轮式移动机器人里程计系统误差校核*

里程计使用编码器为轮式移动机器人提供基本的位姿估计,在运行过程中里程计存在严重的误差累计,通过校核系统参数可以减小系统误差,UMBmark方法是轮式移动机器人广泛使用的系统误差校核方法。针对UMBmark方法存在的不足,提出一种改进的系统误差校核新方法：综合考虑三种主要系统误差来源产生的误差对移动机器人直线运动和定点旋转运动造成的影响,同时采用正方形回路终点的方向误差代替传统UMBmark方法中的位置误差来校核系统参数。实验结果表明提出的方法能够有效校核系统参数,提高移动机器人的定位精度。     

基于单UWB融合里程计的多机器人相对定位方法

针对卫星信号受阻,无预设基础设施(定位基站、地标等)环境下多机器人间的相对定位问题,提出了一种基于单个超宽带(ultra-wideband, UWB)融合里程计的多机器人相对定位方法。该方法利用滑动窗口截取历史时刻的多组机器人间测距信息与里程计预测的机器人位姿,构建非线性最小二乘问题,实现机器人间的相对位姿估计;利用扩展卡尔曼滤波算法估计里程计协方差,并将其以加权的方式运用于非线性优化,抑制滑动窗口内里程计累积误差对定位结果的影响;最后,利用图优化算法融合里程计与非线性优化获得的相对位姿作进一步优化,抑制UWB测量误差影响,以获得稳定的相对定位结果。实验结果表明,在6 m×12 m的真实测试环境中,所提方法能够获得0.32 m的相对位置精度和4.16°的相对角度精度,相比于现有多机器人相对定位方案,该方法具有高精度、低成本、部署简单以及定位稳定的优点。     

Sensor fusion-based exploration in home environments using information,driving and localization gains

Exploration is one of the most important functions for a mobile service robot because a map is required to carry out various tasks. A suitable strategy is needed to efficiently explore an environment and to build an accurate map. This study proposed the use of several gains (information, driving, localization) that, if considered during exploration, can simultaneously improve the efficiency of the exploration process and quality of the resulting map. Considering the information and driving gains reduces behavior that leads a robot to explore a previously visited place, and thus the exploration distance is reduced. In addition, the robot can select a favorable path for localization by considering the localization gain during exploration, and the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, which verified that a robot can build an accurate map fully autonomously and efficiently in various home environments using the proposed method.     

Mobile robot localization based on effective combination of vision and range sensors

Most localization algorithms are either range-based or vision-based, but the use of only one type of sensor cannot often ensure successful localization. This paper proposes a particle filter-based localization method that combines the range information obtained from a low-cost IR scanner with the SIFT-based visual information obtained from a monocular camera to robustly estimate the robot pose. The rough estimation of the robot pose by the range sensor can be compensated by the visual information given by the camera and the slow visual object recognition can be overcome by the frequent updates of the range information. Although the bandwidths of the two sensors are different, they can be synchronized by using the encoder information of the mobile robot. Therefore, all data from both sensors are used to estimate the robot pose without time delay and the samples used for estimating the robot pose converge faster than those from either range-based or vision-based localization. This paper also suggests a method for evaluating the state of localization based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can reliably estimate the robot pose in various indoor environments and can recover the robot pose upon incorrect localization. Recommended by Editorial Board member Sooyong Lee under the direction of Editor Hyun Seok Yang. This research was conducted by the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Yong-Ju Lee received the B.S. degree in Mechanical Engineering from Korea University in 2004. He is now a Student for Ph.D. of Mechanical Engineering from Korea University. His research interests include mobile robotics. Byung-Doo Yim received the B.S. degree in Control and Instrumentation Engineering from Seoul National University of Technology in 2005. Also, he received the M.S. degree in Mechatroncis Engineering from Korea University in 2007. His research interests include mobile robotics. Jae-Bok Song received the B.S. and M.S. degrees in Mechanical Engineering from Seoul National University in 1983 and 1985, respectively. Also, he received the Ph.D. degree in Mechanical Engineering from MIT in 1992. He is currently a Professor of Mechanical Engineering, Korea University, where he is also the Director of the Intelligent Robotics Laboratory from 1993. His current research interests lie mainly in mobile robotics, safe robot arms, and design/control of intelligent robotic systems.