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Estimating boundary dynamics using robotic sensor networks with pointwise measurements

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Abstract

In this paper, we consider environmental boundaries that can be represented by a time-varying closed curve. We use n robots equipped with location sensors to sample the dynamic boundary. The main difficulty during the prediction process is that only n boundary points can be observed at each time step. Our approach combines finite Fourier series for shape-estimation and polynomial fitting for point tracking in time. This combination gives a continuous parametric function that describes the boundary shape and its dynamics. We validate our strategy in simulation and with experiments using actual robots. We tested on non-convex boundaries assuming noisy measurements and inaccurate motion actuators.

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Notes

  1. The dataset: https://github.com/dsaldana/boundary-dataset

  2. The video is available at https://youtu.be/Zwc6vNuUFDw

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Authors and Affiliations

  1. Intelligent and Autonomous Robotics Laboratory (AIRLab), Lehigh University, Bethlehem, PA, USA

    David Saldaña

  2. Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Renato Assunção & Mario F. M. Campos

  3. GRASP Laboratory, University of Pennsylvania, Philadelphia, PA, USA

    M. Ani Hsieh & Vijay Kumar

Authors
  1. David Saldaña
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  2. Renato Assunção
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  3. M. Ani Hsieh
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  4. Mario F. M. Campos
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  5. Vijay Kumar
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Correspondence to David Saldaña.

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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Multi-Robot and Multi-Agent Systems.

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Saldaña, D., Assunção, R., Hsieh, M.A. et al. Estimating boundary dynamics using robotic sensor networks with pointwise measurements. Auton Robot 45, 193–208 (2021). https://doi.org/10.1007/s10514-020-09954-5

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  • DOI: https://doi.org/10.1007/s10514-020-09954-5

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