This week, I put together diagrams to organize our thoughts and make sure we’re all on the same page about interconnections. First, I drew the door design. In a moment of panic, I thought that the servo would not be strong enough to lift a wooden panel, so I changed it to a flappy door design:
Then a mechanical engineering friend told me to redo the calculations and I realized that kg-cm means kg force x centimeter distance. So our 10kg-cm servo can lift 2kg at 5cm radius. So we’re back to the lifting door design.
I learned that it is important to have consistent lighting for machine vision. I selected an LED that content creators on Youtube often use for filming. It will be connected to a power relay that is controlled by the jetson. An alternative lighting mechanism would be IR lighting. IR wavelengths of 850nm and 940nm (also called NIR – Near InfraRed) are commonly used in machine vision. IR reduces color of objects, glare, and reflections. IR has a longer wavelength than visible light which usually results in a greater transmission of light into a material through materials like paper, cloth and plastic. IR wavelengths react differently on materials and coatings than visible light, so certain defects and flaw detection can be identified with IR where visible light did not work. One drawback would be that IR lighting changes the color of the cats fur in the image and therefore, our machine learning model would have to be trained on IR images. This dataset is hard to find.
Here are the interconnections between all the parts, along with the software we need to write:
And an events diagram for what happens after what, which will be converted into a flowchart of images:
I some did reading on computer vision, specifically motion detection and tracking. Basically, we can detect motion by taking the average of the past ten frames and comparing it to the current frame. Read the team status update for more details!
Onwards to design review!
Irene