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Some Applications in Robotics

  • Jean-François Mari
  • René Schott
Chapter

Abstract

In this section, we describe a method based on hidden Markov models for learning and recognizing places in an indoor environment by a mobile robot. Hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (e.g. neural networks, ... ) are their capabilities to modelize noisy temporal signals of variable length.

Keywords

Hide Markov Model Mobile Robot Optimal Policy Markov Decision Process Reward Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [Laroche et al., 1999]
    Laroche, P., Charpillet, J., and Schott, R. (1999). Mobile Robotics Planning Using Abstract Markov Decision Processes. pages 299–306. ICTAI’99, IEEE.Google Scholar
  2. [Aycard et al., 1997a]
    Aycard, O., Charpillet, F., Fohr, D., and Mari, J.-F. (1997a). Place Learning and Recognition Using Hidden Markov Models. In Proceedings IEEE-RSJ on International Conference on Intelligent Robots and Systems, pages 1741 — 1746, Grenoble, France.Google Scholar
  3. [Aycard et al., 1998]
    Aycard, 0., Mari, J.-F., and Charpillet, F. (1998). Second Order Hidden Markov Models for Places Recognition: New Results. In proceedings of IEEE International Conference on Tools with Artificial Intelligence.Google Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Jean-François Mari
    • 1
  • René Schott
    • 2
  1. 1.LORIA and Université Nancy 2France
  2. 2.Université Henri Poincaré-Nancy 1France

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