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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A Trust-Assist Framework for Human–Robot Co-Carry Tasks
Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework.
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- NSF-PAR ID:
- 10423874
- Date Published:
- Journal Name:
- Robotics
- Volume:
- 12
- Issue:
- 2
- ISSN:
- 2218-6581
- Page Range / eLocation ID:
- 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments, where they operate side by side with humans to achieve shared tasks. However, little progress has been made toward the development of systems that are truly effective in supporting the human, proactive in their collaboration, and that can autonomously take care of part of the task. In this work, we present a collaborative system capable of assisting a human worker despite limited manipulation capabilities, incomplete model of the task, and partial observability of the environment. Our framework leverages information from a high-level, hierarchical model that is shared between the human and robot and that enables transparent synchronization between the peers and mutual understanding of each other’s plan. More precisely, we firstly derive a partially observable Markov model from the high-level task representation; we then use an online Monte-Carlo solver to compute a short-horizon robot-executable plan. The resulting policy is capable of interactive replanning on-the-fly, dynamic error recovery, and identification of hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a realistic furniture construction task.more » « less
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Objective This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot’s end-effector on workers’ mental stress during handover tasks.
Background Human–robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers’ stress.
Methods Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed.
Results Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for “surprise” and “predictability” subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front.
Conclusion The mental stress of workers during HRC is lower when the robot’s end effector (1) approaches a worker within the worker’s field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory.
Application The outcome of this study can serve as a guide to design HRC tasks with a low level of workers’ mental stress.
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null (Ed.)To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition and prediction into the robot controller is critical for real-time awareness, response and communication inside a heterogeneous environment (robots, humans, equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot’s decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human-robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI’s underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human-robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/robotics12020030
