The biggest difference between driving Sawppy and most other robotic platforms is the calculation behind operating the six-wheel-drive, four-wheel-steering chassis. Making tight turns in such a platform demands proper handling of Ackermann steering geometry calculations. While Sawppy’s original code (adapted from SGVHAK rover) was functional, I thought it was more complex than necessary.
So when I decided to rewrite Sawppy code for ROS Melodic (since abandoned) I also wanted to rework the math involved. I’ve done this a few times, most recently to make the calculations in C for an Arduino implementation of Sawppy control code, and it always starts with a sketch on paper so I can visualize the problem and keep critical components in mind.
Once satisfied with the layout on paper, I translate them into code. And as typically happens, that code would not work properly on the first try. The test/debug/repeat loop is a lot more pleasant in Python than it was in C, so I was happy to work with the tools I knew. But if the iterative process was even faster, I was convinced I could write even better code.
Thus I had my first real world use of a Jupyter notebook: my Sawppy Python Ackermann code. I could document my thinking in Markdown right alongside the code, and I could test ideas for simplification right in the notebook and see their results in numerical form.
But I’m not limited to numerical form: Jupyter notebooks can access a tremendous library of data visualization tools. It was quite overwhelming to wade through all of my options, I ended up using matplotlib‘s quiver plot. It plots a 2D field of arrows, and I used arrow direction to represent steering angle and arrow length to represent rolling speed. This plot gave a quick visual confirmation those numbers made sense.
In the Jupyter notebook I could work freely without worrying about whether I was adhering properly to style guides. It made the iterative work faster, but that did mean spending time to rework the code to satisfy wemake style guides. The basic logic remains identical between the two implementations.
I think this calculation is better than what I had used on SGVHAK rover, but it feels like there’s still room for improvement. I don’t know exactly how to improve just yet, but when I have ideas, I know I can bring up the Jupyter notebook for some quick experiments.