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JANUARY 2018 - Volume: 93 - Pages: 19
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Several variants of the MCL (Monte Carlo Localization) algorithm have been used in the research for solving the problem of localization for mobile robots. Considering the work of [1], where several random samples are used, with respect to the Markov Location approximations two research approaches were derived. The first approach focuses on the adaptation of the algorithm for its implementation in hardware, particularly, with the aim of reducing the use of physical and computational resources, due to the type of application that refers [2]. In the second, complementary techniques are implemented to improve the performance of the algorithm. An example of this is the research of [3], where the incorporation of a multi-objective evolutionary approach is implemented, to re-sample the particles with weights and distribution of the population.
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