Once the clock hit 11:05, our team immediately opened up the box and all read the challenge. After reading the prompt, we all knew what we needed to do and that was to begin researching how to accomplish this challenge that was put in front of us.

Our team:
Cory: Started to build the conveyor belt contraption.

Isaac, Matthew, Ruize: Looked into different methods to achieve decent accuracy for the machine vision detection.

Ruize: Setup the Arduino UNO Q, configuring the IDE and downloading all the necessary resources.

While researching about the different models we could implement to solve this challenge, some of the previous knowledge from past classes had been brought up. Whether or not we used a  (CNN) or a standard LLM was a debated discussion within our team. We also were thinking on whether or not it would be a good idea to divide the R&D into two implementations and exploring whether or not it would be beneficial given the limited time-frame had. 

We opted to go with one path, the CNN approach using Ultralytic's You Only Look Once (YOLO) vision model. By using that vision model in conjunction with different datasets from Roboflow, Ruize, Matthew, and Isaac was successful in setting up a working detection script in Python, using the webcam provided by the challenge to detect everyday items.

On the other hand, Cory was looking at ways to improve on the conveyor belt design and placement of the webcam for the optimal viewing angle and extraction time. While also working on the logistics for the team.

Currently, the team is still in processing of getting a good dataset and testing the of the model, while for hardware, designs are being sketched up and implemented.