Static calibration and dynamic compensation of the SCORBOT robot using sensor fusion and LSTM networks

IF 1 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY Journal of the Chinese Institute of Engineers Pub Date : 2023-10-16 DOI:10.1080/02533839.2023.2261984
Yong-Lin Kuo, Chia-Hang Hsieh
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Abstract

ABSTRACTThis paper presents both static calibration and dynamics compensation to reduce the positioning errors of the SCORBOT robot. First, a sensor fusion scheme is proposed to estimate the position and attitude of the end-effector of a robot instead of using laser trackers or coordinate measuring machines. The scheme integrates an extended Kalman filter (EKF) with the models of an inertial measurement unit (IMU) and a depth camera. Second, a static calibration scheme is presented to reduce the mechanism errors of robots. The scheme modifies the Denavit-Hartenberg (D-H) parameters provided by the manufacturer based on the least squares method. Third, a dynamic compensation scheme is proposed to reduce the errors caused by robot motions. The scheme establishes a long short-term memory (LSTM) network to compensate the joint angles, where the robot dynamics is integrated into the scheme. Finally, both simulations and experiments are performed to validate the proposed schemes.CO EDITOR-IN-CHIEF: Kuo, Cheng-ChienASSOCIATE EDITOR: Su, Shun-FengKEYWORDS: Static calibrationdynamic compensationsensor fusionLSTM network Nomenclature iAj=transformation matrix form coordinate systems i to jaidiαi=D-H parameters of the ith joint axisariami=actual and measured linear accelerations of the ith joint axisbaibωi=signal biases of linear accelerations and angular velocitiesbfbibcbo=biases of LSTM networkscDHcDH0=of D-H parameters and nominal D-H parameterscisi=cosine and sine functions of rotating angle of the ith joint axisE[]=expected valueFw=matrix and vector in the continuous-time state equationFDK=position vector of the end-effector by direct kinematicsG=gravitational force vectorHv=matrix and vector in the measurement equationJ=objective functionK=Kalman filter gainM=inertia matrixnainωi=signal noises of linear accelerations and angular velocitiesP=covariance matrix of the statesp=position vector of the end-effectorq=generalized coordinatesqi=rotating angle of the ith joint axis.T=generalized force vector.t=discrete timeu, v, w=vectors to describe the orientation of the end-effectorV=Centrifugal and Coriolis force vectorWfWiWcWo=weights of LSTM networksx=state vectorxtht=input and output of LSTM arrays(Xi,Yi,Zi)=ith coordinate systemz=measurementsΔcDH=variations of D-H parameter vectorΔt=sampling timeΦη=matrix and vector in the discrete-time state equationϕθψ=Euler anglesωriωmi=actual and measured angular velocities⋅2=2-normAcknowledgmentsThis work was supported in part by the Ministry of Science and Technology, Taiwan, under Grant MOST 109-2221-E-011-068.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Ministry of Science and Technology, Taiwan [MOST 109-2221-E-011-068].
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基于传感器融合和LSTM网络的SCORBOT机器人静态标定和动态补偿
摘要采用静态标定和动态补偿相结合的方法减小SCORBOT机器人的定位误差。首先,提出了一种传感器融合方案来估计机器人末端执行器的位置和姿态,而不是使用激光跟踪仪或坐标测量机。该方案将扩展卡尔曼滤波(EKF)与惯性测量单元(IMU)和深度相机模型相结合。其次,提出了一种减小机器人机构误差的静态标定方案。该方案基于最小二乘法对制造商提供的Denavit-Hartenberg (D-H)参数进行修改。第三,提出了一种动态补偿方案,以减小机器人运动引起的误差。该方案建立了一个长短期记忆(LSTM)网络来补偿关节角,并将机器人动力学特性融入到该方案中。最后,通过仿真和实验对所提方案进行了验证。联合主编:郭承谦副主编:苏顺丰静态校准动态补偿传感器fusionLSTM网络术语iAj=从坐标系i到jadi的变换矩阵αi=第i个关节轴的D-H参数αi=第i个关节轴的实际和测量的线加速度ω ωi=线加速度和角速度的信号偏差bfbibcbo= LSTM网络的偏差scdhcdh0 =D-H参数和标称的D-H参数ω =第i个关节轴旋转角度的余弦和正弦函数[]=期望值efw =矩阵和连续时间状态方程中的矢量fdk =末端执行器直接运动学的位置矢量sg =重力矢量hv =矩阵和测量方程中的矢量j =目标函数k =卡尔曼滤波增益m =惯性矩阵ωi=线加速度和角速度的信号噪声esp=状态协方差矩阵p=末端执行器的位置矢量q=广义坐标qi=关节第i轴的转角T=广义力向量。T =离散时间,v,台湾,授予MOST 109-2221-E-011-068。披露声明作者未报告潜在的利益冲突。本研究得到了台湾科技部的支持[MOST 109-2221-E-011-068]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of the Chinese Institute of Engineers
Journal of the Chinese Institute of Engineers 工程技术-工程:综合
CiteScore
2.30
自引率
9.10%
发文量
57
审稿时长
6.8 months
期刊介绍: Encompassing a wide range of engineering disciplines and industrial applications, JCIE includes the following topics: 1.Chemical engineering 2.Civil engineering 3.Computer engineering 4.Electrical engineering 5.Electronics 6.Mechanical engineering and fields related to the above.
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