机器人2026，Vol.48Issue(1)：25-37,13.DOI:10.13973/j.cnki.robot.240294
面向地下排水管网的轮式机器人自适应卡尔曼滤波定位算法
Adaptive Kalman Filter Localization Algorithm for Wheeled Robots for Underground Drainage Networks
摘要
Abstract
To improve the positioning accuracy of pipeline robots in underground pipeline networks with diameters ranging from 400 to 600 mm,data from low-cost wheel encoders,optical flow sensors,and six-axis inertial measurement units are integrated to achieve robot localization in the absence of global information,thereby estimating a more precise trajectory of the robot movement.Firstly,an outlier detection mechanism is introduced to address the common issue of wheel slippage in pipeline environments,and the data from wheel encoders are corrected by setting a threshold for outlier judgment.Secondly,the Taguchi method is employed to assist in adjusting the parameters of the process noise covariance matrix in the Kalman filtering process,achieving an efficiency 5 times that of the trial-and-error method.Additionally,an adaptive parameter adjustment mechanism is implemented by incorporating residuals,a forgetting factor,and parameter thresholds,to tackle the adjustment of the measurement noise covariance matrix.Finally,experiments are conducted on a physical platform.The results demonstrate that the improved algorithm effectively suppresses errors caused by longitudinal slippage,with a positioning accuracy 3.26 times that of the extended Kalman filter-based positioning algorithm.关键词
管道机器人/多传感器融合定位/自适应扩展卡尔曼滤波/滑动检测/田口算法/遗忘因子Key words
pipeline robot/multi-sensor fusion localization/adaptive extended Kalman filter/slide detection/Taguchi algorithm/forgetting factor引用本文复制引用
朱瑾玉,孙少欣,胡安森,苏晓杰..面向地下排水管网的轮式机器人自适应卡尔曼滤波定位算法[J].机器人,2026,48(1):25-37,13.基金项目
国家自然科学基金(U22A20101) （U22A20101）
中央高校基本科研业务费专项资金(2023CDJXY-010) （2023CDJXY-010）
重庆市自然科学基金(CSTB2024NSCQ-LZX0143) （CSTB2024NSCQ-LZX0143）
重庆市技术创新与应用发展专项(CSTB2022TIAD-CUX0015) （CSTB2022TIAD-CUX0015）
重庆英才计划(cstc2024ycjh-bgzxm0033). （cstc2024ycjh-bgzxm0033）
