{"title":"The collaboration scale: A novel approach for assessing robotic systems collaboration capabilities","authors":"Federico Barravecchia, Riccardo Gervasi, Luca Mastrogiacomo, Fiorenzo Franceschini","doi":"10.1016/j.rcim.2025.103062","DOIUrl":null,"url":null,"abstract":"<div><div>In the transformative landscape of Industry 4.0 and the impending transition to Industry 5.0, the paradigm of collaborative robotics is emerging as a cornerstone, combining human and robotic distinctive abilities. This intersection is leading to a new era of 'human-centric' manufacturing, where the integration of human with robots is not just an option, but a need. In particular, the shift towards Industry 5.0 highlights the return of the human element to technological processes, emphasising adaptability, customization, and collaboration between humans and machines.</div><div>In this context, this study introduces the <em>Collaboration Scale,</em> a metric designed to evaluate the collaborative capabilities of robotic systems within this human-centred framework. This scale provides clear levels of collaboration across five foundational dimensions: <em>Situation awareness, Adaptivity, Communication, Learning</em>, and <em>Mobility</em>.</div><div>The proposed scale has three objectives: (i) establishing a common language for practitioners and researchers, (ii) promoting innovation and standardisation in collaborative robotics, and (iii) providing a practical tool for assessing and comparing the collaborative capabilities of different systems.</div><div>The framework aims to bridge the gap between current capabilities and future aspirations in robotics, while also promoting a human-centric approach for Industry 5.0.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"96 ","pages":"Article 103062"},"PeriodicalIF":9.1000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584525001164","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In the transformative landscape of Industry 4.0 and the impending transition to Industry 5.0, the paradigm of collaborative robotics is emerging as a cornerstone, combining human and robotic distinctive abilities. This intersection is leading to a new era of 'human-centric' manufacturing, where the integration of human with robots is not just an option, but a need. In particular, the shift towards Industry 5.0 highlights the return of the human element to technological processes, emphasising adaptability, customization, and collaboration between humans and machines.
In this context, this study introduces the Collaboration Scale, a metric designed to evaluate the collaborative capabilities of robotic systems within this human-centred framework. This scale provides clear levels of collaboration across five foundational dimensions: Situation awareness, Adaptivity, Communication, Learning, and Mobility.
The proposed scale has three objectives: (i) establishing a common language for practitioners and researchers, (ii) promoting innovation and standardisation in collaborative robotics, and (iii) providing a practical tool for assessing and comparing the collaborative capabilities of different systems.
The framework aims to bridge the gap between current capabilities and future aspirations in robotics, while also promoting a human-centric approach for Industry 5.0.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.