Category: Rhea’s Status Reports

Finishing the design report and making sure all the feedback from the presentation was addressed took up most of the time this week. Besides that, I prepared for the hardware setup by finalizing the wiring layout and diode plan for the 11×11 switch matrix. Since most of the parts hadn’t arrived yet, I focused on setting up the Raspberry Pi and organizing the copper wire for the matrix. I also got ready for soldering and breadboard testing, which I’ll start once the remaining materials come in.

Part C: Environmental Factors

Our system considers environmental factors by reducing waste and energy use while keeping people connected. Because players can share a game from home, they do not need to travel to meet in person. This lowers transportation emissions over time, especially for groups that play often.

The design also uses materials and power carefully. The LEDs and camera run at low power, and the system stays quiet and cool during use. Its modular build lets users replace or repair parts instead of throwing away the whole board, which helps reduce electronic waste. The camera’s infrared light stays at safe levels for people and pets. Together, these choices make the system more energy-efficient, safe, and sustainable.

This week, I finalized the switch circuit design and ordered the necessary parts to build an initial working prototype. Our plan is to start with a smaller 3×3 switch matrix to validate the circuit before scaling up to the full 11×11 version. Once the parts arrive, we’ll assemble and test the smaller matrix on a breadboard to confirm functionality. If we identify any adjustments during this phase, I will update the schematic accordingly before constructing the full board.

I spent a lot of time working and designing how the switch matrix would work and function, finding optimal placements to maximize the space we had, and minimize required components, keeping in mind ease of implementation. I laid out the 20″ × 20″ hexagonal game board with 11 copper rows and 11 copper columns, creating a grid that allows each intersection to be a unique addressable switch. The pink points in the diagram below represent the actual switch positions, while the diagonal copper wires are 18-gauge conductors that form the backbone of the matrix. By pressing two adjacent switches, players can place a road in the game, while a single press can represent a settlement (with an additional press later upgrading it to a city). This switch matrix design balances compactness, wiring simplicity, and intuitive user interaction.

In addition, I worked out how the computer vision dice detection system will function. We are planning to use the Oak-D Pro depth camera, connected to the Raspberry Pi 5 via USB to capture the dice rolls inside a transparent dice tray. The video frames will be processed by OpenCV, where we apply preprocessing steps like grayscale conversion, Gaussian blur, and adaptive thresholding to reduce noise and highlight the dice. From there, a blob detection algorithm is used to identify the dark circular regions (pips) on the dice faces. To ensure accuracy, the algorithm will use density-based clustering (such as DBSCAN) to group nearby pixels and eliminate spurious detections caused by reflections or shadows.

This approach has several advantages over mechanical or magnetic sensors:

1. It allows the use of regular dice without modifications.
2. The system can generalize across lighting conditions by adjusting preprocessing thresholds dynamically.
3. By knowing the tray’s approximate size and camera angle, we can filter out non-dice blobs and count only the valid pips.
4. Because the Oak-D Pro provides depth information, we could later extend the pipeline to verify that exactly two dice are present and lying flat before confirming a roll.

The final result of the pipeline will be a pair of integers corresponding to the dice values. These values are then sent to the Raspberry Pi’s game logic, which passes them over the WebRTC DataChannel to keep all boards synchronized. This means that once a roll is detected, the same value propagates across every connected board in under 500 ms.

Together, these two pieces, the switch matrix for structured player input on the board and the CV pipeline for automated dice roll detection, complete the sensing and input layer of the system. They integrate well with the Raspberry Pi’s GPIO and USB interfaces, and fit directly into the broader WebRTC-based communication system Tanisha finalized this week.

My plan for next week is to prototype OpenCV pipeline with the Oak-D Pro to validate dice detection accuracy and robustness under different lighting conditions.

During the proposal presentation, there were some questions regarding which specific Raspberry Pi we would be using and whether there would be enough GPIO pins to wire all our desired connections. I looked into the Raspberry Pi pins to check how many were available and whether we could reasonably support all our hardware. For our project, we need to support the camera along with all the LEDs, buttons, and the connection to our server, while also making sure everything works reliably as one system. My progress is on schedule, and I plan to complete a rudimentary block diagram showing the connections between our hardware in the next week.