RPY modeling and error calibration of flexible measuring arm

ZHAO Lei; ZHAO Xin-hua; WANG Shou-jun; GE Wei-min

doi:10.3788/OPE.20162402.0365

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RPY modeling and error calibration of flexible measuring arm

更新时间：2020-08-13

- RPY modeling and error calibration of flexible measuring arm
- Optics and Precision Engineering Vol. 24, Issue 2, Pages: 365-371(2016)
- 作者机构：
  
  1. 天津市先进机电系统设计与智能控制重点实验室
  2. 天津理工大学机械工程学院天津,300384
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162402.0365
  CLC： TH721
- Received：20 August 2015，
  
  Revised：15 September 2015，
  
  Published：25 February 2016
- 稿件说明：
移动端阅览
赵磊, 赵新华, 王收军等. 柔性测量臂的RPY建模与误差标定[J]. 光学精密工程, 2016,24(2): 365-371

ZHAO Lei, ZHAO Xin-hua, WANG Shou-jun etc. RPY modeling and error calibration of flexible measuring arm[J]. Editorial Office of Optics and Precision Engineering, 2016,24(2): 365-371
赵磊, 赵新华, 王收军等. 柔性测量臂的RPY建模与误差标定[J]. 光学精密工程, 2016,24(2): 365-371 DOI： 10.3788/OPE.20162402.0365.

ZHAO Lei, ZHAO Xin-hua, WANG Shou-jun etc. RPY modeling and error calibration of flexible measuring arm[J]. Editorial Office of Optics and Precision Engineering, 2016,24(2): 365-371 DOI： 10.3788/OPE.20162402.0365.

摘要

为了提高1P3R型柔性测量臂的检测精度

提出了一种自适应误差标定方法来完成它的参数标定和误差补偿。首先

基于RPY理论建立柔性测量臂的运动学模型并应用微分法推导了误差模型。然后

基于马尔柯夫链收敛性理论进行实数编码;在遗传算法(GA)中引入自适应控制算子

提高了父代种群的多样性和最优个体变异数量;最后

对比分析了归一化GA、一般GA和最小二乘法等3种算法的误差标定精度及其收敛性

验证了所提出方法的有效性和可行性。结果显示:归一化GA仅用328代寻优计算

其精度达到5.2m

收敛速度是一般GA的2.3倍且精度提高了3.1m;最小二乘法经20次迭代计算后停止收敛

精度仅为18.4m。实验结果表明:归一化GA具有收敛速度快、标定精度高和收敛稳定性好等明显优势

更易于实现该类测量机的高精度检测。

Abstract

An adaptive error calibration method was proposed to complete parameter calibration and error compensation to realize the high accuracy test for a 1P3R flexible measuring arm. Firstly

a kinematic model of 1P3R flexible measuring arm was built based on RPY theory and a differential method was used to deduce the error model of system. Then

the real number encoding was performed based on the theory of Markova chain convergence. Adaptive control factors were added to Genetic Algorithm(GA) to improve population diversity and the quantity of the best individual. Finally

the calibration accuracy and convergence performance of a normalized GA

a general GA and least square method were compared and analyzed and the results were used to verify the feasibility and effectiveness of the proposed method. It shows that the accuracy of the normalized GA has been enhanced from 3.1m to 5.2m just after 328 generation searching

and its convergence velocity is 2.3 times that of the general GA. Moreover

the convergence of least square method is stopped after 20 generations and its calibration accuracy is only 18.4m. Experiments indicate that normalized GA is characterized by some advantages of fast convergence speed

high calibration accuracy and better stability. It is suitable for measuring these kinds of flexible measuring arms in high accuracy.

关键词

Keywords

references

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