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Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (6): 1142-1149    DOI: 10.3785/j.issn.1008-973X.2021.06.015
    
Hierarchical closed-loop optimization strategy for cumulative error of robot machining system
Yan HAO(),Ya-bin DING*(),Jin-sheng FU
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, China
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Abstract  

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.



Key wordsrobot machining      positioning accuracy      cumulative error      closed-loop optimization      neural network      visual measurement     
Received: 03 July 2020      Published: 30 July 2021
CLC:  TH 161  
  TB 96  
Fund:  国家自然科学基金资助项目(51775376)
Corresponding Authors: Ya-bin DING     E-mail: hyhaoyan@tju.edu.cn;ybding@tju.edu.cn
Cite this article:

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.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.06.015     OR     https://www.zjujournals.com/eng/Y2021/V55/I6/1142


机器人加工系统累积误差逐级闭环优化策略

针对机器人加工系统的误差累积问题,提出逐级闭环优化策略. 构建以激光跟踪仪、机器人基坐标系和末端工具坐标系组成的核心闭环,以激光跟踪仪、末端工具坐标系、视觉测量坐标系和工件组成的辅助闭环,以激光跟踪仪、视觉测量坐标系和末端工具坐标系组成的相关性闭环. 基于3个闭环独立优化,构建神经网络模型,以数据驱动的方式融合闭环优化结果,提高系统闭环优化的稳定性. 仿真实验结果表明,利用该方法有效地减小系统累积误差,提高机器人末端工具的定位精度,有助于指导后续的样机实验和加工应用.


关键词: 机器人加工,  定位精度,  累积误差,  闭环优化,  神经网络,  视觉测量 
Fig.1 Robot machining system
Fig.2 Fully 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.1 Core closed-loop optimization test results
Fig.3 
Fig.3 Core closed-loop optimization effects of different robot poses
Fig.4 Errors of sampling points after closed-loop optimization and neural network fusion
Fig.5 Neural network fusion effect of auxiliary and correlational closed-loop optimization
Fig.6 Errors of sampling points before optimization, after closed-loop optimization and neural network fusion
Fig.7 Reduced percentage of point errors after neural network fusion than before optimization
Fig.8 Part of workpiece and predetermined target points on workpiece
Fig.9 Positions of robot end tool
Fig.10 Position errors of robot end tool
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