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SMT-based Planning for Robots in Smart Factories

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Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

Abstract

Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI planning can play a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. We explore the use of SMT at the core of planning techniques to deal with real-world scenarios in the emerging smart factory paradigm. We present special-purpose and general-purpose algorithms, based on current automated reasoning technology and designed to tackle complex application domains. We evaluate their effectiveness and respective merits on a logistic scenario, also extending the comparison to other state-of-the-art task planners.

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Notes

  1. 1.

    http://www.robocup-logistics.org/sim-comp.

  2. 2.

    RCLLPlan exploits a simple cost definition in its current state, i.e., minimize time to delivery for each product. However, richer goal structures could be specified.

  3. 3.

    https://github.com/timn/ros-rcll_ros/tree/master/pddl.

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Acknowledgements

The research of Arthur Bit-Monnot and Luca Pulina has been funded by the EU Commission’s H2020 Program under grant agreement N.732105 (CERBERO project). The research of Luca Pulina has been also partially funded by the Sardinian Regional Project PROSSIMO (POR FESR 2014/20-ASSE I) and the FitOptiVis (ID: 783162) project.

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Authors and Affiliations

  1. University of Sassari, Sassari, Italy

    Arthur Bit-Monnot, Francesco Leofante & Luca Pulina

  2. RWTH Aachen University, Aachen, Germany

    Francesco Leofante

  3. University of Genoa, Genoa, Italy

    Francesco Leofante & Armando Tacchella

Authors
  1. Arthur Bit-Monnot
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  2. Francesco Leofante
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  3. Luca Pulina
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  4. Armando Tacchella
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Corresponding author

Correspondence to Arthur Bit-Monnot .

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Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

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Bit-Monnot, A., Leofante, F., Pulina, L., Tacchella, A. (2019). SMT-based Planning for Robots in Smart Factories. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_58

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  • DOI: https://doi.org/10.1007/978-3-030-22999-3_58

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  • Online ISBN: 978-3-030-22999-3

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