Coevolution Based Adaptive Monte Carlo Localization

Coevolution Based Adaptive Monte Carlo Localization

by Luo Ronghua

Type Book
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An adaptive localization algorithm CEAMCL is proposed in this paper. Using an ecological competition model, CEAMCL can adaptively adjust the sample size according to the total environment resource, which represents uncertainty of the position of the robot. Coevolution between species ensures that the problem of premature convergence when using MCL in highly symmetric environments can be solved. And genetic operators used for intra-species evolution can search for optimal samples in each species…

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Title Statements

  • Full title :Coevolution Based Adaptive Monte Carlo Localization.

Summary

An adaptive localization algorithm CEAMCL is proposed in this paper. Using an ecological competition model, CEAMCL can adaptively adjust the sample size according to the total environment resource, which represents uncertainty of the position of the robot. Coevolution between species ensures that the problem of premature convergence when using MCL in highly symmetric environments can be solved. And genetic operators used for intra-species evolution can search for optimal samples in each species, so the samples can represent the desired posterior density better. Experiments prove that CEAMCL has the following advantages: (1) it can adaptively adjust the sample size during localization; (2) it can make stable localization in highly symmetric environment; (3) it can make precise localization with a small sample size.

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Edition Statements

  • Publication Date:2005.

Publication Statements

The Physical Object

  • Description:1 online resource

Edition Identifiers

  • ISBN:3866110383, 9783866110380
  • oclc:ocn884020484

Subjects

Open Access Collection

Writers

Luo Ronghua Hong Bingrong Li Maohai

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