摘要
针对常规蒙特卡洛定位法中的粒子贫化和绑架问题,提出了一种分域自适应蒙特卡洛定位方法.该方法首先定义了两个用于描述粒子集合分布及其与真实位姿之间的差异的特征变量.然后根据特征变量的组合值将定位过程识别为全局定位、局部定位、局部跟踪和容错定位四种状态的交替过程,并为每种状态设计了自适应的控制策略来调整参数和重新采样规则.基于大型人形足球比赛机器人系统的物理和仿真实验的结果均表明,该定位方法有利于提高定位的准确性和实时性.同时,该方法还可以高效地解决绑架问题,提高了系统的鲁棒性.
A subsectional adaptive Monte Carlo localization method is presented to overcome some shortcomings in regu-lar Monte Carlo localization, such as particle degeneracy and the kidnap problem. Firstly, two feature variables are proposed to describe distribution of particle set and its difference from the real posture. Secondly, four states （global localization, local localization, local tracking and fault-tolerant localization） are identified by the combination of the vaxiable values during the whole process of localization, and different strategies are designed for each state in order to adjust parameters and resampling rules adaptively. Finally, the results of physical and simulative experiments based on adult-size humanoid soccer robot system show that the proposed method is effective in achieving an accurate and real-time localization. Furthermore, this method can enhance the robustness of localization system by solving the kidnap problem efficiently.
出处
《机器人》
EI
CSCD
北大核心
2012年第6期652-659,744,共9页
Robot
基金
吉林大学"985工程"工程仿生科技创新平台项目
吉林大学基本科研业务费资助项目(200903312)
