基于改进遗传算法的足球机器人自定位方法

Self-Localization of Soccer Robot Based on Improved Genetic Algorithm

为解决中型组比赛环境下足球机器人的自定位、绑架和跟踪问题,提出一种基于改进遗传算法的机器人自定位方法.首先建立根据图像上白线点与模型地图对应点距离之和最小来评定目标函数的遗传算法数学模型;然后在遗传算法的全局自定位基础上,利用梯度优化算法局部修正主位姿,以提高自定位的精度和算法的鲁棒性;最后针对绑架和跟踪,提出机器人运动时观测点与真实点的距离误差应符合高斯分布,并以此来更新种群状态,实现机器人的跟踪;同时,在种群的个体适应度急剧下跌时,动态自适应调整变异概率,可以减少种群匮乏效应,实现绑架恢复自定位.仿真和实验结果表明,文中方法较基于传统遗传算法和蒙特卡罗算法的自定位方法具有更好的性能,所得自定位平均跟踪误差为(0.046m,0.22°).

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°）.