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Uncertainty Quantification for Small Robots Using Principal Orthogonal Decomposition

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2016 International Symposium on Experimental Robotics (ISER 2016)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 1))

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Abstract

The paper reports on a new data-driven methodology for uncertainty quantification in centimeter-scale robots. We employ tools from functional expansion-based methods, the Karhunen-Loeve (KL) decomposition in particular, to identify appropriate reduced-order models of robotic systems through empirical observations, and discover underlying dominant dynamical behaviors of a system in the presence of uncertainty. The approach is applied to a quadrotor aerial vehicle tasked to hover at various heights from the ground. Several experimental data sets are collected to extract dominant modes. First-order modes correctly capture expected behaviors of the system, while higher-order modes quantify the degree of uncertainty at different hovering conditions. The information provided by this model can be used to develop robust controllers in the face of aerodynamic disturbances and unmodeled nonlinearities.

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Notes

  1. 1.

    The system is modeled using a second-order transfer function, and is controller through a PID controller; for more details see [5].

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Acknowledgments

This work is supported in part by NSF under grant CMMI-1462825.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California, 900 University Ave, 357 Bourns Hall B, Riverside, California, 92521, USA

    Konstantinos Karydis

  2. Department of Mechanical Engineering and Mechanics, Drexel University, 3141 Chestnut Street, Randell 115, Philadelphia, Pennsylvania, 19104, USA

    M. Ani Hsieh

Authors
  1. Konstantinos Karydis
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  2. M. Ani Hsieh
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Corresponding author

Correspondence to Konstantinos Karydis .

Editor information

Editors and Affiliations

  1. Gra Sch of Info Sci&Tech,Dept of MechInf, The University of Tokyo Gra Sch of Info Sci&Tech,Dept of MechInf, Tokyo, Japan

    Dana Kulić

  2. Computer Science Department, Stanford University Computer Science Department, Stanford, California, USA

    Yoshihiko Nakamura

  3. Department of Electrical & Computer Engg, University of Waterloo Department of Electrical & Computer Engg, Waterloo, Ontario, Canada

    Oussama Khatib

  4. Department of Mechanical Systems Enginee, Tokyo University of Agriculture and Tech Department of Mechanical Systems Enginee, Tokyo, Japan

    Gentiane Venture

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Karydis, K., Hsieh, M.A. (2017). Uncertainty Quantification for Small Robots Using Principal Orthogonal Decomposition. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-50115-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50114-7

  • Online ISBN: 978-3-319-50115-4

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