Reflection

Looking back on this project as a whole, the thing that stands out most is how much was accomplished by keeping the scope focused. The modular chassis design was one of the best early decisions I made, as building a single vehicle with interchangeable suspension arms rather than two separate builds kept the experiment clean and the variable isolation intact. The Python analysis pipeline also worked out well, as having a repeatable script that could parse raw sensor logs and generate consistent graphs made the transition from data to presentation smooth and professional. And on a broader level, the use of Similitude as the theoretical foundation gave the project academic legitimacy that a more casual build-and-test approach would have lacked. It grounded every design decision in real engineering reasoning.

That said, there are things that would be done differently given another pass at this. The motor and gearing selection deserved more research upfront: the speed issue was manageable, but it introduced a limitation that affected the quality of the data and required a workaround rather than a solution. For anyone continuing this work, the first recommendation would be to plan the drivetrain around the terrain, not the other way around. The second would be to seek a professional or experienced engineer to review the CAD geometry before printing. Student-designed suspension components are a reasonable starting point, but independent validation would meaningfully strengthen the findings. The significance of this research lies in demonstrating that Similitude-based scale testing is not just an abstract engineering concept; it is something a motivated student can actually execute with accessible tools and produce real, interpretable data from. That has value for anyone in robotics, automotive engineering, or physics looking for a rigorous project framework.

As for where this work could go next: re-gearing the drivetrain is the most immediate extension, as it would allow terrain inputs to more fully engage the suspension and produce cleaner performance differences in the data. Beyond that, this same platform could be used to test additional suspension geometries, different spring and damper configurations, or even active suspension control systems. The chassis and the data pipeline are both reusable. More broadly, the Similitude methodology applied here could be extended to other disciplines such as structural impact testing, aerodynamic studies, or even robotics locomotion research. On a personal note, getting to spend a trimester doing a structured, rigorous project on something I am genuinely passionate about has been one of the more rewarding academic experiences I have had. It is one thing to study engineering and another to actually do it.