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

项目来源

美国国家科学基金(NSF)

项目主持人

Beiwen Li

项目受资助机构

UNIVERSITY OF GEORGIA

项目编号

2506209

财政年度

2025,2021

立项时间

未公开

项目级别

国家级

研究期限

未知 / 未知

受资助金额

419466.00美元

学科

未公开

学科代码

未公开

基金类别

Standard Grant

关键词

NRI-National Robotics Initiati ; COMPUTER VISION ; ROBOTICS ; Natl Robotics Initiative(NRI) ; ENVIRON CONSCIOUS DESIGN AND MANUFACTURI ; WOMEN ; MINORITY ; DISABLED ; NEC

参与者

未公开

参与机构

UNIVERSITY OF GEORGIA RESEARCH FOUNDATION,INC

项目标书摘要：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.

人员信息

Beiwen Li(Principal Investigator)：Beiwen.Li@uga.edu；

机构信息

【University of Georgia(Performance Institution)】StreetAddress：310 E CAMPUS RD RM 409,ATHENS,Georgia,United States/ZipCode：306021589；【UNIVERSITY OF GEORGIA RESEARCH FOUNDATION,INC.】StreetAddress：310 E CAMPUS RD RM 409,ATHENS,Georgia,United States/PhoneNumber：7065425939/ZipCode：306021589；

项目主管部门

Directorate for Engineering(ENG)-Division of Civil,Mechanical,and Manufacturing Innovation(CMMI)

项目官员

Jordan Berg(Email：jberg@nsf.gov；Phone：7032925365)

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  • 1.AI-driven fringe restoration for overexposed condition using a GAN-based framework

    • 关键词：
    • 3D reconstruction;Contour measurement;Frequency domain analysis;Image reconstruction;Interferometry;Optical projectors;Profilometry;Projection systems;Restoration;Adversarial networks;Condition;Fourier;Frequency domains;Fringe pattern;Fringe projection profilometry;Highly reflective;Overexposure compensation;Phase information;Reflective surfaces
    • Cheng, Yang;Li, Beiwen
    • 《6th Applied Optical Metrology》
    • 2025年
    • August 4, 2025 - August 6, 2025
    • San Diego, CA, United states
    • 会议

    Fringe projection profilometry (FPP) is prone to overexposure when scanning highly reflective surfaces, causing fringe saturation and loss of phase information. We propose FDCGAN, a frequency-domain constrained GAN that restores saturated fringe patterns by designing frequency-aware networks with Fourier-based loss functions. Trained on synthetic data and fine-tuned with real measurements, FDCGAN achieves superior performance in fringe recovery and 3D reconstruction under extreme exposure. The method is practical for industrial and biomedical FPP applications facing challenging lighting conditions. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

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  • 2.SINGLE SHOT 3D SHAPE MEASUREMENT OF NON-VOLATILE DATA STORAGE DEVICES

    • 关键词：
    • Computer aided design;Deep learning;Municipal solid waste;Recycling;Robotics;Three dimensional computer graphics;Virtual storage;3-d shape measurement;Data storage devices;Electronics wastes;Fringe images;Fringe projection profilometry;Hard disc;High-accuracy;Measurements of;Non-volatile data;Single-shot
    • Balasubramaniam, Badrinath;Li, Beiwen
    • 《ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023》
    • 2023年
    • June 12, 2023 - June 16, 2023
    • New Brunswick, NJ, United states
    • 会议

    The value of electronic waste at present is estimated to increase rapidly year after year, and with rapid advances in electronics, shows no signs of slowing down. Storage devices such as SATA Hard Disks and Solid State Devices are electronic devices with high value recyclable raw materials which often goes unrecovered. Most of the e-waste currently generated, including HDDs, is either managed by the informal recycling sector, or is improperly landfilled with the municipal solid waste, primarily due to insufficient recovery infrastructure and labor shortage in the recycling industry. This emphasizes the importance of developing modern advanced recycling technologies such as robotic disassembly. Performing smooth robotic disassembly operations of precision electronics necessitates fast and accurate geometric 3D profiling to provide a quick and precise location of key components. Fringe Projection Profilometry (FPP), as a variation of the well-known structured light technology, provides both the high speed and high accuracy needed to accomplish this. However, Using FPP for disassembly of high-precision electronics such as hard disks can be especially challenging, given that the hard disk platter is almost completely reflective. Furthermore, the metallic nature of its various components make it difficult to render an accurate 3D reconstruction. To address this challenge, We have developed a single-shot approach to predict the 3D point cloud of these devices using a combination of computer graphics, fringe projection, and deep learning. We calibrate a physical FPP-based 3D shape measurement system and set up its digital twin using computer graphics. We capture HDD and SSD CAD models at various orientations to generate virtual training datasets consisting of fringe images and their point cloud reconstructions. This is used to train the U-NET which is then found efficient to predict the depth of the parts to a high accuracy with only a single shot fringe image. This proposed technology has the potential to serve as a valuable fast 3D vision tool for robotic re-manufacturing and is a stepping stone for building a completely automated assembly system. Copyright © 2023 by ASME.

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