The Long Journey From Standardization to Fully Fab Automation and More

Heinrich, Harald; Deutschlander, Arthur; Zoghlami, Feryel; Sen, Okan Kamil

doi:10.1109/tsm.2019.2945352

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…In [145], the authors described cooperative autonomous product tracking and maintenance systems using automated guided vehicles to minimize human interaction inside a smart factory. Noteworthy is the project experiences related to implementing automated solutions described by [146]. In their publication, the authors pointed out that only some recipes for introducing automation in the factory exist.…”

Section: Othermentioning

confidence: 99%

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Tubis,

Poturaj,

Smok

2024

Sustainability

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Background: This article presents the results of a literature review from 2018 to 2023, which focused on research related to human and AGV system cooperation in a shared workspace. This study defines AGV systems as systems using Automated Guided Vehicles or Autonomous Guided Vehicles. An Automated Guided Vehicle is a cart that follows a guided path, while an Autonomous Guided Vehicle is an Automated Guided Vehicle that is autonomously controlled. The analyses conducted answered two research questions: (RQ1) In what aspects are the human factor examined in publications on the implementation and operation of AGV systems? (RQ2) Has the human-AGV collaboration aspect been analyzed in the context of a sustainable work environment? Methods: The literature review was conducted following the systematic literature review method, using the PRISMA approach. Results: Based on the search of two journal databases, according to the indicated keywords, 1219 documents pertaining to the analyzed issues were identified. The selection and elimination of documents that did not meet the defined criteria made it possible to limit the number of publications to 117 articles and proceedings papers. On this basis, the authors defined a classification framework comprising five basic research categories and nine subcategories. The analyzed documents were classified, and each distinguished group was characterized by describing the results. Conclusions: The development of a two-level classification framework for research from the analyzed area according to the assumptions of the concept map and the identification of research gaps in the area of human-AGV interaction.

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Section: Othermentioning

confidence: 99%

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Tubis,

Poturaj,

Smok

2024

Sustainability

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“…To address this, robotic operations must adapt their behavior to accommodate variations in human task performance [80]. Currently, robots primarily react to pure sensor information regarding safety requirements, resulting in inadequate responses when encountering unexpected obstacles [83]. A significant drawback is that robots fail to halt their activities in the presence of unforeseen events or when humans approach the safety perimeter, as laser scanners lack the necessary information, such as 3D size, distance, or direction of objects.…”

Section: Robot-driven Logisticsmentioning

confidence: 99%

“…A significant drawback is that robots fail to halt their activities in the presence of unforeseen events or when humans approach the safety perimeter, as laser scanners lack the necessary information, such as 3D size, distance, or direction of objects. Implementing sensor elements like time-of-flight or radar sensors provides an effective solution by enabling the detection of 3D environmental information [83]. Knight [84] argues that human-robot interaction entails a balanced combination of profound comprehension of information, mechanical capabilities, sophisticated system thinking, and the ability to handle novel or unexpected phenomena while considering human interests.…”

Section: Robot-driven Logisticsmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.

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