Inspiration:
Coca-Cola Having the opportunity to work with the leading producer or soda in the world with a phenomenal level of innovation, was one of the main driving factors of our interest in this project. From the experience, to the convenience of working with Coca-cola here in Atlanta, this is truly a remarkable opportunity.
Arduino As electrical and computer software engineering students, we have high interest in Arduino boards and their implementations. Tyler has used mbed boards in projects and Christopher has experience with Arduino boards. We felt this gave us an ability to be successful in the CoolerHack.
What it does:
Uses a light sensor, TCS34725, to scan each row of drinks and keep a count of each type of drink scanned.
How we built it:
Mechanical The support bars, in front and rear, are clipped into the pre-existing brackets inside of the cooler. The The sensor rides this mechanism scanning each row individually, sending the code the the Arduino over I2C wires. Digital The code drives the motors from the front of the row to the rear. While the motor is on, the code constantly draws the readings from the TCS34725 if it is within an error range of the preset values for a product, then it increases the count of that project by one. There is also an error test to make sure that any malfunction is not due to communication with the sensor.
Challenges we ran into:
The hardest challenge to overcome was the product recognition idea. Other than that the only other issues we ran into were time dependencies from one part of the project onto the other.
Accomplishments that we're proud of:
We are very proud of our navigation systems and our product differentiation technology. We are pleased, overall, with our result.
What we learned:
We learned about I2C communications and their implementations. We also were able to gather information and techniques in Arduino and the TCS34725.
What's next for CoolerHack:
Our presentation model is a prototype model. With machine built hardware, we can increase the sensitivity on the sensor and lower our percent error inside our code to allow for the differentiation of a higher order of products. This increased accuracy could also incorporate an accelerometer that could tell when a failed reading was due to a mechanical failure. The implementation of the database used for photo-recognition can be used as the parameters for the program. However, the biggest improvement is the use of a lens. By adding a servo motor to the sensor, and a lens at a slight angle two sets of sensor with rails could cover the entire upper row of the cooler where the fan interferes.
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