基于光学运动跟踪系统的机器人末端位姿测量与误差补偿
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摘要
针对工业机器人绝对定位精度较低的问题,采用加拿大NDI公司的Optotrak Certus HD光学运动跟踪系统作为机器人位姿的测量设备,提出了一种基于再生权最小二乘法的最优剪枝极限学习机算法,通过该算法将机器人目标位姿映射到修正位姿上,实现了对机器人末端位姿补偿的效果.利用爱普生6轴机器人末端进行实验,在不同速度下完成直线轨迹运动、圆轨迹运动以及离散随机运动,对该误差补偿方法的有效性进行验证和分析.结果表明,该误差补偿方法均能提高机器人的位姿精度,其测试点在X、Y、Z三轴总方向上的绝对位置精度为0.06 mm～0.25 mm,比无补偿时的2 mm～3 mm有了1个数量级的提高;而姿态误差补偿后,其均方根误差和平均绝对误差均减小到未补偿时姿态误差的26.09%.同时,该补偿方法还可有效降低异常值的影响,具有良好的稳健性.
For the problem of low absolute positioning accuracy in industrial robots, the Optotrak Certus HD optical motion tracking system made by NDI company in Canada is utilized as the measurement device for the robot pose, and an optimallypruned extreme learning machine(OPELM) algorithm based on the self-born weighted least squares(SBWLS) method is proposed. The algorithm realizes the pose compensation effect of the robot end-effector by mapping the robot target pose to the revised pose. In order to verify and analyze the validity of the error compensation method, the end-effector of the Epson's6-axis robot is used to perform different motion modes, i.e., linear motion, circular motion, and discrete random motion, at a series of speeds in the experiment. The results show that the proposed error compensation method improves the pose accuracy of robot end-effector, and the total absolute positioning accuracy of the test point in X, Y, and Z axes is raised from 2 mm ～3 mm to 0.06 mm ～ 0.25 mm, which means the positioning accuracy is increased by an order of magnitude. What's more,the root-mean-square error and mean absolute error after compensation are reduced to 26.09% of the uncompensated errors.Meanwhile, the compensation method also significantly decreases the influence of the outliers, and possesses the outstanding robustness.
For the problem of low absolute positioning accuracy in industrial robots, the Optotrak Certus HD optical motion tracking system made by NDI company in Canada is utilized as the measurement device for the robot pose, and an optimallypruned extreme learning machine(OPELM) algorithm based on the self-born weighted least squares(SBWLS) method is proposed. The algorithm realizes the pose compensation effect of the robot end-effector by mapping the robot target pose to the revised pose. In order to verify and analyze the validity of the error compensation method, the end-effector of the Epson's6-axis robot is used to perform different motion modes, i.e., linear motion, circular motion, and discrete random motion, at a series of speeds in the experiment. The results show that the proposed error compensation method improves the pose accuracy of robot end-effector, and the total absolute positioning accuracy of the test point in X, Y, and Z axes is raised from 2 mm ～3 mm to 0.06 mm ～ 0.25 mm, which means the positioning accuracy is increased by an order of magnitude. What's more,the root-mean-square error and mean absolute error after compensation are reduced to 26.09% of the uncompensated errors.Meanwhile, the compensation method also significantly decreases the influence of the outliers, and possesses the outstanding robustness.
引文
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[24]Liu Z, Mei W J, Zeng X P, et al. Remaining useful life estimation of insulated gate biploar transistors(IGBTs)based on a novel volterra k-nearest neighbor optimally pruned extreme learning machine(VKOPP)model using degradation data[J].Sensors, 2017, 17(11):2524.
[25]Ge Y H, Yuan Y, Jia N. More efficient methods among commonly used robust estimation methods for GPS coordinate transformation[J]. Survey Review, 2013, 45(330):229-234.
[26]Lin Z R, Dai H D, Wu Z X, et al. Analysis of a six-axis industrial robot’s dynamic path accuracy based on an optical tracker[C]//5th International Conference on Enterprise Systems(ES). Piscataway, USA:IEEE, 2017:178-184.
[2]International Organization for Standardization. Manipulating industrial robots–Performance criteria and related test methods:ISO 9283:1998[S]. Geneva, Switzerland:ISO, 1998.
[3]Slamani M, Nubiola A, Bonev I. Assessment of the positioning performance of an industrial robot[J]. Industrial Robot, 2012,39(1):57-68.
[4]曾远帆,廖文和,田威.面向精度补偿的工业机器人采样点多目标优化[J].机器人,2017,39(2):239-248.Zeng Y F, Liao W H, Tian W. Multi-objective optimization of samples for industrial robot error compensation[J]. Robot,2017, 39(2):239-248.
