基于多源信息融合的巡检机器人定位系统  

Localization system of inspection robots based on multi⁃source information fusion

作　　者：许永跃 苏金辉 陈鹏河 戴理想 陈夕松[1] 苏金亚 XU Yongyue;SU Jinhui;CHEN Penghe;DAI Lixiang;CHEN Xisong;SU Jinya(School of Automation,Southeast University,Nanjing 210096,China;Fujian Longking Intelligent Conveying Engineering Co Ltd,Xiamen 361015,China)

机构地区：[1]东南大学自动化学院,江苏南京210096 [2]福建龙净环保智能输送工程有限公司,福建厦门361015

出　　处：《传感器与微系统》2024年第11期58-62,共5页Transducer and Microsystem Technologies

基　　金：福建省区域发展项目(2022H4027)。

摘　　要：巡检机器人的高精度位置信息对于机器人巡检管理至关重要,在GPS拒止环境下,惯性测量单元(IMU)和超宽带(UWB)定位是常用的组合定位策略。然而,UWB在复杂环境中易受到非视距影响等产生异常值,严重影响经典非线性卡尔曼滤波的性能。本文基于最大相关性准则(MCC),改进扩展卡尔曼滤波(EKF)及无迹卡尔曼滤波(UKF)提高定位算法面对UWB测量异常值时的鲁棒性和精度。最后,搭建巡检机器人的定位系统验证平台。实验结果表明:所提出的算法能够提高IMU⁃UWB融合定位的精度以及鲁棒性,改进后的最大相关熵EKF(MCEKF)和MCUKF精度分别提升2.9%和5.6%。High⁃precision positional information is crucial for inspection robot management.In GPS⁃denied environments,the combination of inertial measurement unit(IMU)and ultra⁃wideband(UWB)are commonly used localization strategy.However,UWB is susceptible to outliers in complex environments due to non⁃line⁃of⁃sight(NLOS)effects,which significantly affect degrade the performance of classical nonlinear Kalman filtering.Extended Kalman filtering(EKF)and unscented Kalman filtering(UKF)based on the maximum correntropy criterion(MCC)are improved.Enhancing the robustness and accuracy of the localization algorithm against UWB outliers.Finally,a verification platform of positioning system for the robot is set up.Experimental results demonstrate that the proposed algorithms can improve the precision and robustness of IMU⁃UWB fusion localization,the precision of the improved maximum correlation entropy EKF(MCEKF)and MCUKF are increased by 2.9%and 5.6%,respectively.

关 键 词：传感器融合 卡尔曼滤波 最大相关准则 巡检机器人 超宽带定位 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]