- Award ID(s):
- NSF-PAR ID:
- Date Published:
- Journal Name:
- 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS)
- Page Range / eLocation ID:
- 1 to 6
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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As cobots become more widely introduced in the workplace and their capabilities greatly improved, there is a need to consider how they can best help their human partners.
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Disassembly currently is a labor-intensive process with limited automation. The main reason lies in the fact that disassembly usually has to address model variations from different brands, physical uncertainties resulting from component defects or damage during usage, and incomplete product information. To overcome these challenges and to automate the disassembly process through human-robot collaboration, this paper develops a disassembly sequence planner which distributes the disassembly task between human and robot in a human-robot collaborative setting. This sequence planner targets to address potential issues including distinctive products, variant orientations, and safety constraints of human operators. The proposed disassembly sequence planner identifies the locations and orientations of the to-be-disassembled items, determines the starting point, and generates the optimal dis-assembly sequence while complying with the disassembly rules and considering the safe constraints for human operators. This algorithm is validated by numerical and experimental tests: the robot can successfully locate and disassemble the pieces following the obtained optimal sequence, and complete the task via collaboration with the human operator without violating the constraints.more » « less
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