The most significant risk that could jeopardize the success of the project remains the board state detection accuracy, which has not changed since last week. Our goal is to achieve above 95% accuracy in detecting board states from the physical board. However, even at 95% accuracy, this would correspond to approximately one incorrect detection every 20 moves, and with typical games lasting around 40–60 moves, this could introduce multiple errors during gameplay. Such misreads could propagate incorrect game states to the engine and affect move recommendations. To mitigate this risk, we plan to develop more robust evaluation metrics to identify specific failure cases, such as misclassification of certain pieces or errors under particular lighting or board conditions. As we continue testing with the physical board and pieces, we will focus on improving detection reliability and identifying conditions that cause failures.
We do not have any changes to the existing design requirements and have no updates to the project schedule this week.
For individual status reports, see:
Charlie – http://course.ece.cmu.edu/~ece500/projects/s26-teamd3/2026/03/15/charlies-progress-report-for-3-14/
Yoyo – http://course.ece.cmu.edu/~ece500/projects/s26-teamd3/2026/03/14/yoyos-status-report-for-3-14/