[5]史晓佳,张福民,曲兴华,等.KUKA工业机器人位姿测量与在线误差补偿[J].机械工程学报,2017,53(8):1-7.Shi X J, Zhang F M, Qu X H, et al. Position and attitude measurement and online errors compensation for KUKA industrial robots[J]. Journal of Mechanical Engineering, 2017, 53(8):1-7.
[6]Chen G, Li T, Chu M, et al. Review on kinematics calibration technology of serial robots[J]. International Journal of Precision Engineering&Manufacturing, 2014, 15(8):1759-1774.
[7]刘志,赵正大,谢颖,等.考虑结构变形的机器人运动学标定及补偿[J].机器人,2015,37(3):376-384.Liu Z, Zhao Z D, Xie Y, et al. Kinematic calibration and compensation for a robot with structural deformation[J]. Robot,2015, 37(3):376-384.
[8]Tang H Y, He Z Y, Ma Y X, et al. A step identification method for kinematic calibration of a 6-DOF serial robot[M]. Singapore:Springer, 2017:1009-1020.
[9]Zhou W Y, Chen W Y, Liu H D, et al. A new forward kinematic algorithm for a general Stewart platform[J]. Mechanism&Machine Theory, 2015, 87:177-190.
[10]Stone H W. Kinematic modeling, identification, and control of robotic manipulators[M]. Boston, USA:Kluwer Academic Publishers, 1987.
[11]Rosvold J M, Atarod M, Frank C B, et al. An instrumented spatial linkage for measuring knee joint kinematics[J]. Knee, 2015,23(1):43-48.
[12]Li C, Wu Y, L?we H, et al. POE-based robot kinematic calibration using axis configuration space and the adjoint error model[J]. IEEE Transactions on Robotics, 2016, 32(5):1264-1279.
[13]Joubair A, Zhao L F, Bigras P, et al. Absolute accuracy analysis and improvement of a hybrid 6-DOF medical robot[J]. Industrial Robot, 2015, 42(1):44-53.
[14]张旭,郑泽龙,齐勇.6自由度串联机器人D-H模型参数辨识及标定[J].机器人,2016,38(3):360-370.Zhang X, Zheng Z L, Qi Y. Parameter identification and calibration of D-H model for 6-DOF serial robots[J]. Robot, 2016,38(3):360-370.
[15]Yu C Y, Xi J T. Simultaneous and on-line calibration of a robotbased inspecting system[J]. Robotics and Computer-Integrated Manufacturing, 2018, 49:349-360.
[16]Collin S. Kinematic robot calibration using a double ball-bar[D]. Lund, Sweden:Lund University, 2016.
[17]Joubair A, Bonev I A. Non-kinematic calibration of a six-axis serial robot using planar constraints[J]. Precision Engineering,2015, 40:325-333.
[18]Kamali K, Joubair A, Bonev I A, et al. Elasto-geometrical calibration of an industrial robot under multidirectional external loads using a laser tracker[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2016:4320-4327.
[19]郭青阳.基于KUKA工业机器人的定位误差补偿方法的研究[D].长春:长春工业大学,2016.Guo Q Y. Research of position error compensation method based on KUKA industrial robot[D]. Changchun:Changchun University of Technology, 2016.
[20]熊杰.六关节机器人误差补偿技术研究与实现[D].北京:中国科学院大学,2015.Xiong J. Research and implementation of error compensation technology on six-joint robot[D]. Beijing:University of Chinese Academy of Sciences, 2015.
[21]Boyd S, Vandenberghe L. Convex optimization[M]. New York,USA:Cambridge University Press, 2004.
[22]Huang G B, Zhu Q Y, Siew C K. Extreme learning machine:Theory and applications[J]. Neurocomputing, 2006, 70(1/2/3):489-501.
[23]Miche Y, Sorjamaa A, Bas P, et al. OP-ELM:Optimally pruned extreme learning machine[J]. IEEE Transactions on Neural Networks, 2010, 21(1):158-162.
[24]Liu Z, Mei W J, Zeng X P, et al. Remaining useful life estimation of insulated gate biploar transistors(IGBTs)based on a novel volterra k-nearest neighbor optimally pruned extreme learning machine(VKOPP)model using degradation data[J].Sensors, 2017, 17(11):2524.
[25]Ge Y H, Yuan Y, Jia N. More efficient methods among commonly used robust estimation methods for GPS coordinate transformation[J]. Survey Review, 2013, 45(330):229-234.
[26]Lin Z R, Dai H D, Wu Z X, et al. Analysis of a six-axis industrial robot’s dynamic path accuracy based on an optical tracker[C]//5th International Conference on Enterprise Systems(ES). Piscataway, USA:IEEE, 2017:178-184.