Award Abstract # 2026276
FW-HTF-RL: Collaborative Research: The Future of Remanufacturing: Human-Robot Collaboration for Disassembly of End-of-Use Products

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: UNIVERSITY OF FLORIDA
Initial Amendment Date: August 25, 2020
Latest Amendment Date: October 14, 2020
Award Number: 2026276
Award Instrument: Standard Grant
Program Manager: Alexandra Medina-Borja
amedinab@nsf.gov
 (703)292-7557
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: October 1, 2020
End Date: September 30, 2025 (Estimated)
Total Intended Award Amount: $1,514,197.00
Total Awarded Amount to Date: $1,514,197.00
Funds Obligated to Date: FY 2020 = $1,514,197.00
History of Investigator:
  • Sara Behdad (Principal Investigator)
    sarabehdad@ufl.edu
  • Gulcan Onel (Co-Principal Investigator)
  • Boyi Hu (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Florida
1523 UNION RD RM 207
GAINESVILLE
FL  US  32611-1941
(352)392-3516
Sponsor Congressional District: 03
Primary Place of Performance: University of Florida
Environmental Engineering
Gainesville
FL  US  32611-0001
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): NNFQH1JAPEP3
Parent UEI:
NSF Program(s): FW-HTF-Adv Cogn & Phys Capblty,
FW-HTF Futr Wrk Hum-Tech Frntr
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 063Z, 9102
Program Element Code(s): 082Y00, 103Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

This Future of Work at the Human Technology Frontier (FW-HTF) project will advance effective human-robot collaboration (HRC) to reduce electronics remanufacturing costs and improve operator safety, while considering the highly complex unstructured nature of the remanufacturing environment. Scarcity of resources, environmental regulations, and potential profits from salvaging valuable materials and components have motivated consideration of end-of-use product recovery and remanufacturing. However there are significant challenges related to the labor-intensive nature of disassembly, which is an integral part of critical remanufacturing operations such as reuse, repair, maintenance, and recycling. This project focuses on robot-assisted disassembly to increase productivity, while enhancing job satisfaction and ensuring worker safety. Today, disassembly is still a predominantly labor-intensive process that requires direct contact with many elements that are potentially harmful to human health. The research will advance fundamental understanding of the way humans and robots distribute tasks, cooperate, and interact in a safe and complementary manner. Among the expected benefits of the research results are improved quality of life for remanufacturing workers, increased recycling and reduced waste for used electronic materials, the creation of new manufacturing jobs, reduced dependency on foreign sources of strategic materials, and increased stocks of domestically harvested rare earth elements. The multidisciplinary research crosses the boundaries between robotics, sustainable design, human factors, data science, and labor economics, by the joint efforts between the University at Buffalo (UB) and the University of Florida (UF). The research will positively impact engineering education and workforce development through educational and outreach activities such as workshops for K12 students, course development at both institutions, timely training of graduate students, and a set of workshops for industry and academic audiences.

The project is focused on advancing an integrated framework that utilizes the capabilities of both humans and robots in a safe, complementary, and interactive manner, towards designing an economically viable disassembly system for the remanufacturing industry. The research team will perform fundamental studies on collaborative disassembly systems by implementing five interdependent research tasks within the contexts of Future Technology, Future Worker, and Future Work: (1) work environment monitoring with human motion prediction, (2) planning, learning, and control for collaborative robots, (3) disassembly sequence planning under uncertainty and exploring HRC-inspired design guidelines, (4) human-robotics system integration, and (5) modeling and prediction of economic impacts of HRC in remanufacturing environments. Specific knowledge gaps are addressed by mutual interactions among product design guidelines, HRC, occupational safety standards, and remanufacturing labor market. The convergent research approach will allow iteratively adjusted and enhanced collaborative disassembly systems to be implemented in future remanufacturing factories.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 24)
Chen, Yuhao and Liao, Hao-Yu and Behdad, Sara and Hu, Boyi "Human activity recognition in an end-of-life consumer electronics disassembly task" Applied Ergonomics , v.113 , 2023 https://doi.org/10.1016/j.apergo.2023.104090 Citation Details
Chen, Yuhao and Luo, Yue and Yerebakan, Mustafa Ozkan and Xia, Shuyan and Behdad, Sara and Hu, Boyi "Human Workload and Ergonomics during Human-Robot Collaborative Electronic Waste Disassembly" 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS) , 2022 https://doi.org/10.1109/ICHMS56717.2022.9980828 Citation Details
Hu, Shuaizhou and Zhang, Xinyao and Liao, Hao-Yu and Liang, Xiao and Zheng, Minghui and Behdad, Sara "Deep Learning and Machine Learning Techniques to Classify Electrical and Electronic Equipment" Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2021, August 17-20, 2021 , 2021 Citation Details
Lee, Meng-Lun and Behdad, Sara and Liang, Xiao and Zheng, Minghui "Task allocation and planning for product disassembly with humanrobot collaboration" Robotics and computerintegrated manufacturing , v.76 , 2022 https://doi.org/2021.102306 Citation Details
Lee, Meng-Lun and Liang, Xiao and Hu, Boyi and Onel, Gulcan and Behdad, Sara and Zheng, Minghui "A Review of Prospects and Opportunities in Disassembly With HumanRobot Collaboration" Journal of Manufacturing Science and Engineering , v.146 , 2024 https://doi.org/10.1115/1.4063992 Citation Details
Lee, Meng-Lun and Liu, Wansong and Behdad, Sara and Liang, Xiao and Zheng, Minghui "Robot-Assisted Disassembly Sequence Planning With Real-Time Human Motion Prediction" IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2022 https://doi.org/10.1109/TSMC.2022.3185889 Citation Details
Liao, Hao-Yu and Chen, Yuhao and Hu, Boyi "Forecasting the Range of Possible Human Hand Movement in Consumer Electronics Disassembly Using Machine Learning" Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference , 2023 Citation Details
Liao, Hao-yu and Chen, Yuhao and Hu, Boyi and Behdad, Sara "Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human-Robot Collaboration" The ASME Manufacturing Science and Engineering Conference (MSEC) , 2022 https://doi.org/10.1115/MSEC2022-85383 Citation Details
Liao, Hao-yu and Chen, Yuhao and Hu, Boyi and Behdad, Sara "Optimization-Based Disassembly Sequence Planning Under Uncertainty for HumanRobot Collaboration" Journal of Mechanical Design , v.145 , 2023 https://doi.org/10.1115/1.4055901 Citation Details
Liao, Hao-Yu and Esmaeilian, Behzad and Behdad, Sara "Automated Evaluation and Rating of Product Repairability Using Artificial Intelligence-Based Approaches" Journal of Manufacturing Science and Engineering , v.146 , 2024 https://doi.org/10.1115/1.4063561 Citation Details
Liao, Hao-yu and Pulikottil, Terrin and Peeters, Jef R and Behdad, Sara "A Disassembly Score for Human-Robot Collaboration Considering Robots Capabilities" , 2024 Citation Details
(Showing: 1 - 10 of 24)

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