首页> 外文期刊>International Journal Of Modelling & Simulation >ERROR MODELLING AND DIFFERENTIAL-EVOLUTION-BASED PARAMETER IDENTIFICATION METHOD FOR REDUNDANT HYBRID ROBOT
【24h】

ERROR MODELLING AND DIFFERENTIAL-EVOLUTION-BASED PARAMETER IDENTIFICATION METHOD FOR REDUNDANT HYBRID ROBOT

机译:冗余混合机器人误差建模和基于微分进化的参数识别方法

获取原文
获取原文并翻译 | 示例

摘要

This paper focuses on the geometrical error modelling and parameter identification of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid intersector welding/cutting robot (IWR). The proposed hybrid robot consists of a kinematically redundant 4-DOF serial mechanism to enlarge workspace and a 6-DOF Stewart parallel robot to improve the end-effector accuracy. Due to its redundant degrees of freedom and the serial-parallel structure, the traditional error modelling and identification methods which tailored for pure serial robot or pure parallel robot cannot be directly used. In this paper, a hybrid error modelling method for redundant serial-parallel hybrid robot is presented by combining both the traditional forward calibration and inverse calibration method. Furthermore, because of the high nonlinear and multi-modal characteristics of the derived hybrid error model, the traditional iterative linear least-square algorithm cannot be utilized to identify the error parameters. In this paper, an easy-to-use and powerful evolutionary global optimization algorithm named differential evolution (DE) is employed to search for a set of optimum combination of all error parameters in the error model to minimize the discrepancies of measured and predicted leg lengths. Numerical simulation and analysis are conducted by generating random manufacturing and assembly errors within the real error parameter tolerance range. Meanwhile, different measurement poses of the end-effector and the corresponding joint displacements of the serial mechanism are also randomly generated in the workspace to simulate the real physical behaviours. The simulation results show that the DE-based parameter identification method is robust and reliable, and all of the preset errors can be successfully recovered. The simulation also shows that the hybrid calibration method can avoid the external pose measurement of the connecting point between serial and parallel mechanism, and the pose measurement of the end-effector of serial-parallel robot can satisfy the calibration purpose effectively.
机译:本文着重于10自由度(DOF)冗余串并联混合跨部门焊接/切割机器人(IWR)的几何误差建模和参数识别。拟议的混合机器人由运动学上冗余的4自由度串行机构(可扩大工作空间)和6自由度Stewart并联机器人组成,可提高末端执行器的精度。由于其冗余的自由度和串行-并行结构,传统的错误建模和识别方法不能直接用于纯串行机器人或纯并行机器人。本文结合传统的前向校准和逆向校准方法,提出了一种冗余的串并联混合机器人的误差建模方法。此外,由于导出的混合误差模型的高非线性和多模态特性,传统的迭代线性最小二乘算法无法用于识别误差参数。在本文中,采用了一种易于使用且功能强大的进化全局优化算法,称为差分进化(DE),以寻找误差模型中所有误差参数的一组最佳组合,以最大程度地减少实测和预测支腿长度的差异。通过在实际误差参数公差范围内生成随机的制造和装配误差来进行数值模拟和分析。同时,端部执行器的不同测量姿势和串行机构的相应关节位移也在工作空间中随机生成,以模拟实际的物理行为。仿真结果表明,基于DE的参数辨识方法可靠,可靠,能够成功地恢复所有预设误差。仿真结果表明,混合标定方法可以避免串并联机构连接点的外部姿态测量,而串并联机器人末端执行器的姿态测量可以有效地满足标定目的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号