Graded Compensation Method of Robot Positioning Error Based on GBDT Algorithm
In order to further improve the positioning accuracy of industrial robots,a graded compensation method was proposed to reduce the positioning error caused by geometric and non-geometric factors.The genetic algorithm optimized least squares method(GA-LS)was used to identify the geometric parameter errors,and then the geometric parameter errors were compensated it into the ro-bot kinematics model.Then the gradient boosting decision tree(GBDT)model was used to predict and compensate the residual non-ge-ometric parameter errors,and finally the UR10 robot was used as the research object for experiments to verify the accuracy of the meth-od.The experimental results show that the graded compensation method can effectively improve the absolute positioning accuracy of the robot,and the average positioning error of the robot is reduced from 2.381 mm to 0.156 mm after compensation,the positioning accuracy is increased by 93.4％,the root mean square positioning error is reduced from 2.417 mm to 0.163 mm,and the positioning accuracy is improved by 93.2％.The effectiveness of the graded compensation method is verified by the experimental results.
robot calibrationerror identificationabsolute positioning accuracygradient boosting decision tree
万方数据
维普
