We are using machine learning and object recognition to build the first robotics automated electronic waste recycling process. With many electronics reaching their end of life cycle everyday, many end up in land fields, posing danger of both respiratory and cardiovascular diseases caused by heavy metals such as lead in decaying electronics. Efforts to recycle are inefficient, expensive and labor intensive. The people in recycling plants are also exposed to health hazards especially from inhaling gases from CRT.
We are currently training an algorithm that can on the architecture of different electronics. When done, our system will be able to distinguish between reusable components of electronics, automatically disassemble the components and reverse engineer the boards to set functionalities. We have reached a 60% success with the model on disassembling phones, and reversing the motherboards to different module, our AVR boards.
We plan to license our algorithms to existing e-waste recyclers as well as selling the robotics systems to them . this will improve the recycling process by upto 30% reducing the effects of e-waste on our land fields. It will also reduce the carbon emissions caused by burning of electronic waste in order to extract precious metals from them. It will also enable us reach the 100% reuse policy for electronics.