A prediction and compensation method of robot tracking error considering pose-dependent load decomposition

Industrial robots are widely used because of their high flexibility and low cost compared with CNC machine tools, but the low tracking accuracy limits their application in the field of high-precision manufacturing. To improve the tracking accuracy and solve the complex modeling problems, a prediction and compensation method of robot tracking error is proposed based on temporal convolutional network (TCN), where the pose-dependent effect of load on joint tracking error is considered. The terminal load is decomposed to joint load by using Ja-cobian matrix and then used as the pose-dependent information of the data-based model. A prediction model based on TCN is used to predict the tracking error of joints. Finally, a pre-compensation method is adopted to improve the joint tracking accuracy based on the predicted errors. Experimental results show that the model presents good prediction and compensation accuracy. The mean absolute tracking errors are increased by more than 80% in the test path. This method can effectively compensate the tracking errors of the robot joints and therefore greatly improve the tracking accuracy of the tool center point and tool orientation in the Cartesian coordinate system.

Automated summary

Industrial robots are widely used due to their high flexibility and low cost compared to CNC machine tools. However, their low tracking accuracy hinders their application in high-precision manufacturing. To overcome this, a prediction and compensation method using temporal convolutional network (TCN) is proposed to improve tracking accuracy by considering the pose-dependent effect of load on joint tracking error. Experimental results demonstrate that the model effectively predicts and compensates for errors, significantly enhancing tracking accuracy in the Cartesian coordinate system.