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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...
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Published in: | Arabian journal for science and engineering (2011) 2023-09, Vol.48 (9), p.11621-11644 |
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creator | Islam, Syed Osama Bin Lughmani, Waqas Akbar |
description | 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
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doi_str_mv | 10.1007/s13369-022-07490-1 |
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-value < 0.01. The proposed framework can be applied to any industrial scenario where HRC is involved like manufacturing, assembling, packaging, etc.</description><identifier>ISSN: 2193-567X</identifier><identifier>ISSN: 1319-8025</identifier><identifier>EISSN: 2191-4281</identifier><identifier>DOI: 10.1007/s13369-022-07490-1</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>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</subject><ispartof>Arabian journal for science and engineering (2011), 2023-09, Vol.48 (9), p.11621-11644</ispartof><rights>King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-45a18f00bf0ef748befd46a95434f1db0735a8e3311d61ccb8b13ecd16b48ed33</citedby><cites>FETCH-LOGICAL-c319t-45a18f00bf0ef748befd46a95434f1db0735a8e3311d61ccb8b13ecd16b48ed33</cites><orcidid>0000-0002-1593-8152</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Islam, Syed Osama Bin</creatorcontrib><creatorcontrib>Lughmani, Waqas Akbar</creatorcontrib><title>A Connective Framework for Safe Human–Robot Collaboration in Cyber-Physical Production Systems</title><title>Arabian journal for science and engineering (2011)</title><addtitle>Arab J Sci Eng</addtitle><description>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
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The proposed framework can be applied to any industrial scenario where HRC is involved like manufacturing, assembling, packaging, etc.</description><subject>Anomalies</subject><subject>Anxiety</subject><subject>Collaboration</subject><subject>Cooperation</subject><subject>Decision making</subject><subject>Engineering</subject><subject>Human performance</subject><subject>Humanities and Social Sciences</subject><subject>Industrial applications</subject><subject>Industrial safety</subject><subject>Industry 4.0</subject><subject>Intelligent manufacturing systems</subject><subject>Manufacturing</subject><subject>Manufacturing engineering</subject><subject>multidisciplinary</subject><subject>Optimization</subject><subject>Research Article-mechanical Engineering</subject><subject>Robots</subject><subject>Science</subject><issn>2193-567X</issn><issn>1319-8025</issn><issn>2191-4281</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kMFKxDAQhoMouKz7Ap4KnqOZJm3T41JcV1hwcRW8xaRNtNo2a9IqvfkOvqFPYncrePMwzMB8_wx8CJ0COQdCkgsPlMYpJmGIScJSguEATUJIAbOQw-F-pjiKk4djNPO-VIRxmkYAdIIe50Fmm0bnbfmug4WTtf6w7jUw1gUbaXSw7GrZfH9-3Vpl24GtKqmsk21pm6BsgqxX2uH1c-_LXFbB2tmiy_fLTe9bXfsTdGRk5fXst0_R_eLyLlvi1c3VdTZf4ZxC2mIWSeCGEGWINgnjSpuCxTKNGGUGCkUSGkmuKQUoYshzxRVQnRcQK8Z1QekUnY13t86-ddq34sV2rhleipBHCR8qgYEKRyp31nunjdi6spauF0DETqYYZYpBptjLFLsQHUN-gJsn7f5O_5P6AUjweSI</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Islam, Syed Osama Bin</creator><creator>Lughmani, Waqas Akbar</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-1593-8152</orcidid></search><sort><creationdate>20230901</creationdate><title>A Connective Framework for Safe Human–Robot Collaboration in Cyber-Physical Production Systems</title><author>Islam, Syed Osama Bin ; Lughmani, Waqas Akbar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-45a18f00bf0ef748befd46a95434f1db0735a8e3311d61ccb8b13ecd16b48ed33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Anomalies</topic><topic>Anxiety</topic><topic>Collaboration</topic><topic>Cooperation</topic><topic>Decision making</topic><topic>Engineering</topic><topic>Human performance</topic><topic>Humanities and Social Sciences</topic><topic>Industrial applications</topic><topic>Industrial safety</topic><topic>Industry 4.0</topic><topic>Intelligent manufacturing systems</topic><topic>Manufacturing</topic><topic>Manufacturing engineering</topic><topic>multidisciplinary</topic><topic>Optimization</topic><topic>Research Article-mechanical Engineering</topic><topic>Robots</topic><topic>Science</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Islam, Syed Osama Bin</creatorcontrib><creatorcontrib>Lughmani, Waqas Akbar</creatorcontrib><collection>CrossRef</collection><jtitle>Arabian journal for science and engineering (2011)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Islam, Syed Osama Bin</au><au>Lughmani, Waqas Akbar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Connective Framework for Safe Human–Robot Collaboration in Cyber-Physical Production Systems</atitle><jtitle>Arabian journal for science and engineering (2011)</jtitle><stitle>Arab J Sci Eng</stitle><date>2023-09-01</date><risdate>2023</risdate><volume>48</volume><issue>9</issue><spage>11621</spage><epage>11644</epage><pages>11621-11644</pages><issn>2193-567X</issn><issn>1319-8025</issn><eissn>2191-4281</eissn><abstract>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
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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 |
title | A Connective Framework for Safe Human–Robot Collaboration in Cyber-Physical Production Systems |
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