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

国家机器人倡议(NRI)项目解决了手机、平板电脑和笔记本电脑等越来越多的高精度电子产品被丢弃的问题。目前的回收方法依赖于电池拆卸后的粉碎，因为拆卸的劳动力成本很高。因此，许多有价值的部件被埋在垃圾填埋场，而不是回收利用。拆卸是回收的第一步，它比组装复杂得多，因为产品类型的变化要大得多，因此，再制造通常是无利可图的。该奖项支持研究，以提供开发新型机器人系统所需的基本理解，该系统可以有效地执行高精度拆卸操作，并使其在实用和经济上可行。这项工作有可能缓解回收行业的劳动力短缺，减少电子垃圾，并彻底改变高精度电子产品的再制造。这项研究涉及多个学科，包括3D传感、深度学习和机器人学。多学科研究将被整合到一系列教育和推广活动中，这些活动将增加代表不足的群体对研究的参与，并对工程教育产生积极影响。与组装产品的机器人装配线不同，为机器人编程进行重复操作并不是拆卸的可行解决方案，因为丢弃的高精度电子产品种类繁多。因此，高精度电子产品的拆卸比装配复杂得多，对机器人的适应性、灵巧性和精确度要求很高。这项研究旨在通过对3D传感、深度学习、机械手设计和高精度操作的集成和融合研究，使新型机器人系统能够准确地看到、解释和拆卸高精度电子产品。特别是，研究团队将(1)对表现出广泛光学性质和反射率变化的复杂表面进行精确的3D传感；(2)设计和优化用于3D点云解释的深度学习体系结构的设计；以及(3)设计一种新型的轻型缆索驱动机器人手，并开发能够从经验中高效学习的高精度操纵算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.