NRI/Collaborative Research: Robotic Disassembly of High-Precision Electronic Devices
NRI/合作研究:高精度电子设备的机器人拆卸
基本信息
- 批准号:2422640
- 负责人:
- 金额:$ 56.49万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-01-01 至 2025-01-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
国家机器人计划(NRI)项目旨在解决越来越多的废弃高精度电子产品,如手机,平板电脑和笔记本电脑。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,许多有价值的组件被埋在垃圾填埋场,而不是回收。回收的第一步是再制造,它比组装更复杂,因为产品类型的可变性更大,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,并使其在实际和经济上可行。这项工作有可能缓解回收行业的劳动力短缺,减少电子废物,并彻底改变高精度电子产品的再制造。该研究涉及多个学科,包括3D传感,深度学习和机器人技术。多学科研究将被整合到一系列教育和推广活动中,这将增加代表性不足的群体在研究中的参与,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,由于废弃的高精度电子产品种类繁多,因此为机器人重复操作编程并不是拆卸的可行解决方案。因此,高精度电子产品的拆卸比组装复杂得多,需要机器人高度的适应性、灵活性和准确性。该研究旨在通过对3D传感,深度学习,机器人手设计和高精度操作的集成和融合研究,实现一种新型机器人系统,可以准确地看到,解释和拆卸高精度电子产品。特别是,研究团队将(1)对具有广泛光学特性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架构的设计;(3)设计了一种新型的轻型缆索驱动机械手,并研制了一种高该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Minghui Zheng其他文献
Synergetic promoting/inhibiting mechanisms of copper/calcium compounds in the formation of persistent organic pollutants and environmentally persistent free radicals from anthracene
铜/钙化合物对蒽形成持久性有机污染物和环境持久性自由基的协同促进/抑制机制
- DOI:
10.1016/j.cej.2022.136102 - 发表时间:
2022 - 期刊:
- 影响因子:15.1
- 作者:
Bingcheng Lin;Lili Yang;Minghui Zheng;Linjun Qin;Shuting Liu;Yuxiang Sun;Changzhi Chen;Guorui Liu - 通讯作者:
Guorui Liu
Iterative Learning for Heterogeneous Systems
异构系统的迭代学习
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Zhu Chen;Xiao Liang;Minghui Zheng - 通讯作者:
Minghui Zheng
Sources of unintentionally produced polychlorinated naphthalenes
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:8.8
- 作者:
Guorui Liu;Zongwei Cai;Minghui Zheng; - 通讯作者:
Free radical mechanism of toxic organic compound formations from emo/em-chlorophenol
从 emo/em-氯酚形成有毒有机化合物的自由基机制
- DOI:
10.1016/j.jhazmat.2022.130367 - 发表时间:
2023-02-05 - 期刊:
- 影响因子:11.300
- 作者:
Xiaoyun Liu;Guorui Liu;Shuting Liu;Linjun Qin;Bingcheng Lin;Mingxuan Wang;Lili Yang;Minghui Zheng - 通讯作者:
Minghui Zheng
Intelligent Autonomous Navigation of Car-Like Unmanned Ground Vehicle via Deep Reinforcement Learning
基于深度强化学习的类车无人地面车辆智能自主导航
- DOI:
10.1016/j.ifacol.2021.11.178 - 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Shathushan Sivashangaran;Minghui Zheng - 通讯作者:
Minghui Zheng
Minghui Zheng的其他文献
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{{ truncateString('Minghui Zheng', 18)}}的其他基金
CAREER: Facilitating Autonomy of Robots Through Learning-Based Control
职业:通过基于学习的控制促进机器人的自主性
- 批准号:
2422698 - 财政年份:2024
- 资助金额:
$ 56.49万 - 项目类别:
Continuing Grant
Collaborative Research: Road Information Discovery through Privacy-Preserved Collaborative Estimation in Connected Vehicles
协作研究:通过联网车辆中保护隐私的协作估计来发现道路信息
- 批准号:
2422579 - 财政年份:2024
- 资助金额:
$ 56.49万 - 项目类别:
Standard Grant
NRI/Collaborative Research: Robotic Disassembly of High-Precision Electronic Devices
NRI/合作研究:高精度电子设备的机器人拆卸
- 批准号:
2132923 - 财政年份:2022
- 资助金额:
$ 56.49万 - 项目类别:
Standard Grant
CAREER: Facilitating Autonomy of Robots Through Learning-Based Control
职业:通过基于学习的控制促进机器人的自主性
- 批准号:
2046481 - 财政年份:2021
- 资助金额:
$ 56.49万 - 项目类别:
Continuing Grant
FW-HTF-RL: Collaborative Research: The Future of Remanufacturing: Human-Robot Collaboration for Disassembly of End-of-Use Products
FW-HTF-RL:协作研究:再制造的未来:人机协作拆卸最终产品
- 批准号:
2026533 - 财政年份:2020
- 资助金额:
$ 56.49万 - 项目类别:
Standard Grant
Collaborative Research: Road Information Discovery through Privacy-Preserved Collaborative Estimation in Connected Vehicles
协作研究:通过联网车辆中保护隐私的协作估计来发现道路信息
- 批准号:
2030375 - 财政年份:2020
- 资助金额:
$ 56.49万 - 项目类别:
Standard Grant
FW-HTF-P: Human-Robot Collaboration in Disassembly for Future Remanufacturing
FW-HTF-P:人机协作拆卸以实现未来再制造
- 批准号:
1928595 - 财政年份:2019
- 资助金额:
$ 56.49万 - 项目类别:
Standard Grant
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