摘要
针对环境或人为因素引入的测量粗差对测量坐标系和机器人基坐标系的转换存在较大影响的问题,对奇异值分解(SVD)算法进行了改进,并将其应用于机器人运动学标定中。以ABB-IRB2600型机器人为研究对象,建立修正型D-H(MD-H)运动学模型和误差模型;通过激光跟踪仪测量得到机器人末端靶球位置坐标,在SVD算法中,根据补偿前位置误差大小对测量数据重新分配权重,转换测量坐标系和机器人基坐标系;使用LevenbergMarquart(L-M)算法进行了误差参数辨识,并在Matlab中对机器人25个运动学参数进行了仿真补偿。仿真和实验结果表明,加权SVD算法稳定性更优,能够减小测量粗差影响,经标定后机器人的平均绝对误差降低了65.10%,均方根误差降低了65.85%,其绝对定位精度得到了明显提高。
Aiming at the problem that the gross error of measurement introduced by environmental or human factors has a great influence on the conversion of measurement coordinate system and base coordinate system of robot,a method is proposed that the singular value decomposition( SVD) algorithm is improved and applied to the robot kinematics calibration. Taking ABB-IRB2600 robot as the research object,modified D-H( MD-H) kinematics model and error model were established. The position coordinates of the target sphere at the end of robot were measured by the laser tracker. In the SVD algorithm,the weight of the measured data was redistributed according to the position error before compensation,and the measurement coordinate system and the robot base coordinate system were converted. Levenberg-Marquardt( L-M)algorithm was used to identify the error parameters,and 25 kinematic parameters of the robot were simulated and compensated in Matlab. Simulation and experimental results show that the weighted SVD algorithm has better stability and can reduce the impact of gross errors. After calibration,for the average absolute error of the robot is reduced by65. 10 % and the root mean square error by 65. 85 %,and its absolute positioning accuracy is obviously improved after calibration.
作者
班朝
任国营
王斌锐
陈相君
薛梓
王凌
BAN Zhao;REN Guo-ying;WANG Bin-rui;CHEN Xiang-jun;XUE Zi;WANG Ling(College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou,Zhejiang 310018,China;National Institute of Metrology,Beijing 100029,China;State Key Laboratory of Precision Measurement Technology and Instrument,Tianjin University,Tianjin 300072,China)
出处
《计量学报》
CSCD
北大核心
2021年第9期1128-1135,共8页
Acta Metrologica Sinica
基金
国家重点研发计划(2018YFF0212701,2018YFF0212702,2018YFB2101004)。
