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.
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Human Workload and Ergonomics during Human-Robot Collaborative Electronic Waste Disassembly
A rapid rise in the recycling and remanufacturing of end-of-use electronic waste (e-waste) has been observed due to multiple factors including our increased dependence on electronic products and the lack of resources to meet the demand. E-waste disassembly, which is the operation of extracting valuable components for recycling purposes, has received ever increasing attention as it can serve both the economy and the environment. Traditionally, e-waste disassembly is labor intensive with significant occupational hazards. To reduce labor costs and enhance working efficiency, collaborative robots (cobots) might be a viable option and the feasibility of deploying cobots in high-risk or low value-added e-waste disassembly operations is of tremendous significance to be investigated. Therefore, the major objective of this study was to evaluate the effects of working with a cobot during e-waste disassembly processes on human workload and ergonomics through a human subject experiment. Statistical results revealed that using a cobot to assist participants with the desktop disassembly task reduced the sum of the NASA-TLX scores significantly compared to disassembling by themselves (p = 0.001). With regard to ergonomics, a significant reduction was observed in participants’ mean L5/S1 flexion angle as well as mean shoulder flexion angle on both sides when working with the cobot (p < 0.001). However, participants took a significantly longer time to accomplish the disassembly task when working with the cobot (p < 0.001), indicating a trade-off of deploying cobot in the e-waste disassembly process. Results from this study could advance the knowledge of how human workers would behave and react during human-robot collaborative e-waste disassembly tasks and shed light on the design of better HRC for this specific context.
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- Award ID(s):
- 2026276
- NSF-PAR ID:
- 10465131
- Date Published:
- Journal Name:
- 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Objective Trade-offs between productivity, physical workload (PWL), and mental workload (MWL) were studied when integrating collaborative robots (cobots) into existing manual work by optimizing the allocation of tasks.
Background 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.
Methods A theoretical data-driven analysis was conducted using the O*NET Content Model to evaluate 16 selected jobs for associated work context, skills, and constraints. Associated work activities were ranked by potential for substitution by a cobot. PWL and MWL were estimated using variables from the O*Net database that represent variables for the Strain Index and NASA-TLX. An algorithm was developed to optimize work activity assignment to cobots and human workers according to their most suited abilities.
Results Human workload for some jobs decreased while workload for some jobs increased after cobots were reassigned tasks, and residual human capacity was used to perform job activities designated the most important to increase productivity. The human workload for other jobs remained unchanged.
Conclusions The changes in human workload from the introduction of cobots may not always be beneficial for the human worker unless trade-offs are considered. Application: The framework of this study may be applied to existing jobs to identify the relationship between productivity and worker tolerances that integrate cobots into specific tasks.
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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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Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g., collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g., pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use.more » « less
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Digital games featuring programmable agents are popular tools for teaching coding and computational thinking skills. However, today's games perpetuate an arguably obsolete relationship between programmable agents and human operators. Borrowing from the field of human-robotics interaction, we argue that collaborative robots, or cobots, are a better model for thinking about computational agents, working directly with humans rather than in place of or at arm's length from them. In this paper, we describe an initial design inquiry into the design of “cobot games”, programmable agent scenarios in which players program an in-game ally to assist them in accomplishing gameplay objectives. We detail three questions that emerged out of this exploration, our present thinking on them, and plans for deepening inquiry into cobot game design moving forward.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICHMS56717.2022.9980828
