Automatic training method applied to a wifi+ultrasound pomdp navigation system

  • Authors:

  • M. Ocaña;L. m. Bergasa;M. a. Sotelo;R. Flores;D. f. Llorca;D. Schleicher

  • Affiliations:

  • Department of electronics, escuela politécnica superior, university of alcalá, campus universitario s/n, 28871 alcalá de henares, madrid, spain.;Department of electronics, escuela politécnica superior, university of alcalá, campus universitario s/n, 28871 alcalá de henares, madrid, spain.;Department of electronics, escuela politécnica superior, university of alcalá, campus universitario s/n, 28871 alcalá de henares, madrid, spain.;Department of electronics, escuela politécnica superior, university of alcalá, campus universitario s/n, 28871 alcalá de henares, madrid, spain.;Department of electronics, escuela politécnica superior, university of alcalá, campus universitario s/n, 28871 alcalá de henares, madrid, spain.;Department of electronics, escuela politécnica superior, university of alcalá, campus universitario s/n, 28871 alcalá de henares, madrid, spain.

  • Venue:
  • Robotica
  • Year:
  • 2009

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Abstract

This paper presents an automatic training method based on the Baum–Welch algorithm (also known as EM algorithm) and a robust low-level controller. The method has been applied to the indoor autonomous navigation of a surveillance robot that utilizes a WiFi+Ultrasound Partially Observable Markov Decision Process (POMDP). This method uses a robust local navigation system to automatically provide some WiFi+Ultrasound maps. These maps could be employed within probabilistic global robot localization systems. These systems use a priori probabilistic map in order to estimate the global robot position. The method has been tested in a real environment using two commercial Pioneer 2AT robotic platforms in the premises of the Department of Electronics at the University of Alcalá. Some experimental results and conclusions are presented.