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A Probabilistic Topological Approach to Feature Identification Using a Stochastic Robotic Swarm

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Distributed Autonomous Robotic Systems

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

Abstract

This paper presents a novel automated approach to quantifying the topological features of an unknown environment using a swarm of robots with local sensing and limited or no access to global position information. The robots randomly explore the environment and record a time series of their estimated position and the covariance matrix associated with this estimate. After the robots’ deployment, a point cloud indicating the free space of the environment is extracted from their aggregated data. Tools from topological data analysis, in particular the concept of persistent homology, are applied to a subset of the point cloud to construct barcode diagrams, which are used to determine the numbers of different types of features in the domain. We demonstrate that our approach can correctly identify the number of topological features in simulations with zero to four features and in multi-robot experiments with one to three features.

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Acknowledgements

R.K.R. thanks Dr. Subhrajit Bhattacharya for his valuable input on this work. This work was supported by NSF Award CMMI-1363499 and DARPA Young Faculty Award D14AP00054.

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Authors and Affiliations

  1. School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USA

    Ragesh K. Ramachandran, Sean Wilson & Spring Berman

Authors
  1. Ragesh K. Ramachandran
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  2. Sean Wilson
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  3. Spring Berman
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Corresponding author

Correspondence to Spring Berman .

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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Ramachandran, R.K., Wilson, S., Berman, S. (2018). A Probabilistic Topological Approach to Feature Identification Using a Stochastic Robotic Swarm. 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_1

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  • DOI: https://doi.org/10.1007/978-3-319-73008-0_1

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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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