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Assessing worker performance using dynamic cost functions in human robot collaborative tasks

Smith, Thomas; Benardos, Panorios; Branson, David

Authors

Thomas Smith

Panorios Benardos

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DAVID BRANSON DAVID.BRANSON@NOTTINGHAM.AC.UK
Professor of Dynamics and Control



Abstract

The aim of this research is to develop a framework to allow efficient Human Robot, HR, collaboration on manufacturing assembly tasks based on cost functions that quantify capabilities and performance of each element in a system and enable their efficient evaluation. A proposed cost function format is developed along with initial development of two example cost function variables, completion time and fatigue, obtained as each worker is completing assembly tasks. The cost function format and example variables were tested with two example tasks utilizing an ABB YuMi Robot in addition to a simulated human worker under various levels of fatigue. The total costs produced clearly identified the best worker to complete each task with these costs also clearly indicating when a human worker is fatigued to a greater or lesser degree than expected.

Citation

Smith, T., Benardos, P., & Branson, D. (2020). Assessing worker performance using dynamic cost functions in human robot collaborative tasks. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234(1), 289-301. https://doi.org/10.1177/0954406219838568

Journal Article Type Article
Acceptance Date Feb 18, 2019
Online Publication Date Mar 26, 2019
Publication Date 2020
Deposit Date Mar 7, 2019
Publicly Available Date Mar 7, 2019
Journal Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Print ISSN 0954-4062
Electronic ISSN 2041-2983
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 234
Issue 1
Pages 289-301
DOI https://doi.org/10.1177/0954406219838568
Keywords Dynamic task allocation; Robotics; Machine Learning; Scheduling; Automation
Public URL https://nottingham-repository.worktribe.com/output/1616437
Publisher URL https://journals.sagepub.com/doi/full/10.1177/0954406219838568

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