This week our team has explored materials and measurements of the mechanical parts including the bins (one big bin and two small “sub-bins” for recyclables and trash). We updated our trapdoor design from a single door operated by an actuator to a swinging door with two openings, one for recycling and one for trash. This way, instead of the user having to wait for the device to respond and having to take the waste off the platform if not recyclable, the device can educate the user with its alerts while still taking care of the organization, making its use easier and more practical overall. Since we were still in the design stage and hadn’t ordered parts for the old design, there wasn’t much of a cost to account for due to this change. From there, we have designed and prototyped where and how to connect each part including the platform and the servos that controls the closing and opening of the lid. We have also added the idea of using ultrasonic sensors to detect if the bins are full. The fill indicator is not a part of our MVP but we will look into how the sensors could read and transfer the capacity status. Simulations of arduino are performed on tinkercad and more details could be found on our presentation slides.
Our biggest risk right now that could jeopardize success is the operation of the ML waste classification model, since we are having some initial set up setbacks and the model not working could mess up the entire classification part of our project, which is one of our biggest requirements. In terms of software, we have delved into the ML code and tried to run it with our drinking waste classification datasets. Right now the training could be run halfway and we still need to adjust our dataset structure. Since the size of datasets is huge and Google colab takes fairly long to run, this part is taking more time than expected. However, as we definitely have a better understanding of the model and we have written scripts to deal with raw data and set up our project structure on colab, we are able to deal with the risks without it not leaving us too much behind and we are confident we can get this working soon. As a contingency plan, we do have other backup models/datasets from our research earlier this semester that we can switch in if the current set up does not work out.
Over the weekend we are ordering more mechanical parts such as the bins, servos, and lids; and after the design report, we could transfer code from tinkercad to the devices and integrate them.
So far, HW wise we are on/ahead of schedule (design + simulation complete), however, the delayed part ordering may set us back a bit this upcoming week. Software wise we are a little behind due to the difficulty in debugging the training model, and therefore will focus more of our efforts here to get back on track.
Some principles of engineering, science and mathematics that our team used to develop the the design solution include:
- ML (detection and classification algorithms): ML Model
- FSMs, engineering design charts: design diagrams
- basic mathematics/geometry: mechanical measurements and camera distance calculations (focal point + triangle calculations)
- Algorithms, Programming, and Control flow: training model debugging, hardware programming for simulation
- Circuit design: Arduino + components hardware design for speaker, neopixels, servos, etc.