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Bundling Policies for Sequential Stochastic Tasks in Multi-robot Systems

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Distributed Autonomous Robotic Systems

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 6))

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Abstract

This paper studies multi-robot task allocation in settings where tasks are revealed sequentially for an infinite or indefinite time horizon, and where robots may execute bundles of tasks. The tasks are assumed to be synergistic so efficiency gains accrue from performing more tasks together. Since there is a tension between the performance cost (e.g., fuel per task) and the task completion time, a robot needs to decide when to stop collecting tasks and to begin executing its whole bundle. This paper explores the problem of optimizing bundle size with respect to the two objectives and their trade-off. Based on qualitative properties of any multi-robot system that bundles sequential stochastic tasks, we introduce and explore an assortment of simple bundling policies. Our experiments examine how these policies perform in a warehouse automation scenario, showing that they are efficient compared to baseline policies where robots do not bundle tasks strategically.

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Notes

  1. 1.

    Again, robots may employ more advanced methods rather than this in-order task bundling and execution. The focus of this paper is not on elaborate methods for these aspects, so we leave them as modules that can be replaced by well-designed allocation and planning methods.

  2. 2.

    The lower bound of the execution time per task is greater than the arrival interval of a task, i.e., \(E_d + 1 = 26.50 > \alpha = 2\).

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Acknowledgements

This work was supported in part by NSF awards IIS-1302393 and IIS-1453652.

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Authors and Affiliations

  1. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Changjoo Nam

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Dylan A. Shell

Authors
  1. Changjoo Nam
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  2. Dylan A. Shell
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Corresponding author

Correspondence to Changjoo Nam .

Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

    Roderich Groß

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

    Andreas Kolling

  3. School for Engineering of Matter, Transport and Energy (SEMTE), Arizona State University, Tempe, AZ, USA

    Spring Berman

  4. Massachusetts Institute of Technology, Cambridge, MA, USA

    Emilio Frazzoli

  5. ENAC, IIE, DIAL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Alcherio Martinoli

  6. Department of Mechanical Engineering and Science, Kyoto University, Kyoto, Japan

    Fumitoshi Matsuno

  7. Wyss Institute for Biologically Inspired Engineering, Harvard University Wyss Institute for Biologically Inspired, Cambridge, MA, USA

    Melvin Gauci

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Nam, C., Shell, D.A. (2018). Bundling Policies for Sequential Stochastic Tasks in Multi-robot Systems. In: Groß, R., et al. Distributed Autonomous Robotic Systems. Springer Proceedings in Advanced Robotics, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-73008-0_17

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  • DOI: https://doi.org/10.1007/978-3-319-73008-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73006-6

  • Online ISBN: 978-3-319-73008-0

  • eBook Packages: EngineeringEngineering (R0)

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