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This content will become publicly available on March 11, 2025

Title: End User Interfaces for Human-Robot Collaboration
Collaborative robots (cobots) are increasingly utilized within the manufacturing industry. However, despite the promise of collaboration and easier programming when compared to traditional industrial robots, cobots introduce new interaction paradigms that require more thought about the environment and distribution of tasks to fully realize their collaboration capabilities. Due to these additional requirements, these collaboration capabilities are underutilized in current manufacturing. Therefore, to make cobots more accessible and easy to use, new systems need to be developed that support users during interaction. In this research, we propose a set of tools that target cobot use for multiple groups of individuals that use them, to better support users and simplify cobot collaboration.  more » « less
Award ID(s):
2026478 1925043
NSF-PAR ID:
10505247
Author(s) / Creator(s):
;
Publisher / Repository:
ACM
Date Published:
Page Range / eLocation ID:
157 to 159
Format(s):
Medium: X
Location:
Boulder CO USA
Sponsoring Org:
National Science Foundation
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  3. Ruis, Andrew R. ; Lee, Seung B. (Ed.)
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  5. 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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