Journal of Intelligent and Robotic Systems

, Volume 46, Issue 3, pp 221–243 | Cite as

A Neural-based Model for Fast Continuous and Global Robot Location

  • Álvaro Sánchez Miralles
  • Miguel Ángel Sanz Bobi


One of the problems in the field of mobile robotics is the estimation of the robot position in an environment. This paper proposes a model for estimating a confidence interval of the robot position in order to compare it with the estimation made by a dead-reckoning system. Both estimations are fused using heuristic rules. The positioning model is very valuable in estimating the current robot position with or without knowledge about the previous positions. Furthermore, it is possible to define the degree of knowledge of the robot previous position, making it possible to adapt the estimation by varying this knowledge degree. This model is based on a one-pass neural network which adapts itself in real time and learns about the relationship between the measurements from sensors and the robot position.

Key words

first location problem location mobile robot neural network 


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Copyright information

© Springer Science + Business Media B.V. 2006

Authors and Affiliations

  • Álvaro Sánchez Miralles
    • 1
  • Miguel Ángel Sanz Bobi
    • 1
  1. 1.Escuela Técnica Superior de Ingeniería- ICAI, Instituto de Investigación TecnológicaUniversidad Pontificia ComillasMadridSpain

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