在机器人打磨中，机器人末端六维力/力矩传感器受打磨轮接触振动等影响，力测量信号淹没于噪声中，对该信号实时有效地提取是控制力的关键。从力测量信号快速傅里叶变换后的特征得知，其噪声是由高斯白噪声、振动噪声及有色噪声叠加所构成的复杂噪声，由此提出了基于串联结构的双卡尔曼滤波（double Kalman filter，DKF）算法。包括：1）第一个滤波采用卡尔曼滤波（Kalman filter，KF）算法，主要滤除高斯白噪声和高频振动谱峰群；2）针对未能滤除的高斯白噪声和低频振动噪声叠加形成有色噪声的特点，进行了分析，引入单个参数，以指数加权方式设计了一个逐渐时变噪声方差，描述有色噪声特性，以此改进KF作为第二个滤波算法；3）两个滤波器以串联方式构成双卡尔曼滤波。以眼镜框与打磨轮接触打磨过程为例，实验结果表明，DKF算法比KF算法更加有效地滤除力测量信号噪声，并具有计算复杂度低、实用性强特点。
Abstract
The consequence that the robot end’s six-dimensional force/torque sensor affected by the contact vibration of the grinding wheel causes the force measurement signal to be submerged by noise in robot grinding, so that real-time and effective extraction of the signal is the key to control force. From the characteristics of the force measurement signal after fast Fourier transform, its noise is a complex noise formed by the superposition of Gaussian white noise, vibration noise and colored noise. Double Kalman Filter(DKF) algorithm based on series structure was proposed. Including: 1)The Gaussian white noise and high-frequency vibration peak groups were filtered by the first Kalman Filter(KF) algorithm; 2) To analyze the characteristics of colored noise formed by the superimposition of unfiltered Gaussian white noise and low-frequency vibration noise, a single parameter was introduced to design the time-varying variance to describe them by exponential weighting, so that KF was improved as the second filtering algorithm;3) Two filters formed double Kalman filter in series. Taking the contact grinding process between eyeglass frame and grinding wheel as an example, the experimental results showed that the proposed algorithm is more effective than traditional KF algorithm in suppressing noise in force measurement signal, and has low computational complexity and strong practicability.
关键词
打磨 /
振动噪声 /
有色噪声 /
双卡尔曼滤波
{{custom_keyword}} /
Key words
grinding /
vibration noise /
colored noise /
double Kalman filter
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] KIM U，LEE D，KIM Y B，et al. A novel 6-axis force/torque sensor for robotic applications [J]. IEEE/ASME Transactions on Mechatronics, 2017,22(3):1381-1391.
[2] XIONG L，GUO Y X，JIANG G Z，et al. Six-dimensional force/torque sensor based on fiber bragg gratings with low coupling [J]. IEEE Transactions on Industrial Electronics,2021, 68(5):4079-4089.
[3] 陶 波，赵兴炜，李汝鹏，等. 机器人测量-操作-加工一体化技术研究及其应用[J]. 中国机械工程，2020，31(1)：49-56.
TAO Bo, ZHAO Xing-wei, LI Ru-peng, et al. Research on robotic measurement-operation-machining technology and its application [J]. China Mechanical Engineering, 2020, 31(1): 49-56.
[4] JIN X B，ROBERTJEREMIAH R，SU T L，et al. The new trend of state estimation: from model-driven to hybrid-driven methods [J]. Sensors,2021,21(6):2085.
[5] RABOUDI N F, AIT-EL-FQUIH B，OMBAO H，et al. Ensemble Kalman filtering with coloured observation noise [J]. Quarterly Journal of the Royal Meteorological Society, 2021,147(741): 4408-4424.
[6] 史鹏程，彭珍瑞，董康立. 基于有限测量信息的两步响应重构方法[J]. 振动与冲击，2022，41(11)：291-297.
