本文選題：焊接機器人 + 機器視覺��； 參考：《華南理工大學》2015年碩士論文

【摘要】：由于人工焊接存在工作環(huán)境惡劣、勞動強度大、效率低、焊接質(zhì)量得不到保證等問題,當前焊接機器人已經(jīng)在許多工業(yè)領(lǐng)域得到了應用。然而焊接機器人一般采用示教再現(xiàn)方式工作,為確保這種工作方式能在具體焊接環(huán)境中實施,需要解決兩個關(guān)鍵問題:第一正確識別焊接工件以確定示教程序。第二對前工序中由人工點焊定位導致的定位誤差進行自動補償。這兩個問題是焊接機器人應用的突出技術(shù)難點,成為制約焊接機器人技術(shù)推廣應用的瓶頸。為此,本課題采用機器視覺技術(shù),以解決焊接工件識別和焊接軌跡自動校正的問題。本課題的研究獲得了廣東省科技計劃項目(編號:2011A091101001,工業(yè)機器人核心技術(shù)研究及典型產(chǎn)品產(chǎn)業(yè)化)和企業(yè)橫向項目(集裝箱后端生產(chǎn)線全自動裝配和焊接機器人應用,南方中集東部物流裝備制造有限公司)的資助。本文研究了手眼系統(tǒng)的標定方法。針對Eye-to-Hand和Eye-in-Hand兩種不同的焊接機器人和視覺系統(tǒng)安裝方式,分別采用不同的手眼系統(tǒng)標定方法。獲得攝像機的內(nèi)外參數(shù),計算攝像機坐標系、機器人末端坐標系及世界坐標系之間的轉(zhuǎn)換矩陣,從而實現(xiàn)圖像坐標系和世界坐標系的轉(zhuǎn)換。本文研究了圖像預處理技術(shù),包括灰度轉(zhuǎn)換、濾波去噪、閾值處理、形態(tài)學運算及邊緣檢測。通過對圖像預處理,濾去噪聲,增強目標信息,使其具有一定的魯棒性。在此基礎上,對焊接工件的特征進行了分析和提取,結(jié)合這些特征,設計和訓練了高斯混合模型分類器、多層感知神經(jīng)網(wǎng)絡分類器及支持向量機分類器,并對特征向量和分類器參數(shù)進行了優(yōu)化,最終確定最優(yōu)的特征向量和分類器參數(shù),以實現(xiàn)焊接工件的準確識別。本文在傳統(tǒng)模板匹配技術(shù)基礎上提出了基于幾何形狀的金字塔分層匹配算法,提取圖像幾何特征,并對幾何特征進行了分層。計算灰度區(qū)域的圖像質(zhì)心,對焊接工件進行定位。以像素點的平移矩陣和旋轉(zhuǎn)矩陣為基礎,根據(jù)模板匹配檢測到的偏移量和旋轉(zhuǎn)角度,計算出實際軌跡,從而校正焊接軌跡。本文設計了基于機器視覺的焊接機器人實驗平臺,并進行了焊接工件的分類、檢測、識別定位和焊接軌跡校正實驗。實驗結(jié)果表明上述的理論和算法都能滿足焊接機器人對時間和精度的要求。本課題研究的成果目前已用于南方中集東部物流裝備制造有限公司的集裝箱后端鎖座和鉸鏈的焊接,不僅焊縫質(zhì)量良好,而且能滿足焊接生產(chǎn)線對時序的要求。
[Abstract]:Due to the problems of poor working environment, high labor intensity, low efficiency and unguaranteed welding quality in manual welding, welding robots have been applied in many industrial fields. However, welding robots generally work in teaching and reproducing mode. In order to ensure that the working mode can be implemented in a specific welding environment, two key problems need to be solved: first, the welding workpiece is correctly identified to determine the teaching procedure. Second, the positioning error caused by manual spot welding in the former procedure is automatically compensated. These two problems are the prominent technical difficulties in the application of welding robot and become the bottleneck restricting the application of welding robot technology. Therefore, machine vision technology is used to solve the problem of welding workpiece identification and automatic correction of welding track. The research of this subject has obtained the project of Guangdong province science and technology plan (number: 2011A091101001, industrial robot core technology research and typical product industrialization) and enterprise horizontal project (automatic assembly and welding robot application of container back-end production line), Southern Zhongji East Logistics equipment Manufacturing Co., Ltd. The calibration method of hand-eye system is studied in this paper. Aiming at two different installation modes of welding robot and vision system, Eye-to-Hand and Eye-in-Hand, different calibration methods of hand-eye system are adopted. The internal and external parameters of the camera are obtained, and the transformation matrix between the camera coordinate system, the robot terminal coordinate system and the world coordinate system is calculated, and the transformation between the image coordinate system and the world coordinate system is realized. In this paper, image preprocessing techniques including gray conversion, filtering and denoising, threshold processing, morphological operation and edge detection are studied. By image preprocessing, noise is filtered, and target information is enhanced to make it robust. On this basis, the features of welded workpieces are analyzed and extracted. Combined with these features, Gao Si hybrid model classifier, multi-layer perceptual neural network classifier and support vector machine classifier are designed and trained. The eigenvector and classifier parameters are optimized and the optimal eigenvector and classifier parameters are finally determined in order to realize the accurate identification of the welded workpiece. In this paper, based on the traditional template matching technology, a pyramid hierarchical matching algorithm based on geometric shape is proposed, which extracts the geometric features of images and delaminate the geometric features. The image centroid of gray area is calculated and the welding workpiece is located. Based on the translation matrix and rotation matrix of pixels, the actual track is calculated according to the offset and rotation angle detected by template matching, and the welding trajectory is corrected. In this paper, a welding robot experimental platform based on machine vision is designed, and the experiments of welding workpiece classification, detection, identification, location and welding trajectory correction are carried out. The experimental results show that the above theory and algorithm can meet the requirements of time and precision of welding robot. The research results of this paper have been applied to the welding of container rear end locking seat and hinge in South Zhongji East Logistics equipment Manufacturing Co., Ltd., which not only have good weld quality, but also meet the requirements of welding production line.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP242;TP391.41

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