本文選題：邦定設備　切入點：運動標定　出處：《現代制造工程》2017年10期

【摘要】：在邦定設備運動標定過程中收集到的對位平臺旋轉中心的樣本數據包含有各種干擾,以至于利用其進行推導時過程復雜、結果精度低且魯棒性不高。為了解決以上問題,提出一種基于隨機抽樣一致性算法的簡單方法,該方法的構建基于樣本數據分布密度模型,該模型進行一定次數的迭代篩選后,其最優(yōu)解集合可進行精確推導。試驗表明,在使用隨機選取的10組樣本數據的計算中,本方法平均計算時間為697.6s,標定精度在7.2pixel以內,并且有7組數據的結果均滿足10pixel的像素精度要求。整個標定結果達到國外同類型設備的先進水平,能夠完全滿足設備運行的要求。
[Abstract]:In order to solve the above problems, the sample data collected during the calibration of the motion of the stationary equipment contain all kinds of interference, so that the derivation process is complicated, the result is low precision and the robustness is not high. A simple method based on random sampling consistency algorithm is proposed. The method is based on sample data distribution density model. After a certain number of iterations, the optimal solution set can be derived accurately. In the calculation of 10 groups of randomly selected sample data, the average calculation time of this method is 697.6 s, and the calibration accuracy is within 7.2pixel. The results of seven groups of data all meet the pixel accuracy requirements of 10pixel, and the calibration results reach the advanced level of the same type of equipment abroad, and can fully meet the requirements of equipment operation.
【作者單位】： 上海交通大學自動化系;昆山工研院新型平板顯示技術中心有限公司;昆山國顯光電有限公司;
【分類號】：O212;TP391.41

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1 徐慶,李朋,徐源,孫濟洲;Alternative Monte Carlo Approach for General Global Illumination[J];Transactions of Tianjin University;2004年04期

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本文編號：1682114