A Connective Framework for Safe Human–Robot Collaboration in Cyber-Physical Production Systems

Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing. Workspaces are transformed into fully shared spaces for performing tasks during human–robot collaboration (HRC), increasing the...

Full description

Saved in:
Bibliographic Details
Published in:Arabian journal for science and engineering (2011) 2023-09, Vol.48 (9), p.11621-11644
Main Authors: Islam, Syed Osama Bin, Lughmani, Waqas Akbar
Format: Article
Language:English
Subjects:
Anomalies
Anxiety
Collaboration
Cooperation
Decision making
Engineering
Human performance
Humanities and Social Sciences
Industrial applications
Industrial safety
Industry 4.0
Intelligent manufacturing systems
Manufacturing
Manufacturing engineering
multidisciplinary
Optimization
Research Article-mechanical Engineering
Robots
Science
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing. Workspaces are transformed into fully shared spaces for performing tasks during human–robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The interactions are quite demanding in terms of safety, employing both cognitive and physical resources. In this work, we have proposed a four-layered connective framework that can quickly respond to changing physical and psychological safety situations. The first layer performs the desired operation of the cyber-physical production system (CPPS) and gets itself aware of anomalies. The second layer assesses, categorizes and quantifies the developed situations as anxiety factor. The third layer mitigates the arising anxiety through the optimal allocation of resources. The fourth layer makes decisions based on the historical knowledge, the current state of anxiety, and the suggested optimization using logic. The results demonstrated that the proposed method improves decision-making of a CPPS, eventually increasing the productivity. The case study shows that the method is less time-intensive as the maximum time to decide on a situation was found 0.03 s. The survey highlighted the technique leads to enhanced fluency, collaboration, comfort, safety and legibility during collaboration. The proposed system was found 16.85% more accurate than the standard system. The significance test of the results highlighted the p -value
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-022-07490-1