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The Methods and Approaches of Explainable Artificial Intelligence Read chapter Read first chapter

2021 | OriginalPaper | Chapter

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

Authors : Adam Ziebinski, Dariusz Mrozek, Rafal Cupek, Damian Grzechca, Marcin Fojcik, Marek Drewniak, Erik Kyrkjebø, Jerry Chun-Wei Lin, Knut Øvsthus, Piotr Biernacki

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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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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Metadata
Title
Challenges Associated with Sensors and Data Fusion for AGV-Driven Smart Manufacturing
Authors
Adam Ziebinski
Dariusz Mrozek
Rafal Cupek
Damian Grzechca
Marcin Fojcik
Marek Drewniak
Erik Kyrkjebø
Jerry Chun-Wei Lin
Knut Øvsthus
Piotr Biernacki
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

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

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