuvers.
Key features:
- Front-wheel steering and rear-wheel drive design for realistic vehicle dynamics.
- Raspberry Pi 4B for vision-based navigation and sensor control and Raspberry Pi BUILD HAT for direct motor and sensor control.
- Three ultrasonic sensors and two LiDAR for accurate wall following and close range wall detection.
- Gyroscope, magnetometer and accelerometer-based orientation stabilization.
- Modular software for quick adaptation to custom strategies.
| Directory | Description |
|---|---|
pi-install/src |
Installation script |
team_photos |
Team photos |
vehicle_photos |
Images of the completed robot builds |
wroprg |
Source code in Python |
README.md |
Project documentation (this file) |
schematic.jpg |
Circuit schematic diagram |
- Kanak Arora – Lead Programming and Electronics and Basic build development – yolabteachers2@gmail.com
- Rachit Tiwari – Electronics, Program development and Overall strategy – yolabs007@gmail.com
- Mohit Kuriseti – Electronics, Build development and Program development – yolabs007@gmail.com
- Saurabh Arora – Coach for team Yo-Vroom – support@yolabs.in
- Team YoLabs – WRO Future Engineers preparation and reference build development.
| Component | Description | Notes |
|---|---|---|
| Chassis | Lego Spike Prime Default chassis | Optimized for stability & handling |
| Spike Prime Large Motor | Rear-wheel drive | Controlled via Raspberry Pi BUILD HAT |
| Spike Prime Small Motor | Front wheel steering system | Controlled via Raspberry Pi BUILD HAT |
| Raspberry Pi 4B | Main processing unit | Runs vision & navigation algorithms, distance sensor control |
| Raspberry Pi Camera Module | Vision input, Front and side distance confirmation | Front-mounted, wide-angle lens |
| Raspberry Pi BUILD HAT | Control unit for all LEGO Education Spike Prime components | Handles motors & colour sensor input |
| Spike Prime Colour sensor | Detecting direction through line colour on mat | Controlled via Raspberry Pi BUILD HAT |
| BNO055 | Gyroscope, accelerometer and magnetometer module for orientation feedback | I2C connected to Raspberry Pi |
| Ultrasonic Sensor (×3) | Distance measurement | Mounted front, left & right for wall following and front wall detection |
| Li-ion Battery (x4) | 18650 3.6V 3500mAh | Powers motors, electronics, BUILD HAT & Raspberry Pi |
| OLED display | For error and info display | I2C connected to Raspberry Pi |
| VL53L0X LiDAR Sensors (x2) | For close range distance detection. | I2C connected to Raspberry Pi |
| Misc. | Smaller components for errors and info, connectors, wiring |
- Configuration: Front-wheel steering with a single Motor rear drive motor.
- Turning Radius: Optimized for narrow WRO track corners.
- Control: PWM-based speed control, Gear mechanism-based steering.
- Build Choice Reasoning: Offers realistic car-like dynamics, ideal for FE challenge simulation.
Possible enhancements: Lighter chassis for better acceleration, Stronger front motor/servo for steeper turning radius.
- Power:
- 4x 18650 3.6V 3500mAh Li-ion Batteries connected in parallel for powering Raspberry Pi, Motors and all Sensors and Electronics through Raspberry Pi BUILD HAT.
- Sensors:
- Ultrasonic (Front, Right, Left): Wall distance, corner detection and wall following
- BNO055: Fuses data from an accelerometer, gyroscope, and magnetometer to give accurate yaw readings.
- Raspberry Pi Camera Module: Vision-based navigation, obstacle and challenge detection.
- Spike Prime Colour sensor: Direction Detection from mat line colour.
- Small LiDAR (Right, Left): Close Range wall detection.
- Raspberry Pi:
- Flash Raspberry Pi OS Lite.
- Enable SSH, I2C, and Camera support.
- Install Python dependencies:
pip install opencv-python numpy imutils
- Clone this repository:
git clone https://github.com/saurabharora2021/wrofutureengineer2025
- Raspberry Pi BUILD HAT:
- Install Python dependencies:
wrofutureengineer2025/pi-install/src/install_service.sh
- Install Python dependencies:
[Initialization]
|
v
[Sensor Calibration & IMU Stabilization]
|
v
[Wall Following + Yaw Correction]
| \
| --(corner detected by side sensor)--> [Turn Initiation]
| |
| v
| [Waiting for Turn Completion]
| |
| v
|-------------------------------<----------
|
v
[Lap Complete] --(final edge counted)--> [Stopped]
- Vision Processing (OpenCV on RPi):
- Detecting front wall.
- Detecting track Obstacles.
- Approximate distance calculation(not accurate) in extreme cases to avoid running into walls.
- Sensor Integration:
- Ultrasonic sensors for precise wall-following.
- LiDAR sensors for close range detection.
- BNO055 for orientation drift correction.
- Colour sensor pointing towards ground to detect direction.
- Wall Following: PD-controlled steering using right and left ultrasonic distance and Small LiDAR sensors. LiDAR sensors are used for close range wall detection(15cm).
- Corner Detection: Front ultrasonic + camera-based detection to initiate turns.
- Lap Completion: BNO055 + distance tracking to ensure accurate lap counts.
- Recovery Logic: If bot drifts, slow down and re-center before resuming speed.
- Straight Walk: Distance sensor, LiDAR, Camera and BNO055(gyroscope, magnetometer and accelerometer module) fusion for smooth path control.
- Chassis Prep:
- Mount Raspberry Pi LEGO Education Spike Prime plate onto base chasis.
- Steering Mechanism:
- Mount LEGO Education Spike Prime Small Motor.
- Make Gear mechanism for steering (Gear ratio is 3:5).
- Electronics Install:
- Mount the custom circuit based on the uplaoded circuit schematics.
- Mount RPi on vibration-dampened standoffs.
- Mount LEGO Education Spike Prime Large Motor.
- Mount LEGO Education Spike Prime Colour sensor.
- Mount Raspberry Pi BUILD HAT on top of Raspberry Pi.
- Mount and connect all other sensors and electronics.
- Power Wiring:
- Ensure regulated supply to Raspberry Pi BUILD HAT.
- Testing:
- Test each component (motors, sensors) individually.
- Run calibration scripts before full run.
- Integrate stronger LiDAR for higher-precision mapping.
- Use lighter Chassis and more open source hardware to decrease errors.
- Improve computer vision FPS via GPU acceleration on RPi.
- Add auto-calibration for ultrasonic sensors.
- Experiment with reinforcement learning for adaptive driving.
This project is licensed under the MIT License.
Special thanks to:
- OpenCV – Computer vision processing.
- Raspberry Pi Foundation – SBC platform.
- Raspberry Pi BUILD HAT – Integrating LEGO Education Spike Prime hardware with raspberry pi.
- WRO community and participating teams for continuous inspiration.
For any questions or feedback, please feel free to contact us via email or open an issue on this repository.