除冰机器人的多传感器融合障碍测距方法研究  

Research on multi-sensor fusion for obstacle distance measurement of deicing robot

作　　者：向阳琴[1] 孙炜[1] 王聪[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《计算机工程与应用》2010年第30期205-207,211,共4页Computer Engineering and Applications

基　　金：国家科技支撑计划重点项目(No.2008BAF36B01);教育部新世纪优秀人才计划项目(No.教技函[2007]70号)

摘　　要：障碍物测距是高压输电线路自主除冰机器人的关键技术之一。针对220kV输电线路除冰机器人的结构特点,提出了一种基于扩展卡尔曼滤波的障碍物距离信息融合检测方法。首先根据障碍物分布情况设计了除冰机器人多传感器检测系统的结构,建立了障碍物信息融合系统模型。然后根据障碍物信息状态模型的非线性特点,对传感器获取的异步测量数据进行同步处理,再应用改进的扩展卡尔曼滤波对多传感器信息进行滤波和融合,并与单个传感器的结果相比较,实验结果研究表明:该方法能有效地融合不同传感器的信息,具有更高的测距精度和更快的收敛速度。Obstacle distance measurement is one of the key techniques for the deicing robot on high voltage transmission line.According to the structure of 220 kV transmission line,an improved measurement method of obstacle distance data fusion based on EKF is put forward.At first this paper designs the structure of deicing Robot multi-sensor detect system according to the distribution of obstacles,builds the obstacle data fusion system.Then for the non-linear characteristics of the obstacle information state model,processing asynchronous sensor measurement data synchronization,then uses improved extended kalman filter for multi-sensor obstacle distance data filtering and fusion,the improved method is compared with fusion result of single sensor method,Results of experiments show that the improved method effectively applies to different sensor data fusion,also has higher distance measurement precision and quicker convergence rate.

关 键 词：除冰机器人 障碍物测距 信息融合 扩展卡尔曼滤波 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]