| Autor: |
Anders, Franz Ferdinand, Mewes, Alexander, Özmen, Serkan, Teppan, Julia, Woschank, Manuel |
| Předmět: |
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| Zdroj: |
IEOM African Conference Proceedings; 4/23/2024, p1212-1217, 6p |
| Abstrakt: |
This paper explores the evolving landscape of autonomous transportation systems in manufacturing, focusing on reducing manual labor and enhancing operational efficiency. Automated guided vehicles (AGVs) are widely adopted, integrating Artificial Intelligence and Big Data Applications. The imperative for companies to embrace these technologies and navigate innovation challenges is highlighted. Recent AGV developments are systematically reviewed using the PRISMA method, categorizing findings and assessing their implications. The historical evolution of autonomous vehicles, including milestones like DARPA's Grand Challenge, is traced. The methodology involves a structured literature search ensuring relevance and reliability. Results indicate increased AGV prevalence in manufacturing, offering advantages like 24-hour operation and optimized warehouse operations. The conclusion emphasizes safety, efficiency, and adaptability benefits, with future perspectives anticipating advancements driven by Artificial Intelligence. Market growth is projected to enhance accessibility, with collaborative efforts shaping regulatory frameworks. Convergence with Industry 4.0 technologies unlocks new possibilities, fostering data-driven decision-making. This paper underscores the critical role of comprehensive analyses for optimal integration into the evolving landscape of Industry 4.0. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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