Optics and Precision Engineering, Volume. 32, Issue 18, 2783(2024)
Spatial error prediction method for industrial robot based on Support Vector Regression
Figures & Tables(12)
Fig. 1. Schematic diagram of the joint coordinate system for the Staubli TX60 industrial robot
Fig. 4. Spatial distribution of pose measurement points for Staubli TX60 industrial robot
Table 1. Theoretical MD-H parameters of Staubli TX60 robot
View tableView in ArticleTable 1. Theoretical MD-H parameters of Staubli TX60 robot
i θi/rad di/mm ai/mm αi/rad βi/rad 1 π 0 0 π/2 - 2 π/2 0 290 0 0 3 π/2 20 0 π/2 - 4 π 310 0 π/2 - 5 π 0 0 π/2 - 6 0 70 0 0 -
Table 2. Neural network parameter settings
View tableView in ArticleTable 2. Neural network parameter settings
Number of layer Inputs Layer Middle Layer Outputs Layer BP 4 6 20/20 6 Elman 4 6 20/20 6
Table 3. Comparison experimental results of robot pose error compensation
View tableView in ArticleTable 3. Comparison experimental results of robot pose error compensation
Model Average error Maximum error Standard deviation of error Position/mm Attitude/（°） Position/mm Attitude/（°） Position/mm Attitude/（°） Origin 0.706 1 0.174 2 1.194 7 0.280 4 0.276 0 0.051 0 BP 0.052 6 0.057 5 0.136 8 0.134 0 0.024 1 0.024 5 Elman 0.051 7 0.059 0 0.140 4 0.145 3 0.023 4 0.026 9 LM 0.196 8 0.065 9 0.587 6 0.201 8 0.083 2 0.037 5 SVR 0.055 6 0.024 6 0.152 3 0.107 3 0.023 9 0.017 5
Tools
Get Citation
Copy Citation Text
Guifang QIAO, Chunhui GAO, Xinyi JIANG, Simin XU, Di LIU. Spatial error prediction method for industrial robot based on Support Vector Regression[J]. Optics and Precision Engineering, 2024, 32(18): 2783
Paper Information
Category:
Received: May. 28, 2024
Accepted: --
Published Online: Nov. 18, 2024
The Author Email: QIAO Guifang (qiaoguifang@126.com)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20