一种有效的移动机器人里程计误差建模方法

移动机器人里程计误差建模是研究移动机器人定位问题的基础. 现有的移动机器人里程计误差建模方法多数针对某一种驱动类型移动机器人设计, 运动过程中缺乏对里程计累计误差的实时反馈补偿, 经过长距离运动过程定位精度大幅度降低. 因此本文针对同步驱动和差动驱动轮式移动机器人平台提出了一种通用的里程计误差建模方法. 在假设机器人运动路径近似弧线基础上, 依据里程计误差传播规律推导了非系统误差、系统误差与里程计过程输入之间的近似函数关系, 进而提出一种具有闭环误差实时反馈补偿功能的移动机器人定位算法, 对定位过程中产生的里程计累计误差给予实时反馈补偿. 实验表明新算法有效地减少了里程计累计误差, 提高了定位精度.

An Efficient Approach to Odometric Error Modeling for Mobile Robots

Odometric error modeling for mobile robot is a basis of localization. Most of the approaches to odometric error modeling are designed for some special driving-type robot up to now. And the unbounded odometric long term error, which degrades localization precision after long-distance movement, is not often able to be compensated in real-time. Therefore, a general approach to odometric error modeling for mobile robot is proposed with respect to both synchronous-drive roller robot and differential-drive roller robot. The method assumes that the robot path to be approximately to circular arcs. The approximate functions relationships between the process input of odometry and non-systematic errors as well as systematic errors are derived based on the odometric error propagation law. Further, a new localization algorithm for mobile robot is proposed, which is used to online compensate the accumulative errors of odometry in the process of localization. The experiments show that the new localization algorithm reduces the accumulative errors of odometry efficiently, and improves the localization precision remarkably.