齿轮机器人复杂装配过程在线建模与参数优化

doi:10.3901/JME.2021.13.124

机械工程学报 ›› 2021, Vol. 57 ›› Issue (13): 124-131.doi: 10.3901/JME.2021.13.124

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齿轮机器人复杂装配过程在线建模与参数优化

刘冬^1,2, 袁利恒¹, 丛明¹

1. 大连理工大学机械工程学院大连 116024;
2. 大连理工江苏研究院有限公司常州 213164

收稿日期:2020-07-15 修回日期:2021-03-09 出版日期:2021-07-05 发布日期:2021-08-31
通讯作者: 丛明(通信作者),男,1963年出生,博士,教授,博士研究生导师。主要研究方向为机器人技术与应用。E-mail:congm@dlut.edu.cn
作者简介:刘冬,男,1985年出生,博士,副教授。主要研究方向为智能机器人与系统、智能控制。E-mail:liud@dlut.edu.cn
基金资助:
国家自然科学基金（61873045）、江苏省自然科学基金（BK20180190）和大连市科技创新基金（2019J12GX043）资助项目

Online Modeling and Parameter Optimization Method for Robotic Complex Assembly Process of Gear

LIU Dong^1,2, YUAN Liheng¹, CONG Ming¹

1. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024;
2. Dalian University of Technology Jiangsu Research Institute Co., Ltd., Changzhou 213164

Received:2020-07-15 Revised:2021-03-09 Online:2021-07-05 Published:2021-08-31

摘要/Abstract

摘要： 以缓速器装配生产线齿轮装配工位为研究背景，提出一种复杂机器人装配过程在线建模学习与参数优化方法，解决当前离线方法装配成功率低和效率低的问题。针对复杂多变的机器人齿轮装配过程，基于高斯过程回归建立机器人接触状态与机器人动作的动态模型，提出一种新的基于生成对抗的粒子群优化算法用于在线学习，生成装配关键参数优化策略，并采用支持向量数据描述对新颖装配数据进行检测，最终实现装配过程在线建模与参数优化。齿轮与花键轴机器人装配实验结果表明，基于生成对抗粒子群-高斯过程回归的装配方法在装配成功率与效率上均优于人工和离线方法，可以对生产线不同批次和规格的齿轮进行在线装配，满足缓速器装配生产线的实际生产需求。

关键词: 工业机器人, 齿轮装配, 在线学习, 改进粒子群算法, 高斯过程回归

Abstract: Taking the gear assembly station of the retarder assembly line as a research background, an online modeling learning and parameter optimization method for complex robot assembly process is proposed to solve the problems of low assembly success rate and low efficiency using current offline methods. For the complex and changeable robotic assembly process of gear, the dynamic model of contact states and robot motion is established based on Gaussian process regression (GPR). A new particle swarm optimization algorithm based on generative adversarial algorithm (GAPSO) is proposed for online learning to generate optimization strategy of assembly key parameters. Support vector data description (SVDD) is utilized to detect the new assembly data. The method finally realizes the online modeling and parameter optimization of the assembly process. Experiment results of the gear and spline shaft show that the GAPSO-GPR method is superior to the manual and offline methods in assembly success rate and efficiency, and can be used for online assembly of gears with different batches and specifications, which can meet the actual production demand of the retarder assembly line. Key words:industrial robots; gear assembly; online learning; improved particle swarm optimization; Gaussian process regression

Key words: industrial robots, gear assembly, online learning, improved particle swarm optimization, Gaussian process regression

中图分类号:

TP242

刘冬, 袁利恒, 丛明. 齿轮机器人复杂装配过程在线建模与参数优化[J]. 机械工程学报, 2021, 57(13): 124-131.

LIU Dong, YUAN Liheng, CONG Ming. Online Modeling and Parameter Optimization Method for Robotic Complex Assembly Process of Gear[J]. Journal of Mechanical Engineering, 2021, 57(13): 124-131.

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齿轮机器人复杂装配过程在线建模与参数优化

Online Modeling and Parameter Optimization Method for Robotic Complex Assembly Process of Gear

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