一种基于遗传算法的移动机器人自定位方法

A Genetic Algorithm Based Autonomous Localization Strategy for Mobile Robots

针对移动机器人定位研究中的位姿跟踪、全局定位和"绑架"三类问题,提出一种基于遗传算法的移动机器人自定位方法.设计基于位置相似度的种群适应度计算方法,利用实值编码方式实现种群的交叉、变异,有效提高算法的实时性.针对机器人定位过程中的"绑架"现象,在常规遗传算法的基础上引入种群发散算子,减小种群匮乏效应.在此基础上,利用机器人运动模型更新种群状态实现机器人的连续定位.在实际室内环境进行机器人定位实验,证实本文算法的有效性.

Aiming at the three main problems in localization of mobile robots, position tracking, global localization and kidnapped problem, an autonomous localization strategy based on genetic algorithm is proposed. A fitness function is designed based on the similarity of position. The real-coded method is used in the crossover and the mutation steps to improve the real-time ability of the algorithm. For the kidnapped problem, a scattering mechanism is introduced into the regular genetic algorithm. Thus, the population impoverishment problem is largely alleviated. Subsequently, the population state is updated with the kinematic model to achieve continuous localization of mobile robots. The experimental results of indoor environment demonstrate the validity of the proposed localization strategy.