A prediction and compensation method of robot tracking error considering pose-dependent load decomposition |
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| Affiliation: | 1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, 310027, Hang Zhou, PR China;2. Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, 310027, Hang Zhou, PR China;3. Hangzhou Innovation Institute, Beihang University, 310051, Hang Zhou, PR China;4. Department of mechanical engineering, Zhejiang University of Technology, 310023, Hang Zhou, PR China;1. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300350, China;2. Shanghai Aerospace Equipments Manufacturer Co., Ltd, Shanghai 200245, China;1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China;2. Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China;3. AVIC Shenyang Aircraft Corporation, Shenyang, 110850, China |
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| Abstract: | 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 Jacobian 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. |
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