A hierarchical closed-loop optimization strategy was proposed to solve the problem of cumulative error in robot machining system. A core closed-loop composed of the laser tracker frame, robot base frame and the end tool frame was constructed. An auxiliary closed-loop composed of the laser tracker frame, end tool frame, visual measurement frame and the workpiece was constructed. A correlational closed-loop composed of the laser tracker frame, visual measurement frame and the end tool frame was constructed. Then a neural network model was constructed based on the independent optimization of three closed-loops, and the results of the closed-loops optimization were fused by using a data-driven approach in order to improve the closed-loop optimization stability of the system. Simulation experimental results show that the method can effectively reduce the cumulative error of robot machining system and improve the positioning accuracy of the robot end tool, which is conducive to guide the subsequent prototype experiment and machining application.
Yan HAO,Ya-bin DING,Jin-sheng FU. Hierarchical closed-loop optimization strategy for cumulative error of robot machining system. Journal of ZheJiang University (Engineering Science), 2021, 55(6): 1142-1149.
Fig.2Fully connected neural network model fusing closed-loop optimization results
位姿数
平均位置误差
平均角度误差
${\varDelta _{\rm{1}}}$ /mm
${\varDelta _{\rm{2}}}$ /mm
Φ /%
${\omega _{\rm{1}}}$ /(°)
${\omega _{\rm{2}}}$ /(°)
Φ /%
30
0.076
0.064
15.39
0.116
0.083
28.45
50
0.073
0.053
27.40
0.119
0.089
25.21
70
0.068
0.056
17.65
0.128
0.094
26.56
位姿数
最大位置误差
最大角度误差
${\varDelta _{\rm{3}}}$ /mm
${\varDelta _{\rm{4}}}$ /mm
Φ /%
${\omega _{\rm{3}}}$ /(°)
${\omega _{\rm{4}}}$ /(°)
Φ /%
30
0.123
0.109
11.38
0.198
0.169
14.65
50
0.105
0.088
16.19
0.190
0.161
15.26
70
0.104
0.098
5.77
0.234
0.185
20.94
Tab.1Core closed-loop optimization test results
Fig.3
Fig.3Core closed-loop optimization effects of different robot poses
Fig.4Errors of sampling points after closed-loop optimization and neural network fusion
Fig.5Neural network fusion effect of auxiliary and correlational closed-loop optimization
Fig.6Errors of sampling points before optimization, after closed-loop optimization and neural network fusion
Fig.7Reduced percentage of point errors after neural network fusion than before optimization
Fig.8Part of workpiece and predetermined target points on workpiece
Fig.9Positions of robot end tool
Fig.10Position errors of robot end tool
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