基于改进遗传算法实现柔性三坐标测量机参数标定

赵磊; 刘书桂

doi:10.3788/OPE.20111911.2753

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基于改进遗传算法实现柔性三坐标测量机参数标定

信息科学 | 更新时间：2020-08-12

- 基于改进遗传算法实现柔性三坐标测量机参数标定
- Implementation of parameter calibration for flexible coordinate measurement machine based on improving genetic algorithm
- 光学精密工程 2011年19卷第11期页码：2753-2758
- 作者机构：
  
  天津大学精密测试技术及仪器国家重点实验室天津,300072
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.50475116)()
- DOI：10.3788/OPE.20111911.2753
  中图分类号： TH721
- 收稿日期：2011-05-09，
  
  修回日期：2011-06-22，
  
  网络出版日期：2011-11-25，
  
  纸质出版日期：2011-11-25
- 稿件说明：
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赵磊, 刘书桂. 基于改进遗传算法实现柔性三坐标测量机参数标定[J]. 光学精密工程, 2011,19(11): 2753-2758

ZHAO Lei, LIU Shu-gui. Implementation of parameter calibration for flexible coordinate measurement machine based on improving genetic algorithm[J]. Editorial Office of Optics and Precision Engineering, 2011,19(11): 2753-2758
赵磊, 刘书桂. 基于改进遗传算法实现柔性三坐标测量机参数标定[J]. 光学精密工程, 2011,19(11): 2753-2758 DOI： 10.3788/OPE.20111911.2753.

ZHAO Lei, LIU Shu-gui. Implementation of parameter calibration for flexible coordinate measurement machine based on improving genetic algorithm[J]. Editorial Office of Optics and Precision Engineering, 2011,19(11): 2753-2758 DOI： 10.3788/OPE.20111911.2753.

摘要

针对柔性三坐标测量机测量精度低的弊端

提出了误差修正和参数标定的方法。应用Denavit-Hartenberg(DH)法建立了柔性三坐标测量系统的运动学模型和误差模型

考虑系统结构参数标定问题

提出了一种基于优化最小二乘法的改进遗传算法。首先

在最小二乘法中引入变化因子来衡量收敛速度;其次

当该因子趋于稳定时

将产生的次优解作为遗传算法的初始群体

并对一般遗传算法进行改进;最后

根据改进的遗传算法进行搜索、计算

得到满足要求的最优解

完成系统结构参数的标定。实验表明:该方法具有收敛速度快、鲁棒性好等优点。

Abstract

According to the low measuring accuracy from a flexible coordinate measurement machine

the error correction and parameter calibration methods for the coording measarement machine were researched. An improving genetic algorithm based on optimization least square method was proposed to implement the parameter calibration. The kinematic model and the error model of the flexible coordinate measurement machine were established by Denavit-Hartenberg(DH) method. Firstly

a variable factor was used in least square method to evaluate the convergence speed. Then

the suboptimal parameter was regarded as the initial population of optimized genetic algorithm while the variable factor became steady. Finally

the improving genetic algorithm was used to search and calculate to obtain the optimal parameter and the parameter calibration was finished. The experiment shows that the proposed algorithm has fast convergence speeds and good robustness.

关键词

Keywords

references

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