{"id":103447,"date":"2024-02-20T13:50:29","date_gmt":"2024-02-20T12:50:29","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103447"},"modified":"2024-05-14T04:33:20","modified_gmt":"2024-05-14T02:33:20","slug":"robotics","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/tools\/robotics\/","title":{"rendered":"Robotics"},"content":{"rendered":"\n\n\n
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\n \n \n \n \"Collaborative\n <\/picture>\n <\/div>\n
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Collaborative Robots in Production Environments<\/h4>\n
Employee qualification and acceptance for human-machine interaction<\/div>
Tobias Wienzek<\/a>, Mathias Cuypers<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The introduction of new technologies poses a major challenge, especially for small and medium-sized enterprises (SMEs). At the same time, SMEs must rise to this challenge in order to keep pace technologically and economically. Employee acceptance is an important factor in ensuring that both the introduction and the long-term use of a technology are successful. At the same time, the introduction process also has a central influence on acceptance in the long term. This article uses the implementation of collaborative robotics as an example for examining such an introduction process, identifying the key factors that influence employee acceptance and the important role played by advanced employee training. It serves to highlight how the introduction process and employee training are seamlessly interlinked. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 14-21 | DOI 10.30844\/I4SE.26.2.14<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Biomechanical\n <\/picture>\n <\/div>\n
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Biomechanical Simulation Pipeline for Exoskeletons<\/h4>\n
A digital tool for the targeted development of support systems<\/div>
Robert Eberle<\/a> \n \"ORCID<\/a>, Maximilian Ebenbichler<\/a> \n \"ORCID<\/a>, Benjamin Reimeir<\/a> \n \"ORCID<\/a>, Lennart Ralfs<\/a> \n \"ORCID<\/a>, Robert Weidner<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Support systems like exoskeletons can reduce physical strain on workers in industrial workplaces. To facilitate their development, a simulation pipeline was created. This pipeline employs musculoskeletal human models coupled with an exoskeleton model, enabling detailed analyses of the biomechanical interaction between humans and exoskeletons. By implementing exoskeleton structures and integrating them into existing musculoskeletal models, the pipeline aims to optimize exoskeleton development while simultaneously enhancing their biomechanical effectiveness. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 30-36 | DOI 10.30844\/I4SE.25.5.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Camera-Based\n <\/picture>\n <\/div>\n
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Camera-Based Ergonomics Assessment<\/h4>\n
Developing a method for use in manual assembly<\/div>
Jannik Liebchen<\/a> \n \"ORCID<\/a>, Burak Vur<\/a> \n \"ORCID<\/a>, Michael Freitag<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Targeted ergonomic design of workplaces and processes can counteract the challenges of manual assembly and improve working conditions. However, current expert ergonomics assessments are time-consuming and resource-intensive. This article presents an automated assessment method based on the Rapid Upper Limb Assessment (RULA). Results from a laboratory study within an assembly scenario are consistent with expert evaluations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 120-126 | DOI 10.30844\/I4SE.25.5.116<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Intelligent\n <\/picture>\n <\/div>\n
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Intelligent Load Carrier Management<\/h4>\n
AI-supported monitoring and reduction of losses in logistics<\/div>
Dominik Augenstein<\/a>, Lea Basler<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Load carriers are essential for transporting manufactured parts in manufacturing companies. Despite their \u2018simplicity\u2019, they are usually expensive to purchase as they are manufactured expressly to fit purpose. While tracking methods such as GPS tracking can be used to prevent the loss of load carriers, this is associated with monitoring costs and presents challenges with regard to data protection as soon as the work performance of intralogistics employees is monitored. Assigning load carriers to designated clusters and monitoring these clusters provides an effective solution\u2014without drawing conclusions about employee performance. Furthermore, artificial intelligence can optimize this approach whilst also deterring the theft of load carriers. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 78-84<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Boosting Competitiveness in Small Batch Production<\/h4>\n
Scalable and flexible body-in-white production line with collaborative mobile robots<\/div>
Walid Elleuch<\/a>, Tadele Belay Tuli<\/a> \n \"ORCID<\/a>, Martin Manns<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Due to the higher customization of products to customer groups and needs, body-in-white manufacturing industries are facing higher variant assembly at the later stages of the production line, thus increasing production costs per unit. Flexible production processes that involve flexible material flows, non-rigid manufacturing sequences, and the automatic reconfiguration of tools are regarded as the pillars of a resilient production system. This article presents a conceptual solution for flexible Body-in-White sheet metal production with autonomous collaborative robotic systems to make product costs affordable for a higher competitive advantage. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 60-67<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Collaborative\n <\/picture>\n <\/div>\n
