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Towards autonomously navigating and cooperating vehicles in cyber-physical production systems

: Böckenkamp, Adrian; Weichert, Frank; Stenzel, Jonas; Lünsch, Dennis


Niggemann, Oliver (Ed.); Beyerer, Jürgen (Ed.):
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2015
Berlin: Springer Vieweg, 2016 (Technologien für die intelligente Automation 1)
ISBN: 978-3-662-48836-2 (print)
ISBN: 978-3-662-48838-6 (online)
Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS) <1, 2015, Lemgo>
Fraunhofer IML ()
decentralized coordination; interoperability; cyber-physical production systems; autonomous navigation; Robot Operating System

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.