基于粒子群算法的机器人运动学参数标定研究

doi:10.3969/j.issn.1000-1158.2020.Z1.16

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摘要为了提高机器人末端绝对定位精度,提出了一种基于粒子群算法的机器人运动学几何参数标定方法,对工业机器人的参数误差进行辨识和补偿。首先,为了避免机械臂相邻两个轴平行时会出现奇异性的情况,采用了MDH参数法建立误差模型;其次,为了把测量数据定义在同一坐标轴上,使用结合模型精度补偿的机器人方位与手眼关系同步标定的坐标转换方法;然后用粒子群算法辨识出机器人的模型几何参数误差。通过对ABB工业机器人的仿真,实验计算和标定,机器人的平均绝对定位精度提高了66.9%。结果表明,文章中的标定算法可以有效地辨识出机器人的模型参数误差,对机器人的模型参数进行补偿后能有效提高绝对定位精度。

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	陈相君
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	班朝

关键词 ：计量学, 工业机器人, 粒子群算法, MDH模型, 坐标转换, 绝对定位精度

Abstract：In order to improve the absolute positioning accuracy of robot end, a calibration method of robot kinematic geometric parameters based on particle swarm optimization (PSO) was proposed to identify and compensate the parameter errors of industrial robots. First, in order to avoid the singularity when two adjacent axes of the manipulator are parallel, MDH parameter method was adopted to establish the error model. Secondly, in order to define the measurement data on the same coordinate axis, the coordinate conversion method of synchronous calibration of robot orientation and hand-eye relationship combined with model precision compensation was used. Then particle swarm optimization (PSO) algorithm was used to identify the model geometric parameter error. Through the simulation, experimental calculation and calibration of ABB industrial robots, the average absolute positioning accuracy of robots was improved by 66.9%. The results showed that the calibration algorithm can effectively identify the model parameter errors of the robot and improve the absolute positioning accuracy after compensating the model parameters of the robot.

Key words： metrology Cooperative robots Particle swarm algorithm MDH model Coordinate transformation Absolute positioning accuracy

PACS:

TB92

基金资助:国家重点研发计划(2018YFF0212701, 2018YFF0212702)

通讯作者: 任国营(1979-),河南开封人,中国计量科学研究院副研究员,博士,硕士研究生导师,主要研究方向为长度计量、机器人测试方法。Email: rengy@nim.ac.cn

作者简介: 陈相君(1996-),河南南阳人,天津大学硕士研究生,主要研究方向为机器人绝对定位的测量与补偿。Email:jun689074@163.com

引用本文:

陈相君,赵志方,任国营,栗大超,班朝. 基于粒子群算法的机器人运动学参数标定研究[J]. 计量学报, 2020, 41(12A): 85-91.
CHEN Xiang-jun,ZHAO Zhi-fang,Ren Guo-ying,LI Da-chao,BAN Zhao. Research on Robot Kinematics Parameter Calibration Based on Particle Swarm Optimization. Acta Metrologica Sinica, 2020, 41(12A): 85-91.

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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2020.Z1.16 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2020/V41/I12A/85

