In the mechanical realm, we have to make sure the stepper/DC/Servo motors don’t draw too much current through the Arduino Motor shield, so we made sure to choose options with the least amount of current draw. We could also swap out the motors with something else down the road if we find it necessary. Also, we do have a concern of RPI 5 not being able to run on full throttle due to requiring 5V 5A, but even on the low power peripheral mode, we expect the 2 cameras and the serial interface to stay within the max current output. Finally, the stepper motor options we could use were heavily restricted by the 1.2A current draw, making us choose a relatively weaker one. It is possible that it will struggle to move the entire chassis, but we do plan to use possibly lighter material for the casing, make the batteries smaller in case we need to reduce weight, and change the gearing design to require less torque on the motor.
In terms of software, the biggest risk is a failed classification model. In our proposal, we promised at least a 95% accuracy in card classification, but we want to achieve something higher than this. Initially, we will be attempting a standard CNN architecture without much preprocessing. If this does not achieve a high enough accuracy, we will preprocess the image (extracting the symbol) to classify the symbol and color separately. This will decrease the dimensionality of both the input and the output, hopefully simplifying the problem for the model. If all of this fails, our backup solution is to tag the cards (say with a QR code) to reliably identify them.
On a systems level, nothing changed. We are calibrating sizing of the case and gear ratios, and minor physical features, but our overarching design is still the same.
We chose a smaller battery pack for the motors as we realized that we could save power by cutting power to the motors when not in use. This allows us to save weight and space in the enclosure. This also saves us money.
We might have to swap what we are doing next week based on the parts delivery. If we do not get the parts, we will just start developing the card dispenser CAD model and 3d print as well as begin work on the classification model itself.
