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Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems

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Machine Learning for Cyber Physical Systems

Abstract

This paper presents a (ROS-based) framework for the development and assessment of (decentralized) multi-robot coordination strategies for Cyber-Physical Production Systems (CPPS) taking into account practical issues like network delays, localization inaccuracies, and availability of embedded computational power. It constitutes the base for (a) investigating the beneficial level of (de-) centrality within Automated Guided Vehicle-based CPPS, and (b) finding adequate concepts for navigation and collision handling by means of behavior-, negotiationand rule-based strategies for resolving or proactively avoiding multi-robot path planning conflicts. Applying these concepts in industrial production is assumed to increase flexibility and fault-tolerance, e. g., with respect to machine failures or delivery delays at the shopfloor level.

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Böckenkamp, A., Weichert, F., Stenzel, J., Lünsch, D. (2016). Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems. In: Niggemann, O., Beyerer, J. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48838-6_14

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  • DOI: https://doi.org/10.1007/978-3-662-48838-6_14

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  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48836-2

  • Online ISBN: 978-3-662-48838-6

  • eBook Packages: EngineeringEngineering (R0)

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