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Industrial Internet of Things pp 447–472Cite as

Cyber-Physical System Intelligence

Knowledge-Based Mobile Robot Autonomy in an Industrial Scenario

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Part of the book series: Springer Series in Wireless Technology ((SSWT))

Abstract

Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.

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Notes

  1. 1.

    To meet the challenges posed by these aspects in particular requires an increased cooperation and interdisciplinary teams of manufacturing industries and computer science research, an observation shared in [56].

  2. 2.

    Supply chains describe logistic networks which comprise interlinked logistic actors [47]. In this chapter, we focus on intra-logistics for the material flow of a smart factory by a group of autonomous mobile robots.

  3. 3.

    Radio Frequency Identification, passive information retrieval via wireless communication.

  4. 4.

    While a global plan is the natural outcome for a centralized planner, a number of local plans is still a possibility, e.g., a number of concurrent planners which use fast communication for asserting plan coherence.

  5. 5.

    A distributed task-level executive may still produce a global plan, e.g., employing an approach like plan merging [1, 31].

  6. 6.

    RoboCup Logistics League website: http://www.robocup-logistics.org.

  7. 7.

    The 2015 stack is available at https://www.fawkesrobotics.org/projects/rcll2015-release/.

  8. 8.

    The lab’s website is at https://kbsg.rwth-aachen.de/teaching/WS2014/LabPRoGrAMR.

  9. 9.

    The OpenPRS project page is available at https://git.openrobots.org/projects/openprs.

  10. 10.

    YAGI stands for “Yet Another Interpreter”, its website is at http://yagi.ist.tugraz.at/.

  11. 11.

    The canonical interpreter is available at http://www.cs.toronto.edu/cogrobo/main/systems/.

  12. 12.

    Video of the final is available at https://youtu.be/_iesqH6bNsY.

  13. 13.

    For details about the referee box we refer to [47] and http://www.robocup-logistics.org/refbox.

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Acknowledgments

We thank the Carologistics RoboCup Team (https://www.carologistics.org) for their tremendous efforts to develop the system with which the case studies could be performed. It would not be without the many students putting endless hours into the project to achieve the seen performance and even implement the various different approaches. In addition to the authors, the team members in 2016 are Mostafa Gomaa, Christoph Henke, Daniel Künster, Nicolas Limpert, Matthias Löbach, Victor Mataré, Tobias Neumann, Johannes Rothe, David Schmidt, and Sebastian Schönitz.

We thank the students participating in the lab course on “Procedural Reasoning on a Group of Adaptive Mobile Robots” (https://kbsg.rwth-aachen.de/teaching/WS2014/LabPRoGrAMR) at the Knowledge-Based Systems Group, RWTH Aachen University, in winter term 2014/2015 for their efforts on implementing the RCLL scenario using OpenPRS. We especially thank Mostafa Gomaa for insightful discussions and preparing the demonstration video (https://youtu.be/5HhOROPLQkY).

T. Niemueller and F. Zwilling were supported by the German National Science Foundation (DFG) research unit FOR 1513 on Hybrid Reasoning for Intelligent Systems (https://www.hybrid-reasoning.org).

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Authors and Affiliations

  1. Knowledge-Based Systems Group, RWTH Aachen University, Aachen, Germany

    Tim Niemueller, Frederik Zwilling & Gerhard Lakemeyer

  2. IMA/ZLW & IfU, RWTH Aachen University, Aachen, Germany

    Matthias Löbach, Sebastian Reuter & Sabina Jeschke

  3. Mobile Autonomous Systems and Cognitive Robotics Institute, Aachen University of Applied Sciences, Aachen, Germany

    Alexander Ferrein

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  1. Tim Niemueller
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Correspondence to Tim Niemueller .

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Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Sabina Jeschke

  2. RWTH Aachen University, Aachen, Germany

    Christian Brecher

  3. West Virginia University, Montgomery, West Virginia, USA

    Houbing Song

  4. Howard University, Washington, District of Columbia, USA

    Danda B. Rawat

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Niemueller, T. et al. (2017). Cyber-Physical System Intelligence. In: Jeschke, S., Brecher, C., Song, H., Rawat, D. (eds) Industrial Internet of Things. Springer Series in Wireless Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-42559-7_17

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  • DOI: https://doi.org/10.1007/978-3-319-42559-7_17

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