  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 robots 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 robots 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.
