为了提升串联机器人绝对定位精度,提出了基于零参考模型(ZRM)的机器人几何参数标定方法。建立了包含方向矢量和连接矢量的机器人零参考模型;针对模型特点,利用改进遗传算法(IGA)优化求解零位方向分量和位置方向分量,给出了用IGA标定几何参数目标函数值计算方法及求解几何参数误差的具体步骤。通过对ER10L-C10工业机器人不同测点下仿真标定及实测研究结果表明:IGA方法能够快速对机器人ZRM的几何参数实现标定,当标定点设定为50个左右时,标定后的机器人在测试点的精度提升泛化能力较好,对ER10L-C10机器人在整个工作空间内实测标定其末端绝对定位精度提升约90%,该方法适于在有高定位精度要求的串联机器人中推广应用。
Abstract
In order to enhance the absolute positioning accuracy of the robot, a calibration method based on zero reference model (ZRM) is proposed. ZRM including direction and connection vectors is founded. According to the features of ZRM, improved genetic algorithm (IGA) is used to search the solution of the direction and position components of zero position vector. The objective function computation methods and the detailed steps of calibrating geometric parameters of ZRM based on IGA are given. Finally, by carrying out the simulation and real measurement research for ER10L-C10 industrial robot, the results show that the geometric parameters of ZRM can be quickly calibrated by IGA. When the calibration point is set at about 50, the accuracy improvement of the test points after calibration has good generalization ability and in the whole workspace the positioning accuracy of the ER10L-C10 end-effector is improved about 90%.This method is suitable to be applied in serial robot calibration with high positioning accuracy requirements.
关键词
计量学 /
串联机器人 /
几何参数标定 /
零参考模型 /
改进遗传算法 /
定位精度
Key words
metrology /
serial robot /
geometric parameters calibration /
ZRM /
IGA /
positioning accuracy
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
［1］中华人民共和国科学技术部. 科技部关于发布国家重点研发计划“智能机器人”等重点专项 2018 年度项目申报指南的通知［EB］. http://www.most.gov.cn/fggw/zfwj/zfwj2018/201808/t20180803_141058. htm, 2018-08-03
［2］温秀兰, 崔俊宇, 芮平, 等. 轴线测量与迭代补偿的机器人几何参数标定［J］. 计量学报, 2018, 39(4): 449-454.
Wen X L, Cui J Y, Rui P, et al. Robot Geometric Parameters Calibration Based on Axis Measurement and Iterative Compensation［J］. Acta Metrologica Sinica, 2018, 39(4): 449-454.
［3］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.
［4］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 Pose Testing Method of Industrial Robot ［J］. Acta Metrologica Sinica, 2018, 39(5): 615-621.
［5］Hayati S A. Robot arm geometric link parameter estimation［C］∥ The 22nd IEEE Conference on Decision and Control. San Antonio, USA, 1983: 1477-1483.
［6］Schroer K, Albrighti L S, Grethlein M. Complete, minimal and model-continuous kinematic models for robot calibration［J］. Robot Computer Integrated Manufacture, 1997, 13(1): 73-85.
［7］白云飞, 丛明, 杨小磊, 等. 基于6参数模型的6R串联机器人运动学参数辨识 ［J］. 机器人, 2015, 37(4): 486-492.
Bai Y F, Cong M, Yang X L, et al. Kinematic parameter identification for 6R serial robots based on a 6-Parameter model ［J］. Robot, 2015, 37(4): 486-492.
［8］He R, Zhao, Y, Yang S, et al. Kinematic Parameter Identification for Serial-Robot Calibration based on POE Formula ［J］. IEEE Transactions on Robotics, 2010, 26(3): 411-423.
［9］Yang S, Zhao Y, He R, et al. Experimentation on Identifying the Kinematic Parameters of Serial Mechanism based on the Product-of-Exponential Formula ［J］. Robot, 2011, 33(1): 1, 35-39.
［10］Liu Y, Jiang Y S, Liang B, et al, Calibration of a 6-DOF space robot using genetic algorithm［J］. Chinese Journal of Mechanical Engineering, 2008, 21(6): 6-12.
［11］周炜, 廖文和, 田威, 等, 基于粒子群优化神经网络的机器人精度补偿方法研究［J］. 中国机械工程, 2013, 24(2): 174-179.
Zhou C, Liao W H, Tian W, et al. Method of industrial robot accuracy compensation based on particle swarm optimization neural network ［J］. China Mechanical Engineering, 2013, 24(2): 174-179.
［12］Nguyen H N, Zhou J, Kang H J, A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network［J］. Neurocomputing, 2015, 151: 996-1005.
［13］房立金, 党鹏飞. 基于量子粒子群优化算法的机器人运动学标定方法［J］. 机械工程学报, 2016, 52(7): 23-30.
Fang L J, Dang P F. Kinematic calibration method of robots based on quantum-behaved particle swarm optimization ［J］. Journal of Mechanical Engineering, 2016, 52(7): 23-30.
［14］Zhao G, Zhang P F, Ma G C, et al. System identification of the nonlinear residual errors of an industrial robot using massive measurements［J］. Robotics and Computer Integrated Manufacturing, 2019, 59(1): 104-114.
［15］Gupta K C, Kinematic Analysis of Manipulators Using the Zero Reference Position Description［J］. International Journal Robotics Research, 1986, 5(2): 5-13.
［16］Kazerounian K, Qian G Z. Kinematic calibration of robotic manipulators［J］. Transaction of the ASME, 1989, 111: 482-487.
［17］Mooring B, Roth Z, Driels M. Fundamentals of manipulator calibration［M］. New York: Wiley, 1991: 28-36.
［18］Wen X L, Xia Q G, Zhao Y B. An effective genetic algorithm for circularity error unified evaluation［J］. International Journal of Machine Tools & Manufacture, 2006, 46(11): 1770-1777.
［19］GB/T 12642-2013 工业机器人性能规范及其试验方法标准［S］. 2013.
基金
国家自然科学基金(51675259,51905258);江苏省自然科学基金(BK20170763);江苏省研究生创新训练项目(SJCX19_0513)
PDF(1600 KB)
