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A Correlated Random Walk Model to Rapidly Approximate Hitting Time Distributions in Multi-robot Systems

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Intelligent Autonomous Systems 17 (IAS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 577))

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Abstract

Multi-robot systems are frequently used for tasks involving searching, so it is important to be able to estimate the searching time. Yet, simulation approaches and real-world experiments to determine searching time can be cumbersome and even impractical. In this work, we propose a correlated-random-walk based model to efficiently approximate hitting time distributions of multi-robot systems in large arenas. We verified the computational results by using ARGoS, a physics-based simulator. We found that the Gamma distribution can provide a good fit to the hitting time distributions of random walkers.

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Acknowledgements

This research was partially supported by the University of Minnesota Robotics Institute.

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Authors and Affiliations

  1. Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Yi Zhang, Daniel Boley, John Harwell & Maria Gini

Authors
  1. Yi Zhang
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  2. Daniel Boley
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  3. John Harwell
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  4. Maria Gini
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Corresponding author

Correspondence to Daniel Boley .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, University of Zagreb, Zagreb, Croatia

    Ivan Petrovic

  2. Department of Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  3. Faculty of Electrical Engineering, University of Zagreb, Zagreb, Croatia

    Ivan Marković

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Zhang, Y., Boley, D., Harwell, J., Gini, M. (2023). A Correlated Random Walk Model to Rapidly Approximate Hitting Time Distributions in Multi-robot Systems. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_48

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