Moving Horizons Estimation for Wheelchair Trajectory Repeatability in the Home

  • Steven B. SkaarEmail author
Part of the STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health book series (STEAM)


An advantage of Moving Horizons Estimation in contrast with the previously tested Extended Kalman Filter is presented in the context of achieving a useful form of teach/repeat for wheelchairs of severely disabled veterans within their homes. The ability to combine numerical integrals of the measured wheel rotations of the chair with chair-mounted cameras and camera-based observations of wall-mounted fiducials within a trailing, selected “window” based on the “taught” trajectory, in order to precisely and reliably repeat that trajectory, is presented. The importance and real-time feasibility of the observation-batch-selection protocol—which is applied both to the teaching data and, in real time, to the tracking data—is discussed. Experimental illustration uses the trajectory shown in



This research was supported in part by the US Department of Veterans Affairs, and in part by the Naval Center for Applied Research in Artificial Intelligence, US Office of Naval Research.


  1. 1.
    L. Fehr, E. Langbein, S. Skaar, Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey. J. Rehabil. Res. Dev. 37(3), 253–260 (2000)Google Scholar
  2. 2.
    E. Baumgartner, S. Skaar, An autonomous vision-based mobile robot. IEEE Trans. Automat. Contr. 39(3), 493–502 (1994)CrossRefGoogle Scholar
  3. 3.
    M. Perrollaz, S. Khorbotly, A. Cool, J. Yoder, E. Baumgartner, Teachless teach-repeat: toward vision-based programming of industrial robots, in Proceedings of IEEE International Conference on Robotics and Automation, St Paul, MN, USA (May 2012)Google Scholar
  4. 4.
    T. Whitworth, Fixturing for automated welding in automotive—repeatability, access, and protection are key. MetalForming 48(2) (2014)Google Scholar
  5. 5.
    H. Goldstein, Classical Mechanics, 3rd edn. (Addison Wesley, 1980), p. 16Google Scholar
  6. 6.
    A. Gelb (ed.), Applied Optimal Estimation (MIT Press), 1974Google Scholar
  7. 7.
    E. Haseltine, J. Rawlings, Critical evaluation of extended kalman filtering and moving-horizon estimation. Ind. Eng. Chem. Res. 44(8), 2451–2460CrossRefGoogle Scholar
  8. 8.
    J.L. Junkins, C. White, J. Turner, Star pattern recognition for real-time attitude determination. J. Astronaut. Sci. 25, 251–270 (1977)Google Scholar
  9. 9.
    D. Belsley, E. Kuh, R. Welsch, The Condition Number, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity (John Wiley & Sons, New York), pp. 100–104Google Scholar
  10. 10.
    C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, R. Neira, J. Leonard, Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1336CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Aerospace and Mechanical EngineeringUniversity of Notre DameNotre DameUSA

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