- There are a couple of significant risks that could jeopardize the success of the project. One risk is the stability of our hardware components. Through our long testing, we discovered that our NRF24L01 wireless transceivers were not reliable 100% of the time. A majority of the time transmission from the web app side of the system to the hardware side of the system worked, and other times the signal don’t go through, which definitely is not ideal when the system hinges on the transceivers to be the bridge between our 2 major subsystems.
- As a contingency plan, we purchased extra NRF24L01 transceivers that can be swapped out if something is wrong with the system. As a last resort, our web app keeps track of item stand transactions, which allows the customer to easily find user items (which is just a normal coat checking system).
- The second risk is facial recognition. This is also another very important component to our system. Without very high accuracy facial recognition, the system could prove to be frustrating to users who may not be able to retrieve their items easily. It is also a very hard thing to perfect and there is not much time left.
- As a contingency plan for the facial recognition system, we can allow the user to enter a name or identifier in place of scanning their face. Any other kind of identification method could work.
- Through our end to end testing, we discovered that some of our design requirements were not realistic. For example, we initially required that once a user’s face was matched, the stand should display the user’s position on the rack with an LED within 1 second. This did not account for the time it would take for the motor to rotate from it’s current position to the user’s position after they had checked in. Therefore, we changed our requirement to 7 seconds to allow the motor to rotate the the target position.
- Overall, there is not much change to the schedule.
Carnegie Mellon ECE Capstone, Spring 2024 | Doreen Valmyr, Ryan Lin, Surafel Tsadik