Abstract:

In order to implement high-accuracy self-localization,kidnapping and tracking of soccer robots during the matches among medium-sized teams,a self-localization method based on the improved genetic algorithm is proposed. In this method,first,a mathematical model of genetic algorithm is established,in which the minimum sum of the white line points in the image and the corresponding points in the model map is used to evaluate the target function. Then,based on the global self-localization of the genetic algorithm,the gradient optimum algorithm is used to partially modify the major pose for the purpose of improving the self-localization precision and the algorithm robustness. Finally,with regard to the kidnapping and tracking of the robot,the author points out that the error of the distance between the observation points and the actual points should accord with the Gaussian distribution for the purpose of updating the population status and realizing the tracking of robot,and that,when the individual adaption degree of population sharply declines,the dynamic self-adaptive tuning of mutation probability helps to reduce the population deficiency effect and realize the recovered self-localization of kidnapping. Simulated and experimental
results indicate that the proposed self-localization method is superior to those based on the traditional genetic
algorithm and on the Monte Carlo algorithm,with its average self-localization tracking error being ( 0. 046m,0. 22°) .

Key words: robot, self-localization, machine vision, kidnapping, genetic algorithm