Detection Model in Collaborative Multi-Robot Monte Carlo Localization.

Barea, Rafael; López, Elena; Bergasa, Luis M.; Álvarez, Sergio; Ocaña, Manuel
Year: 2006
Type of Publication: In Proceedings
Keywords: Monte Carlo localization; assistant robots; collaborative mobile robot localization; detection model; mobile robots; probabilistic methods; Monte Carlo methods; mobile robots; multi-robot systems; position control; probability;
Book title: Distributed Intelligent Systems: Collective Intelligence and Its Applications, 2006. DIS 2006. IEEE Workshop on
Pages: 49 -54
Month: june
ISBN: 0-7695-2589-X
DOI: 10.1109/DIS.2006.24
This paper presents an algorithm for collaborative mobile robot localization based on probabilistic methods (Monte Carlo localization) used in assistant robots. When a root detects another in the same environment, a probabilistic method is used to synchronize each robot's belief. As a result, the robots localize themselves faster and maintain higher accuracy. The technique has been implemented and tested using a virtual environment capable to simulate several robots and using two real mobile robots equipped with cameras and laser range-finders for detecting other robots. The result obtained in simulation and with real robots show improvements in localization speed and accuracy when compared to conventional single-robot localization
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