Evaluation of Robot Degradation on Human-Robot Collaborative Performance in Manufacturing

Published

January 26, 2022

Author(s)

Vinh Nguyen, Jeremy Marvel

Abstract

Human-robot collaborative systems are highly sought by manufacturers owing to their adaptability and consistency in production tasks. However, manufacturers are still hesitant to adopt these systems because of limited quantifiable understanding of their performance and capabilities. Specifically, the influence of the degradation of collaborative industrial robots on human-robot teaming performance limits manufacturers from determining if a human-robot collaborative system will meet their demands. This paper examines the influence of robot degradation on performance metrics of human-robot collaborative systems. Defining teaming performance metrics with respect to robot degradation are outlined. In addition, the metrics are applied to a peg-in-hole case study with respect to the degradation of the joint angular encoder and current sensor. Specifically, this case study compares pure insertion vs. insertion with spatial scanning to solve the peg-in-hole problem, and manual intervention is implemented if the robot fails to solve the peg-in-hole problem. The metrics used in the case study show that pure insertion is faster and more sensitive to robot degradation, thus requiring more manual insertion, while insertion with scanning is more robust to robot degradation at the cost of slower insertion time. Thus, this paper provides knowledge and usable metrics regarding the influence of robot degradation on human-robot collaborative systems in manufacturing applications.

Citation

Smart and Sustainable Manufacturing Systems

Volume

6

Pub Type

Journals

Download Paper

https://doi.org/10.1520/SSMS20210036

Local Download

Keywords

Human-robot collaboration, manufacturing, assembly, degradation, industrial robot

Citation

Nguyen, V. and Marvel, J. (2022), Evaluation of Robot Degradation on Human-Robot Collaborative Performance in Manufacturing, Smart and Sustainable Manufacturing Systems, [online], https://doi.org/10.1520/SSMS20210036, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932188 (Accessed April 19, 2024)