一种基于动态残差的自适应鲁棒无迹卡尔曼滤波器定位算法  

An Adaptive Robust Unscented Kalman Filter Localization Algorithm Based on Dynamic Residual

作　　者：许万[1] 程兆 夏瑞东 陈汉成 XU Wan;CHENG Zhao;XIA Ruidong;CHEN Hancheng(School of Mechanical Engineering,Hubei University of Technology,Wuhan,430072;Shenzhen Overseas Decoration Engineering Co.,Ltd.,Shenzhen,Guangdong,518031)

机构地区：[1]湖北工业大学机械工程学院,武汉430072 [2]深圳海外装饰工程有限公司,深圳518031

出　　处：《中国机械工程》2023年第21期2607-2614,共8页China Mechanical Engineering

基　　金：湖北省重点研发专项(2023BEB031);住建部科技项目(2022GKG078)。

摘　　要：针对标准无迹卡尔曼滤波(UKF)定位算法无法满足移动机器人在不平整地面运动时高精度定位要求的问题,结合抗差估计理论,提出了一种自适应鲁棒无迹卡尔曼滤波器(ARUKF)定位算法。ARUKF根据动态残差对UKF的预测值进行抗差自适应调整,减小了外部干扰对系统预测值的影响,提高了系统的精度与鲁棒性,通过减少采样过程的运算量加快了运算,并提高了系统实时性。仿真和现场测试结果表明,相较于UKF算法和基于Sage-Husa的改进UKF算法,ARUKF算法对不平整地面产生的扰动能更快收敛,具有更加优异的精度、鲁棒性和实时性,平均距离误差小于2 mm,平均角度误差小于0.016 rad,可以满足更苛刻的建筑施工现场放线要求。Aiming at the problems that the standard unscented Kalman filter(UKF)localization algorithm could not meet the high-precision localization requirements of mobile robots when moved on uneven ground,an ARUKF localization algorithm was proposed based on robust estimation theory.The ARUKF adaptively adjusted the predicted value of UKF according to the dynamic residual,reduced the influences of external interference on the predicted values of the systems,improved the accuracy and robustness of the system,speeded up the operation by reducing the computational complexity of the sampling processes,and improved the real-time performance of the system.The simulation and field test results show that the ARUKF algorithm may converge faster for the disturbance generated by uneven ground,and have better accuracy,robustness,and real-time performance,compared with the UKF algorithm and the improved UKF algorithm based on Sage-Husa.The average distance error is less than 2 mm,and the average angle error is less than 0.016 rad,which may meet more stringent requirements of the construction site.

关 键 词：精准定位 抗差估计 动态残差 自适应鲁棒无迹卡尔曼滤波器 移动机器人 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置] TP242.6[自动化与计算机技术—控制科学与工程]