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多臂协同焊接机器人运动学逆解及误差分析

曾氢菲, 刘雪梅, 邱呈溶

曾氢菲, 刘雪梅, 邱呈溶. 多臂协同焊接机器人运动学逆解及误差分析[J]. 焊接学报, 2019, 40(11): 21-27. DOI: 10.12073/j.hjxb.2019400282
引用本文: 曾氢菲, 刘雪梅, 邱呈溶. 多臂协同焊接机器人运动学逆解及误差分析[J]. 焊接学报, 2019, 40(11): 21-27. DOI: 10.12073/j.hjxb.2019400282
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ZENG Qingfei, LIU Xuemei, QIU Chengrong. Inverse kinematics and error analysis of cooperative welding robot with multiple manipulators[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 21-27. DOI: 10.12073/j.hjxb.2019400282
Citation: ZENG Qingfei, LIU Xuemei, QIU Chengrong. Inverse kinematics and error analysis of cooperative welding robot with multiple manipulators[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 21-27. DOI: 10.12073/j.hjxb.2019400282
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多臂协同焊接机器人运动学逆解及误差分析

  • 同济大学, 上海 201804

基金项目: 国家重点研发计划（2017YFB1301600）

Inverse kinematics and error analysis of cooperative welding robot with multiple manipulators

  • Tongji University, Shanghai 213016, China

摘要

    摘要
  • 摘要: 为了实现飞机双曲度壁板与桁条之间T形接头双侧焊缝的精准焊接，提出基于神经网络的多臂协同焊接机器人逆运动学求解方法.在构建多臂协同焊接机器人DH模型并进行有效性验证的基础上，考虑焊接机器人各关节运动范围，获取样本数据.基于BP和RBF神经网络，将18个关节子空间映射到三条机械臂末端作业空间，把高维、非线性逆运动学求解问题转换为多输入多输出预测模型.对两种神经网络模型求解效果进行对比.结果表明，基于神经网络的多臂焊接机器人运动学逆解求解结果精度高，其中BP神经网络求解速度更快，而RBF神经网络的预测效果更好.
    Abstract: In order to realize the precise welding of T-joint between aircraft hyperbolic panel and the stringers, a method of solving inverse kinematics equations for cooperative welding robot with multiple manipulators based on neural network was presented. On the basis of building an effective DH model of cooperative welding robot with multiple manipulators, sample data was obtained considering the movement ranges of the robot joints. Based on BP and RBF neural networks, 18 joint subspaces are mapped to the workspaces of three manipulators. The high-dimensional and non-linear inverse kinematics problem was transformed into multi-input and multi-output prediction model. The results show that prediction model of solving cooperative welding robot kinematics equations has high accuracy, among which the BP prediction model is faster and the RBF prediction model is better.
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    • 参考文献(14)
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  • 收稿日期:  2019-03-21

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