What are the most significant risks that could jeopardize the success of the project? How are these risks being managed? What contingency plans are ready?
The most significant risk to our project is accumulated error. Since our sub components depends on necessary communication between each other (machine learning model results to web app and machine learning model results to Arduino and Arduino commands to gantry movement), if one component does not obey their respective design requirement exactly it could risk the whole project’s validation. In order to prevent, we are taking extreme precautions to ensure that the each sub component is working as intended through rigorous testing. Our aim is to mitigate individual risk to mitigate overall risk.
Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?
There are no current changes made to the existing design of the system.
Provide an updated schedule if changes have occurred.
There are no schedule changes as of now.
Validation.
We plan to do multiple runs, first isolating by each component of the system (e.g. centroid & classification, depth accuracy, end-effector pickup rate) and doing multiple trials with different types of trash items. We will then do the same with the complete, combined system.
Reliable Sorting:
We will test a variety of trash/recyclables with multiple surface types and materials, in order to make sure that the end-effector is able to pick up objects with 95% success rate. We’ll also measure the distance that the gantry moves over a certain amount of steps in order to determine its granularity in the x,y,z movement directions.
Real-Time Monitoring:
We plan to ensure that the bytes from the Jetson Orin Nano reaches the web app in 30 FPS by timing when they leave the Jetson and arrive to the server using either Wireshark or timestamps in the code of each entity communicating over the network.
Real-time Object Detection:
We plan to use a set of real-life trash objects like plastic bottles and cans. We will do multiple sets (10) of static images each containing a different variety of objects to ensure that the machine learning model can work regardless of the images in the camera frame. We will also need to analyze that labels that the model outputs to see if it lines up with reality. We are aiming to match the 0.70 precision. from the Design Report.