Error Modelling and Differential-Evolution-Based Parameter Identification Method for Redundant Hybrid Robot
Error Modelling and Differential-Evolution-Based Parameter Identification Method for Redundant Hybrid Robot
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DOI：
10.2316/journal.205.2012.4.205-5750
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发表时间：
2012-01
影响因子：
3.1
通讯作者：
Yongbo Wang;Huapeng Wu;H. Handroos
中科院分区：
文献类型：
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作者：
Yongbo Wang;Huapeng Wu;H. Handroos
摘要研究了一种10自由度冗余串并联混联式多部门焊接/切割机器人的几何误差建模与参数辨识。提出的混合机器人由一个运动冗余的4自由度串联机构，以扩大工作空间和6自由度Stewart并联机器人，以提高末端执行器的精度。由于其冗余自由度和串并联结构，传统的针对纯串联或纯并联机器人的误差建模和辨识方法无法直接使用。将传统的正标定和逆标定方法相结合，提出了一种冗余度串并联混联机器人的混合误差建模方法。此外，由于推导的混合误差模型的高度非线性和多模态特性，传统的迭代线性最小二乘算法不能被用来识别的误差参数。本文采用一种简单易用、功能强大的进化全局优化算法差分进化（DE）来搜索误差模型中所有误差参数的一组最优组合，以最小化测量腿长与预测腿长的差异。通过在真实的误差参数容限范围内产生随机制造和装配误差进行数值模拟和分析。同时，在工作空间中随机产生末端执行器的不同测量位姿和相应的串联机构的关节位移，以模拟真实的物理行为。仿真结果表明，基于DE的参数辨识方法是鲁棒可靠的，所有的预设误差都可以成功地恢复。仿真结果还表明，该混合标定方法避免了串并联机构连接点的外部位姿测量，对串并联机器人末端执行器的位姿测量能有效满足标定目的。
Abstract 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.