Summary:
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.
Keywords: first location problem; location; mobile robot; neural network
JCR Impact Factor and WoS quartile: 0,265 (2006); 3,100 - Q2 (2023)
DOI reference:
https://doi.org/10.1007/s10846-006-9046-4
Published on paper: July 2006.
Published on-line: August 2006.
Citation:
A. Sánchez, M.A. Sanz-Bobi, A neural-based model for fast continuous and global robot location. Journal of Intelligent & Robotic Systems. Vol. 46, nº. 3, pp. 221 - 243, July 2006. [Online: August 2006]
