Human-Robot Collaboration With a Corrective Shared Controlled Robot in a Sanding Task.

corrective shared control discomfort fatigue human–robot collaboration manufacturing

Journal

Human factors
ISSN: 1547-8181
Titre abrégé: Hum Factors
Pays: United States
ID NLM: 0374660

Informations de publication

Date de publication:
08 Aug 2024
Historique:
medline: 9 8 2024
pubmed: 9 8 2024
entrez: 8 8 2024
Statut: aheadofprint

Résumé

Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human-robot collaborative (HRC) sanding task. Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-time corrections. Twenty laboratory participants removed paint using an orbital sander, both manually and with a CSC robot. A fully automated robot was also tested. The CSC robot improved subjective discomfort compared to manual sanding in the upper arm by 29.5%, lower arm by 32%, hand by 36.5%, front of the shoulder by 24%, and back of the shoulder by 17.5%. Muscle fatigue measured using EMG, was observed in the medial deltoid and flexor carpi radialis for the manual condition. The composite cognitive workload on the NASA-TLX increased by 14.3% for manual sanding due to high physical demand and effort, while mental demand was 14% greater for the CSC robot. Digital imaging showed that the CSC robot outperformed the automated condition by 7.16% for uniformity, 4.96% for quantity, and 6.06% in total. In this example, we found that human skills and techniques were integral to sanding and can be successfully incorporated into HRC systems. Humans performed the task using the CSC robot with less fatigue and discomfort. The results can influence implementation of future HRC systems in manufacturing environments.

Sections du résumé

OBJECTIVE OBJECTIVE
Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human-robot collaborative (HRC) sanding task.
BACKGROUND BACKGROUND
Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-time corrections.
METHODS METHODS
Twenty laboratory participants removed paint using an orbital sander, both manually and with a CSC robot. A fully automated robot was also tested.
RESULTS RESULTS
The CSC robot improved subjective discomfort compared to manual sanding in the upper arm by 29.5%, lower arm by 32%, hand by 36.5%, front of the shoulder by 24%, and back of the shoulder by 17.5%. Muscle fatigue measured using EMG, was observed in the medial deltoid and flexor carpi radialis for the manual condition. The composite cognitive workload on the NASA-TLX increased by 14.3% for manual sanding due to high physical demand and effort, while mental demand was 14% greater for the CSC robot. Digital imaging showed that the CSC robot outperformed the automated condition by 7.16% for uniformity, 4.96% for quantity, and 6.06% in total.
CONCLUSIONS CONCLUSIONS
In this example, we found that human skills and techniques were integral to sanding and can be successfully incorporated into HRC systems. Humans performed the task using the CSC robot with less fatigue and discomfort.
APPLICATIONS CONCLUSIONS
The results can influence implementation of future HRC systems in manufacturing environments.

Identifiants

pubmed: 39117017
doi: 10.1177/00187208241272066
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

187208241272066

Déclaration de conflit d'intérêts

Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Auteurs

Anna Konstant (A)

University of Wisconsin-Madison, USA.

Nitzan Orr (N)

University of Wisconsin-Madison, USA.

Michael Hagenow (M)

University of Wisconsin-Madison, USA.

Isabelle Gundrum (I)

University of Wisconsin-Madison, USA.

Yu Hen Hu (YH)

University of Wisconsin-Madison, USA.

Bilge Mutlu (B)

University of Wisconsin-Madison, USA.

Michael Zinn (M)

University of Wisconsin-Madison, USA.

Michael Gleicher (M)

University of Wisconsin-Madison, USA.

Robert G Radwin (RG)

University of Wisconsin-Madison, USA.

Classifications MeSH