APPROXIMATE MOTION MODEL FOR MOBILE ROBOTS LOCALIZATION

Luis Fernando de Almeida, Henrique Renno de Azeredo Freitas, José Walter Parquet Bizarria

Abstract


In mobile robotics, most part of the techniques which aim the elaboration of an efficient algorithm to the problem of localization is based on probabilistic approaches. Recently, one have applied algorithms bond to data that are conditioned to uncertainty, like the Monte Carlo Localization (MCL) that is part of a family of probabilistic algorithms dependent on the quality of two models: sensors and motion model. This paper proposes an approach for the generation of the motion model based on a discrete model and from that one searches the adequacy of a function of linear nature in order to represent the motion model in an approximated way, with the purpose of detecting the robot motion’s stochastic behavior. The obtained results show that the proposed models work for the problem of localization and they can be alternatives of choice to the MCL algorithm.

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ISSN 2179-7625 (online)

DOI registration: 10.32426/engres.

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Logomarca da Lepidus Tecnologia