NRI/Collaborative Research: Robotic Disassembly of High-Precision Electronic Devices

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The National Robotics Initiative (NRI) project addresses the increasing quantity of discarded high-precision electronics such as cell phones, tablets, and laptops. Current recycling methods rely on shredding after battery removal, due to high labor costs for disassembly. As a result, many valuable components are buried in landfills and not recycled. Disassembly, the first step of recycling, is more complex than assembly since there is much more variability in product type and, as a result, remanufacturing is usually not profitable. This award supports research to provide the fundamental understanding needed for the development of a novel robotic system that can effectively perform high-precision disassembly operations and make them practically and economically viable. The work has potential to mitigate labor shortages in recycling industry, reduce electronics waste, and revolutionize the remanufacturing of high-precision electronics. The research involves several disciplines including 3D sensing, deep learning, and robotics. The multidisciplinary research will be integrated into a series of educational and outreach activities which will increase the participation of underrepresented groups in research and positively impact engineering education.Unlike the robotic assembly lines that assemble products, programming robots for repetitive operations is not a feasible solution for disassembly due to the widely varying types of discarded high-precision electronics. Therefore, disassembly of high-precision electronics is significantly more complex than assembly and requires high robotic adaptability, dexterity and accuracy. The research aims to enable a novel robotic system that can accurately see, interpret, and disassemble high-precision electronics through integrated and convergent research on 3D sensing, deep learning, robotic hand design, and high-precision manipulation. In particular, the research team will (1) perform accurate 3D sensing for complex surfaces exhibiting wide ranges of optical properties and reflectivity variations; (2) design and optimize the design of deep learning architectures for 3D point cloud interpretation; and (3) design a novel lightweight cable-driven robotic hand and develop a high-precision manipulation algorithm enabling efficient learning from experience.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.