🤖 WRO 2025 – Future Engineers

Project: Autonomous Car – Team IITA InnovaX

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📌 Project Description

The Future Engineers Challenge of WRO 2025 invites teams to design, build, and program an autonomous car capable of completing a defined track, facing obstacles, and simulating the real challenges in the development of intelligent vehicles.

Our goal as a team is to create a robot that combines mechanical precision, reliable sensor integration, efficient software and Computer Vision, ensuring stable performance on the track.

The development process followed an iterative engineering cycle:

1. Ideate → brainstorming and planning.
2. Build → mechanical and electronic implementation.
3. Test → validation on the track.
4. Evaluate → analysis of results.
5. Improve → feedback and optimization.

All these stages are documented in the Engineering Journal, highlighting not only the final product but also the engineering journey.

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⚙️ Mechanical Design

The car was engineered with a strong emphasis on stability, precision, and reliability:

• Rear-wheel drive with mechanical differential → the traction motor is connected to the rear axle through a differential, allowing the left and right wheels to rotate at different speeds when turning. This prevents wheel slip, reduces stress on the drivetrain, and mimics real car dynamics.
• Car-like steering system (Ackermann steering geometry) with a dedicated front motor → provides smoother and more realistic turns than differential drive systems.
• Custom modular LEGO chassis → designed for robustness and quick maintenance, enabling fast adjustments during testing sessions and competitions.
• Optimized weight distribution and low center of gravity → strategic placement of motors, sensors, and hub reduces drift and improves stability in sharp turns.
• Scalable architecture → chassis layout allows integration of additional sensors (e.g., OpenMV camera or LiDAR) without compromising balance or performance.

This design provides the robot with authentic vehicle behavior, improving accuracy in navigation, efficiency in cornering, and overall track performance.

🔬 Sensors & Electronics

The following sensors were integrated for autonomous navigation:

• IMU → heading control with proportional correction (KP)
• Color sensor → detection of floor colors
• Ultrasonic sensors → navigation along the scenario
• OpenMV Camera -> Computer vision employed for obstacles recognition

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💻 Software Development

The software was developed in Pybricks (MicroPython).
Key features:

• 🚗 Straight driving control with IMU + KP to correct deviations.
• 🔄 Color-based logic to determine driving direction according to the rules.
• 🧠 Modular architecture → robot class and utility functions split across files.
• 📡 Integration with OpenMV H7 Camera → advanced object and signal detection.

📂 Repository Structure

├── docs/                        # Documentation and diagrams
│   ├── engineering_journal.pdf
│   └── WRO - Electrical Diagram.png
├── src/                         # Source code
│   ├── OpenChallengeCode/
│   │  ├── MainScript.py
│   │  └── robot_funcional93.py
│   ├── ObstacleChallengeCode/
│   │  └── Main_ObstacleChallenge.py
│   ├── Funciones.py             # Helper Functions (prototype)
│   ├── IITA_Motion.py           # Prototype
│   ├── BasicMovementFE.py       # Prototype
│   └── main.py                  # Prototype
├── Videos/
│      ├── WRO FE - Desafío Abierto.mp4
│      └── WRO Fe - Desafío de Obstáculos.mp4
└── README.md                    # This file

🚀 Using the Repository

1. Clone this repository:

   git clone https://github.com/IITA-Proyectos/WRO2025-IITA-SALTA-FE
   cd WRO-2025-FutureEngineers

2. Search into src/OpenChallengeCode or src/ObstacleChallengeCode for script files and upload it into Spike Hub using PyBricks IDE

3. Connect motors and sensors according to (docs/hardware_setup.png)

4. Place the robot on the official WRO track scenario and power it on.

🏆 Rules & Compliance

This project complies with the WRO Future Engineers 2025 General Rules, including:

• Use of approved components.
• Respecting weight and size limits.
• Fully autonomous implementation (no remote control during runs).
• Documentation provided in both Engineering Journal and GitHub README.

👥 Team Members

• Gerardo Benjamín Uriburu Romero – Team Leader & Programmer
• Natanahel Fernández – Team sub-leader & Computing Vision Programmer
• Enzo Juárez - Mentor & Professor

📢 License

This project is released under the MIT License – free to use and adapt for educational purposes.

✨ Acknowledgements

• Special thanks to Instituto de Innovación y Tecnología Aplicada for academic, logistical and financial support.
• Mentors and Professors who share their knowledge with us for the project
• Instituto de Innovación y Tecnología Aplicada Community for their support
• WRO community for providing the framework for innovation and competition.
• Our families and friends for their moral support