Month: March 2025

As a team, we finished writing the Design Document. We kept working individually on the components we had been working on before, but we also organized to get a simpler Camera to use instead of the Oak-D Pro. As mentioned, this camera has far more features than we need, so we just got a simple USB camera off Amazon, and will put the order in after Spring Break. Development is proceeding smoothly for the most part, which we are happy about. We do not have any significant schedule changes or block diagram changes currently. The most significant risks would be an issue with model training, which could delay integration progress, so Nico will be taking the week after break to get the model up and running so we don’t have to worry about that possibility.

This week was focused on the finishing the debugging and finishing reading and understanding the algorithms described in the papers.

For the website, I was able to fix most of the bugs, but one bug that has remained illusive is with having multiple people connect to the website at the same time, so I’m still trying to debug that. The web app and the DB are connected properly though, so I was happy to fix that bug.

The formalization the paper described was a bit complicated, so I spent some time speaking with Nico understanding it, and I think I now understand this fully enough to start implementing it. However, we ironed all these issues and understandings out right before Spring Break, so we will finish it after the break.

For the past two weeks, I have been working on an improved design for the angling of the camera in the Blackjack Buddy mechanism. I have started to create a CAD model for the product, and hope to finish it by the time the next status report is written. I have also continued to work on the algorithm for betting strategies with Joe. The idea is that we will have a module that acts as a function for this algorithm, and the goal is to make this module as portable as possible so that an integration into the entire design will be as seamless as possible.

I have started training of the ML Model, but the environment was having issues, so I worked on resolving that before Spring Break. I was able to iron out the issues by setting up a Conda environment and resolving the dependency issues to use Yolo. I also read more into TensorRT, and decided to start getting the needed resources together to download and use TensorRT on the Jetson Nano. We will report on training next week