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Coevolution Based Adaptive Monte Carlo Localization (CEAMCL)

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Author(s): Luo Ronghua | Hong Bingrong

Journal: International Journal of Advanced Robotic Systems
ISSN 1729-8806

Volume: 1;
Issue: 3;
Start page: 183;
Date: 2008;
Original page

Keywords: Monte Carlo localization | coevolution | evolutionary computation | robot localization

ABSTRACT
An adaptive Monte Carlo localization algorithm based on coevolution mechanism of ecological species is proposed. Samples are clustered into species, each of which represents a hypothesis of the robot's pose. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence when using MCL in highly symmetric environments can be solved. And the sample size can be adjusted adaptively over time according to the uncertainty of the robot's pose by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small size of samples can represent the desired density well enough to make precise localization. The new algorithm is termed coevolution based adaptive Monte Carlo localization (CEAMCL). Experiments have been carried out to prove the efficiency of the new localization algorithm.
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