🚀 Week 4: Training the Brain – ML Integration & Poster Design

Project: Autonomous Driving 

Supervisor: Roberto Ferrero

Team: Yu Jiang, Runlai Yang, Haikun Xu, Jerry Fan

🧠 Machine Learning & Data-Driven Navigation

This week, we successfully transitioned from classical control to Machine Learning (ML) implementation. We conducted controlled test runs on a predefined track to collect a comprehensive dataset of sensor readings and motion outputs. After cleaning the data to remove noise, we trained our path optimization model. The preliminary results are exciting: the model is now capable of supporting adaptive navigation, providing a much more "intelligent" response to the environment than simple logic-based code.

🎨 Professional Poster Preparation

In parallel with our technical work, we began designing our Project Poster for the upcoming Week 5 submission. Our goal is to communicate our complex system architecture—from hardware integration to ML results—in a visually accessible A1 format. We focused on:

  • Visual Logic: Creating block diagrams to illustrate software-hardware synergy.

  • Design Consistency: Ensuring color schemes and font sizes are optimized for professional readability.

  • Modular Structure: Breaking down our journey into clear sections: Architecture, Design, Results, and Future Work.

💡 Weekly Reflection

Week 4 highlighted the power of a data-driven approach. Moving beyond "hard-coded" rules to an adaptive ML model has fundamentally changed how our rover "perceives" its path. Furthermore, the poster preparation process forced us to step back and view our project as a complete, professional package.



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