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Collaborative Drone Inspection<\/h4>\n
A new approach to inspection work with AI support<\/div>
Till Becker<\/a>, Agron Neziraj<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Drone technology and the use of artificial intelligence (AI) offer promising advantages in various sectors, including in inspection. The use of innovative inspection technologies can make inspections more efficient overall. This research project examines various legal and economic aspects of AI-based autonomous drone inspections. It also develops a target process that represents the use of an AI-based drone inspection and controls the use of such inspection technology. In particular, this article focuses on a collaborative approach to this new inspection methodology. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 94-100<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Work-Integrated\n <\/picture>\n <\/div>\n
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Work-Integrated Learning in Industry 4.0<\/h4>\n
A qualitative analysis of various assistance systems in assembly<\/div>
Kathleen Warnhoff<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In the era of Industry 4.0, many industrial companies are facing major transformations. In the process of digitalization, factory management is adopting new technologies such as cognitive assistance systems, which has led to changes in work processes. Regarding assembly in the metal and electrical industries, it is unclear to what extent this development has promoted work-integrated learning. Therefore, the topic of this paper is a qualitative analysis that explores employees' perceptions of the learning opportunities and risks presented by cognitive assistance systems. Results: Not all assembly employees benefit equally from these new developments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 20-29 | DOI 10.30844\/I4SE.25.2.20<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"How\n <\/picture>\n <\/div>\n
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How well do you know robotics and IIoT?<\/h4>\n
Test your knwoledge now!<\/div> <\/td>\n <\/tr>\n <\/table>\n
\nRobotics and the Industrial Internet of Things are two of the most important technologies of the Fourth Industrial Revolution. Do you know your way around? Test your knowledge in our exciting quiz. Expand your knowledge of intelligent machines, networked production and the future of industry. The correct answers will be displayed immediately. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Intelligent Shopfloor Assistants<\/h4>\n
Increasing productivity through the use of generative AI<\/div>
Eckart Uhlmann<\/a> \n \"ORCID<\/a>, Julian Polte<\/a> \n \"ORCID<\/a>, Christopher M\u00fchlich<\/a> \n \"ORCID<\/a>, Yassin Elsir<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>In modern production companies, a heterogeneous IT landscape often complicates day-to-day work. A promising antidote is the use of intelligent agents, which use generative AI for routine tasks and can therefore increase efficiency. Whether these intelligent systems can be successfully integrated into existing networks determines whether the flow of information can be improved and manual effort reduced. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 64-71<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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From Pixels to Presence<\/h4>\n
Transforming remote interactions with telepresence robots<\/div>
Angelika C. Bullinger<\/a> \n \"ORCID<\/a>, Danny Rueffert<\/a> \n \"ORCID<\/a>, Francisco Hernandez<\/a> \n \"ORCID<\/a>, Holger Hoffmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Telepresence Robots (TPR) support the ongoing digital transformation in work and leisure amid climate and societal changes. This article presents two cases, one set in production and one in social participation, to illustrate users\u2019 requirements, which largely coincide. Key requirements include audio and camera quality, a stable Wi-Fi connection, active and passive visual capabilities, and even floor covering. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | Edition 5 | Pages 18-25 | DOI 10.30844\/I4SD.24.5.18<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n4<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103435,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103447","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/nimesh\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103447","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/users\/461"}],"replies":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/comments?post=103447"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103447\/revisions"}],"predecessor-version":[{"id":103451,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103447\/revisions\/103451"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103435"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103447"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}