SHI Peng-Cheng, PENG Zhen-Rui, DONG Kang-Li. Two-step response reconstruction method based on limited measured information [J]. Journal of Vibration and Shock, 2022, 41(11): 291-297.
[7] 牛强军，张 潮，苏登辉. SINS/GPS组合导航卡尔曼滤波算法研究[J]. 兵工自动化，2015，34(5)：38-41.
NIU Qiang-Jun, ZHANG Chao, SU Deng-Hui. Research on SINS/GPS integrated navigation Kalman filter algorithm [J]. Ordnance Industry Automation, 2015, 34(5): 38-41.
[8] 林 旭，刘俊钊. 有色噪声下的目标跟踪卡尔曼滤波新算法[J]. 中国惯性技术学报，2018，26(6)：830-834.
LIN Xu, LIU Jun-Zhao. New Kalman filtering algorithm for target tracking with colored noise [J]. Journal of Chinese Inertial Technology, 2018, 26(6): 830-834.
[9] 靳凯迪，柴洪洲，宿楚涵，等. 顾及有色噪声的光纤陀螺随机噪声自适应滤波方法[J]. 测绘学报，2022，51(1)：80-86.
JIN Kai-Di, CHAI Hong-Zhou, SU Chu-Han, et al. Adaptive Kalman filter method with colored noise for fiber optic gyroscope random drift [J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(1): 80-86.
[10] ZHOU ZB，WU J，LI Y，et al. Critical issues on Kalman filter with colored and correlated system noises [J]. Asian Journal of Control,2018,20(1):1-15.
[11] XU G，HUANG Y L，GAO Z X，et al. A computationally efficient variational adaptive Kalman filter for transfer alignment [J]. IEEE Sensors Journal,2020,20(22):13682-13693.
[12] CHEN B D，DANG L J，GU Y T，et al. Minimum error entropy Kalman filter [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2021,51(9):5819-5829.
[13] YAN Y J，WU G X，DONG Y，et al. An improved MSR-based data-driven detection method using smoothing pre-processing [J]. IEEE Signal Processing Letters,2021,28:444-448. [14] 陈林秀，郝明瑞，赵佳佳. 混合噪声条件下的目标被动定位算法[J]. 兵工学报，2021，42(9)：1923-1930.
CHEN Lin-xiu, HAO Ming-rui, ZHAO Jia-jia. Passive target positioning algorithm for mixed noise[J]. Acta Armamentarii, 2021, 42(9): 1923-1930.
[15] GAO Y T，JIANG Y，GAO Y，et al. A linear Kalman filter-based integrity monitoring considering colored measurement noise [J]. GPS Solutions,2021,25(2):1-13.
[16] KERMARREC G，JAIN A，SCHON S. Kalman filter and correlated measurement noise: the variance inflation factor [J]. IEEE Transactions on Aerospace and Electronic Systems, 2022,58(2):766-780.
[17] 高社生，洪根元，高广乐，等. 基于马氏距离的联邦卡尔曼滤波在SINS/SRS/CNS导航中的应用[J]. 中国惯性技术学报，2021，29(2)：141-146.
GAO She-sheng, HONG Gen-yuan, GAO Guang-le, et al. Mahalanobis distance based federated Kalman filter for SINS/
SRS/CNS integrated navigation system [J]. Journal of Chinese Inertial Technology, 2021, 29(2): 141-146.
[18] ZHAO S Y，SHMALIY Y S，SHI P，et al. Fusion Kalman/UFIR filter for state estimation with uncertain parameters and noise statistics [J]. IEEE Transactions on Industrial Electronics, 2017,64(4):3075-3083.
[19] 肖 磊，王绍举，常 琳，等. 采用自适应无迹卡尔曼滤波的卫星姿态确定[J]. 光学精密工程，2021，29(3)：637-645.
XIAO Lei, WANG Shao-ju, CHANG Lin, et al. Attitude determination for satellite using adaptive unscented Kalman filter [J]. Optics and Precision Engineering, 2021, 29(3): 637-645.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1650 KB)
