{"id":91995,"date":"2014-12-15T12:00:00","date_gmt":"2014-12-15T12:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/human-machine-interface-for-automated-guided-vehicles\/"},"modified":"2014-12-15T12:00:00","modified_gmt":"2014-12-15T12:00:00","slug":"human-machine-interface-for-automated-guided-vehicles","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/human-machine-interface-for-automated-guided-vehicles\/","title":{"rendered":"Human-Machine-Interface for Automated Guided Vehicles"},"content":{"rendered":"

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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
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Industrial Application of Immersive Technologies<\/h4>\n
Exploring XR solutions for training, instruction, design review, and assembly planning<\/div>
Andreas Straube<\/a> \n \"ORCID<\/a>, Faikar Zakky Haidar<\/a> \n \"ORCID<\/a>, Matheus Lenzi dos Santos<\/a> \n \"ORCID<\/a>, Kussai AI Jairoud<\/a> \n \"ORCID<\/a>, Eduardo Koscianski<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In recent years, the decreasing cost and improved usability of immersive hardware and software have made extended reality (XR) increasingly attractive for industrial applications. Stand-alone systems with inside-out tracking and camera-based pass-through enable accessible mixed reality (MR) solutions. At the same time, emerging no-code software platforms allow engineers to create XR environments without programming expertise, broadening adoption across production settings. This paper explores key industrial application areas of immersive technologies through selected commercially available XR software solutions for product and process training, spatial instructions and guides, collaborative design review, and assembly and production planning. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 38-47 | DOI 10.30844\/I4SE.26.3.4<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Learning\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Learning Factories for the Future of Manufacturing in Brazil<\/h4>\n
Advancing manufacturing through technology and skills development<\/div> <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>\nManufacturing firms in developing countries face challenges in closing productivity gaps while adopting Industry 4.0 technologies. Learning factories are one helpful approach to countering these challenges. One such example is the learning factory F\u00e1brica do Futuroin S\u00e3o Paulo, Brazil, which has engaged students, supported competence development, and collaborated with industry in applied research, functioning as a hub for advanced manufacturing initiatives. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Serious\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Serious Gaming and the Energy Transition<\/h4>\n
Collaborative knowledge generation and interactive understanding of complex interrelationships<\/div>
Janine Gondolf<\/a> \n \"ORCID<\/a>, Gert Mehlmann<\/a>, J\u00f6rn Hartung<\/a>, Bernd Schweinshaut<\/a>, Anne Bauer<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 62-69<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Experiencing\n <\/picture>\n <\/div>\n
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Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Jan Schickram<\/a>, Tareq Albeesh<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Collaborative\n <\/picture>\n <\/div>\n
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\t\t

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
\n \n
\n \n \n \n \"XAI\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

XAI for Predicting and Nudging Worker Decision-Making<\/h4>\n
Feasibility and perceived ethical issues<\/div>
Jan-Phillip Herrmann<\/a> \n \"ORCID<\/a>, Catharina Baier<\/a>, Sven Tackenberg<\/a> \n \"ORCID<\/a>, Verena Nitsch<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Explainable artificial intelligence (XAI)-based nudging, while ethically complex, may offer a favorable alternative to rigid, algorithmically generated schedules that simultaneously respects worker autonomy and improves overall scheduling performance on the shop floor. This paper presents a controlled laboratory study demonstrating the successful nudging of 28 industrial engineering students in a job shop simulation. The study shows that the observed concordance between students\u2019 sequencing decisions and a predefined target sequence increases by 9% through nudging. This is done by using XAI to analyze students\u2019 preferences and adjusting task deadlines and priorities in the simulation. The paper discusses the ethical issues of nudging, including potential manipulation, illusory autonomy, and reducing people to numbers. To mitigate these issues, it offers recommendations for implementing the XAI-based nudging approach in practice and highlights its strengths relative to rigid, ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 70-78<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

The human being has the ability to adapt to physical changes in the production and logistic environment. The aim of this research project is to equip series transport vehicle with additional intelligent technique so that the vehicle becomes an automated guided vehicle (AGV) and to reduce the commissioning effort for AGVs. The user can control the AGV by voice and gesture so the AGV can do the work automatically. To realize such an interactive system it is important to develop decentralized controllers for the AGV. Thus, it is possible that the AGV is able to flexibly adapt his own behaviour.<\/p>\n","protected":false},"featured_media":97339,"menu_order":0,"template":"","categories":[],"tags":[],"product_cat":[79304],"topic":[67701],"technology":[67790],"knowhow":[],"industry":[],"writer":[82393,82172,82302,81124],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-91995","article","type-article","status-publish","has-post-thumbnail","product_cat-articles","topic-production-system","technology-artificial-intelligence","writer-florian-podszus-en","writer-georg-ullmann-en","writer-lars-dohrmann-en","writer-ludger-overmeyer-en","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/dohrmann_Mensch_Maschine_Interaktion_fuer_Fahrerlose_Transportfahrzeuge_IM_14_6-1400x788-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Christoph Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"The human being has the ability to adapt to physical changes in the production and logistic environment. The aim of this research project is to equip series transport vehicle with additional intelligent technique so that the vehicle becomes an automated guided vehicle (AGV) and to reduce the commissioning effort for AGVs. The user can control…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/91995","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/97339"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=91995"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=91995"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=91995"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=91995"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=91995"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=91995"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=91995"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=91995"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=91995"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=91995"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=91995"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=91995"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=91995"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}