{"id":103290,"date":"2024-04-15T12:00:00","date_gmt":"2024-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/lagerbestandserfassung-mit-luftschiff-drohnen\/"},"modified":"2025-02-04T16:59:51","modified_gmt":"2025-02-04T15:59:51","slug":"warehouse-inventory-detection-with-airship-drones","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/warehouse-inventory-detection-with-airship-drones\/","title":{"rendered":"Warehouse Inventory Detection with Airship Drones"},"content":{"rendered":"

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The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n−<\/td>\n26,10 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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Optimized Manual Processes in Automotive Production<\/h4>\n
A module-based approach for the efficient creation of work system simulations<\/div>
Barbara Brockmann<\/a>, Tobias Jurk<\/a>, Beate Stoffels<\/a>, Jochen Deuse<\/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>In the manufacturing industry, the integration of digital human models into the product development and manufacturing process is becoming increasingly important. Particularly in assembly, which is characterized by a high proportion of manual tasks, motion simulations enable a realistic representation of human work and thus make a significant contribution to the evaluation of motion economy, process validation, and efficiency improvement. However, widespread application in production planning faces various challenges, such as the high initial effort required to create human simulations as well as volatile planning conditions. This article presents a practice-oriented solution from the automotive assembly sector that enables the creation of simulations with reduced effort as well as their early and consistent use in the planning process. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 48-55<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Digital Factory Planning for Startups<\/h4>\n
A simulation-based production structure design<\/div>
Herwig Winkler<\/a> \n \"ORCID<\/a>, Tobias Isau<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n With the increasing complexity of production and logistics systems, traditional factory planning approaches are reaching their limits. In this context, digital factory planning offers a promising solution for enabling well-informed decisions, particularly during the early planning phases. For startups, the optimal planning of a production facility is challenging, as they often operate with limited financial and infrastructural resources. This paper presents a methodological approach to digital factory planning that utilizes VR simulation for the layout planning of a factory hall for a young company in the solar industry. The proposed approach demonstrates how simulations can support the design of flexible production structures, particularly in startup environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 68-75<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Site\n <\/picture>\n <\/div>\n
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Site Assessment for Flexible Intralogistics in Brownfield Sites<\/h4>\n
Innovative decision support in practice<\/div>
Jolanda Schierbaum<\/a>, Carsten Feldmann<\/a>, Lars Renhof<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Due to its dynamic environment, space planning in intralogistics is not a one-time task but a recurring decision-making process subject to numerous constraints imposed by existing infrastructure. Decisions are often based on incomplete data, resulting in a high risk of poor planning decisions and inefficient use of space. This paper presents a practice-oriented process model for space evaluation in brownfield projects. The proposed approach improves the standardization and consistency of space evaluation and promotes best practices among all stakeholders. By supporting systematic decision-making, the process model contributes to optimized planning and resource allocation, thereby reducing risks and avoiding costly implementation errors.The process model is demonstrated through a case study conducted at a commercial vehicle manufacturer. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 124-133<\/div> <\/div>\n <\/div>\n <\/a>\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>
Jan Schickram<\/a>, Tareq Albeesh<\/a>, Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/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
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\n \n \n \n \"XAI\n <\/picture>\n <\/div>\n
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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
\n \n
\n \n \n \n \"Improving\n <\/picture>\n <\/div>\n
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Improving Documentation Quality and Creating Time for Core Activities<\/h4>\n
Success factors for implementing AI-based documentation systems in nursing care<\/div>
Sophie Berretta<\/a> \n \"ORCID<\/a>, Elisabeth Liedmann<\/a> \n \"ORCID<\/a>, Paul-Fiete Kramer<\/a> \n \"ORCID<\/a>, Anja Gerlmaier<\/a>, Christopher Schmidt<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Demographic change is accompanied by both a growing demand for care and a shortage of qualified nursing staff. Consequently, AI-based technologies are increasingly becoming a focus of care-related innovations. Their aim is to reduce workload pressure, save time, and enhance the attractiveness of the nursing profession. Using the example of AI-supported documentation systems for admission interviews, this article examines to what extent such systems can contribute to improvements in work processes and care quality, focusing on the perspectives of nursing professionals and nursing experts. The results indicate potential for workload relief, enhanced documentation quality, and the reallocation of time resources toward direct patient care. However, realizing these potentials requires a human-centered and context-sensitive implementation approach. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 154-160 | DOI 10.30844\/I4SE.26.1.146<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

The highly dynamic nature of block storage facilities limits the feasibility of manual stock recording. The frequent relocation of pallets, crates or pallet cages without fixed storage locations makes stock recording time-consuming and error-prone. Goods often have to be searched for. Improper storage can also lead to damage. The use of (semi-)autonomous vehicles has the potential to remedy this situation and enable automatic stocktaking via optical detection of goods This article describes the use of (semi-)autonomous airship drones for indoor areas, as these have a significantly longer operating time of several hours compared to classic multicopters. Specifically, a particularly lightweight sensor system was developed for the airship drone in order to realize 360-degree environment detection and to recognize relevant objects (pallets, people, pallet stacks). The functionality of the system was successfully tested in a realistic test scenario.<\/p>\n","protected":false},"featured_media":107475,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[79358,79359,79360,79361,79362,79363,79364,79365,79366,79367,79368,79369,79370],"product_cat":[79304],"topic":[68005,79371],"technology":[67790],"knowhow":[],"industry":[],"writer":[82424,83729,83728,80740,81209],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-103290","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-bestandserfassung-en","tag-deep-learning-en","tag-inventur-en","tag-inventurdrohne-en","tag-ki-en","tag-lagerverwaltungssysteme-en","tag-lagerwirtschaft-en","tag-logistik-en","tag-luftschiffdrohne-en","tag-objekterkennung-en","tag-palettenerkennung-en","tag-produktion-en","tag-qualitaetspruefung-en","product_cat-articles","topic-automation","topic-logistics","technology-artificial-intelligence","writer-benjamin-staar-en","writer-britta-hilt-en","writer-dmitrij-boger-en","writer-michael-freitag-en","writer-michael-luetjen-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\/04\/AdobeStock_647003529-min.jpeg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-150x150.jpeg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-666x375.jpeg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-768x432.jpeg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-1024x576.jpeg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-1032x320.jpeg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-764x376.jpeg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-392x320.jpeg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-608x496.jpeg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-640x325.jpeg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-274x376.jpeg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-514x292.jpeg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-320x440.jpeg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-514x289.jpeg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-196x180.jpeg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min.jpeg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min.jpeg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-510x510.jpeg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-510x287.jpeg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-100x100.jpeg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_647003529-min-64x36.jpeg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"The highly dynamic nature of block storage facilities limits the feasibility of manual stock recording. The frequent relocation of pallets, crates or pallet cages without fixed storage locations makes stock recording time-consuming and error-prone. Goods often have to be searched for. Improper storage can also lead to damage. The use of (semi-)autonomous vehicles has the…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/103290","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\/107475"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103290"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=103290"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=103290"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=103290"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=103290"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=103290"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=103290"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=103290"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=103290"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=103290"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=103290"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=103290"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=103290"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}