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Challenges Associated with Sensors and Data Fusion for AGV-Driven Smart Manufacturing

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12745))

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Abstract

Data fusion methods enable the precision of measurements based on information from individual systems as well as many different subsystems to be increased. Besides, the data obtained in this way enables additional conclusions drawn from their work, e.g., detecting degradation of the work of subsystems. The article focuses on the possibilities of using data fusion to create Autonomous Guided Vehicles solutions in increasing precise positioning, navigation, and cooperation with the production environment, including docking. For this purpose, it was proposed that information from other manufacturing subsystems be used. This paper aims to review the current implementation possibilities and to identify the relationship between various research sub-areas.

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Acknowledgments

The research leading to these results received funding from the Norway Grants 2014–2021, which is operated by the National Centre for Research and Development under the project “Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective” (Project Contract no.: NOR/POLNOR/CoBotAGV/0027/2019 -00) and partially by the Polish Ministry of Science and Higher Education Funds for Statutory Research.

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Authors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Adam Ziebinski, Dariusz Mrozek, Rafal Cupek, Damian Grzechca & Piotr Biernacki

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Marcin Fojcik, Erik Kyrkjebø, Jerry Chun-Wei Lin & Knut Øvsthus

  3. AIUT Sp. z o.o. (Ltd.), Gliwice, Poland

    Marek Drewniak

Authors
  1. Adam Ziebinski
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  2. Dariusz Mrozek
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  4. Damian Grzechca
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  5. Marcin Fojcik
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Corresponding author

Correspondence to Adam Ziebinski .

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Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Ziebinski, A. et al. (2021). Challenges Associated with Sensors and Data Fusion for AGV-Driven Smart Manufacturing. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_45

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  • DOI: https://doi.org/10.1007/978-3-030-77970-2_45

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