［1］Chen G, Li T, Chu M, et al. Review on kinematics calibration technology of serial robots［J］. International Journal of Precision Engineering and Manufacture, 2014, 15(8): 1759-1774
［2］Slamani M, Nubiola A, Bonev I. Assessment of the positioning performance of an industrial robot［J］. Industrial Robot, 2012, 39(1): 57-68
［3］齐俊德, 张定华, 李山, 等. 工业机器人绝对定位误差的建模与补偿［J］. 华南理工大学学报(自然科学版), 2016, 44(11): 113-118.
Qi J D, Zhang D H, Li S, et al. Modeling and compensation of absolute positioning error of industrial robots［J］. Journal of South China University of Technology (Natural Science Edition), 2016, 44(11): 113-118.
［4］王龙飞, 李旭, 张丽艳, 等. 工业机器人定位误差规律分析及基于ELM算法的精度补偿研究［J］. 机器人, 2018, 40(6): 843-851+859.
Wang L F, Li X, Zhang L Y, et al. Analysis of the positioning error of industrial robots and accuracy compensation based on ELM algorithm［J］. Robot, 2018, 40(6): 843-851+859
［5］戴厚德, 曾现萍, 游鸿修, 等. 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［J］. 机器人, 2019, 41(2): 206-215.
Dai H D, Zeng X P, You H X, et al. Pose measurement and error compensation of the robot end-effector based on an optical tracking system［J］. Robot, 2019, 41(2): 206-215.
［6］Hayati S A. Robot arm geometric link parameter estimation［C］// The 22nd IEEE Conference on Decision and Control. San Antonio, USA, 1983: 1477-1483.
［7］Zhang H, Roth Z S, Hamano F. A complete and parametrically continuous kinematic model for robot manipulators［J］. IEEE Transactions on Robotics and Automation, 1992, 8(4): 451-463.
［8］Yang X, Wu L, Li J, et al. A minimal kinematic model for serial robot calibration using POE formula［J］. Robotics and Computer-Integrated Manufacturing, 2014, 30(3): 326-334.
［9］唐宇存, 李锦忠, 林安迪, 等. 基于三坐标测量机的机器人位姿精度检测方法［J］. 计算机工程与应用, 2020, 56(5): 257-262.
Tang Y C, Li J Z, Lin A D, et al. Method for measuring robot pose accuracy based on coordinate measuring machine［J］. Computer Engineering and Applications, 2020, 56(5): 257-262.
［10］张旭, 郑泽龙. 主动光立体标靶检测及其在工业机器人位姿测量中的应用［J］. 中国机械工程, 2016, 27(19): 2594-2601.
Zhang X, Zheng Z L. Active Light Stereo Target Detection and Its Applications in Industrial Robots Pose Measurement［J］. Chinese Journal of Mechanical Engineering, 2016, 27(19): 2594-2601.
［11］刘春学, 郑哲恩, 孙坚, 等. 六自由度工业机器人本体标定实验的研究［J］. 机床与液压, 2017, 45(5): 31-34, 111.
Liu C X, Zheng Z E, Sun J, et al. Research on Six Degree of Freedom Industrial Robot of Body Calibration Experiment［J］. Machine Tool & Hydraulics, 2017, 45(5): 31-34, 111.
［12］王跃灵, 旺玥, 王琪, 等. 基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识［J］. 计量学报, 2020, 41(1): 60-66.
Wang Y L, Wang Y, Wang Q, et al. Dynamic parameter identification of flexible joint robot based on adaptive particle swarm optimization genetic algorithm［J］. Acta Metrologica Sinica, 2020, 41(1): 60-66.
［13］赵春芳, 李江昊, 张大伟. 基于改进免疫遗传优化蚁群算法的移动机器人路径寻优研究［J］. 计量学报, 2019, 40(3): 505-510.
Zhao C F, Li J H, Zhang D W. Path optimization of mobile robots based on improved immune genetic optimization ant colony algorithm［J］. Acta Metrologica Sinica, 2019, 40(3): 505-510.
［14］刘宇, 李瑰贤, 夏丹, 等. 基于改进遗传算法辨识空间机器人动力学参数［J］. 哈尔滨工业大学学报, 2010, 42(11): 1734-1739.
Liu Y, Li G X, Xia D, et al. Identification of dynamic parameters of space robot based on improved genetic algorithm［J］. Journal of Harbin Institute of Technology, 2010, 42(11): 1734-1739.
［15］金轲, 俞桂英, 丁烨, 等. 基于差分进化算法的手眼标定方法［J］. 机械与电子, 2020, 38(4): 71-75.
Jin K, Yu G Y, Ding Y, et al. Hand-eye calibration method based on differential evolutionary algorithm［J］. Machinery & electronics, 2020, 38(4): 71-75.
［16］胡为, 刘冲, 傅莉, 等. 一种高精度的机器人手眼标定算法［J］. 火力与指挥控制, 2018, 43(9): 19-24.
Hu W, Liu Ch, Fu L, et al. A high-precision hand-eye calibration algorithm for robots［J］. Firepower And Command Control, 2016, 43(9): 19-24.
［17］王红艳. 基于粒子群算法的物流路径优化方法研究［J］. 电子设计工程, 2020, 28(17): 61-65.
Wang H Y. Research on optimization method of logistics path based on particle swarm optimization［J］. Electronic design engineering, 2020, 28(17): 61-65.
［18］张树梅, 邓子龙, 高兴军. 基于改进粒子群算法的6-PTRT并联机器人运动学研究［J］. 制造业自动化, 2020, 42(4): 65-68, 93.
Zhang S M, Deng Z L, Gao X J. Kinematics research of 6-PTRT Parallel Robot based on improved Particle Swarm Optimization［J］. Manufacturing Automation, 2020, 42(4): 65-68, 93.
［19］费业泰. 误差理论与数据处理［M］. 北京: 机械工业出版社, 2004.
［20］蔡自兴. 机器人学［M］. 北京: 清华大学出版社, 2000.
［21］任瑜, 张丰, 郭志敏, 等. 一种通用的工业机器人位姿检测方法［J］. 计量学报, 2018, 39(5): 615-621.
Ren Y, Zhang F, Guo Z M, et al. A General industrial Robot pose detection method［J］. Acta Metrologica Sinica, 2015, 39(5): 615-621.
［22］熊有伦. 工业机器人技术基础［M］. 华中科技大学出版社, 1999.
［23］田鹏飞, 杨树明, 吴孜越, 等. 结合精度补偿的机器人优化手眼标定方法［J］. 西安交通大学学报, 2020, 54(8): 99-106.
Tian P F, Yang S M, Wu Z Y, et al. Optimization precision compensation of robot hand-eye calibration method［J］. Journal of Xian Jiaotong University, 2020, 54(8): 99-106.
［24］王伟. 基于卡尔曼滤波和加权LM法的井下精确定位算法［J］. 工矿自动化, 2019, 45(11): 5-9.
Wang W. Downhole accurate positioning algorithm based on kalman filter and weighted LM method［J］. Industrial and mining automation, 2019, 45(11): 5-9.