【摘要】：在工業(yè)自動化高速發(fā)展的背景下,越來越多的機器人技術(shù)被運用于工業(yè)作業(yè)中,這當中就包括機器人分揀技術(shù)。傳統(tǒng)的分揀技術(shù)往往使用運動示教方式,對于目標位置要求必須固定,且有著較低的工作效率。將機器視覺與機械臂相結(jié)合,使得機械臂能夠自主地識別目標物體,提高了作業(yè)速度,降低了勞力成本,有著重大的實際意義。機器人視覺技術(shù)使得機器人具備了視覺感知能力,如今也是當今工業(yè)自動化的重點研究領(lǐng)域。目前在工業(yè)上被廣泛用于缺陷檢測、物流、碼垛、焊接等眾多領(lǐng)域。本文以puma560機械臂為研究本體,對于基于位置的視覺伺服控制方法展開研究,設(shè)計了一個視覺分揀系統(tǒng)。將目標工件圖像處理,工件的匹配識別,相機標定,視覺控制器的設(shè)計為主線貫穿全文,最終實現(xiàn)了機械臂對于靜止目標和運動目標追蹤。本文重點針對以下幾點展開了研究:進行了分揀目標圖像處理算法的研究和設(shè)計,通過實驗對比選取了最佳的圖像處理方案,去除了目標二值圖像的噪聲,得到了滿意的目標二值圖。Otsu算法是圖像分割中最常用的方法之一,針對傳統(tǒng)Otsu算法需遍歷每個灰度值進行類間方差的計算,本文提出一種快速的Otsu改進算法,并與傳統(tǒng)算法進行了比較,驗證了新算法的有效性。對于分揀目標進行Harris角點特征提取,得到了模版和原圖像的角點特征圖像。并使用歸一化互相關(guān)(NCC)策略對原圖和模版圖進行了粗匹配,得到了包含誤匹配的匹配結(jié)果。最終通過使用RANSAC策略去除了誤匹配,得到了提純后正確的匹配結(jié)果。進行了視覺系統(tǒng)的標定研究,分別對于相機標定和手眼標定方法進行了理論研究與標定實驗。計算出了相機的內(nèi)外參數(shù),并給出了手眼矩陣求解一般方法。對于puma560進行了運動學(xué)的求解,研究了基于位置的視覺伺服的控制方法。在matlab/simulink下進行了視覺伺服的控制模型搭建,并分別實現(xiàn)了對靜止目標定位以及運動目標追蹤的仿真研究。通過上述的研究工作表明,本文提出的視覺系統(tǒng)方案對于目標工件識別定位精度高,對于直線運動目標追蹤效果好,證明了本文系統(tǒng)具有一定的實際意義。
[Abstract]:With the rapid development of industrial automation, more and more robot technologies are used in industrial operations, including robot sorting technology. The traditional sorting technology often uses the motion teaching method, which must be fixed for the target position, and has low working efficiency. It is of great practical significance to combine machine vision with robot arm to identify target objects independently, improve the working speed and reduce the labor cost. Robot vision technology makes robot have visual perception ability, and now it is the key research field of industrial automation. At present, it is widely used in many fields such as defect detection, logistics, palletizing, welding and so on. In this paper, the puma560 manipulator is taken as the research body, and a visual sorting system is designed based on the position based visual servo control method. The image processing, matching recognition, camera calibration and the design of vision controller are the main lines in the paper. Finally, the tracking of the stationary and moving targets by the manipulator is realized. This paper focuses on the following points: the research and design of sorting target image processing algorithm is carried out, the optimal image processing scheme is selected through experimental comparison, and the noise of the target binary image is removed. Otsu algorithm is one of the most commonly used methods in image segmentation. In view of the traditional Otsu algorithm needs to traverse each gray value to calculate the inter-class variance, a fast Otsu improved algorithm is proposed in this paper. Compared with the traditional algorithm, the validity of the new algorithm is verified. The corner feature images of template and original image are obtained by Harris corner feature extraction for sorting target. The normalized cross-correlation (NCC) strategy is used to match the original image and template map, and a matching result containing mismatch is obtained. Finally, the mismatch is removed by using RANSAC strategy, and the correct matching result is obtained after purification. The calibration of visual system is studied, and the methods of camera calibration and hand-eye calibration are studied and calibrated. The internal and external parameters of the camera are calculated, and the general method of hand-eye matrix solution is given. The kinematics of puma560 is solved, and the control method of visual servo based on position is studied. The control model of visual servo is built under matlab/simulink, and the simulation research of static target location and moving target tracking is realized respectively. The above research results show that the proposed vision system has high accuracy for target recognition and location, and good tracking effect for linear moving target, which proves that the proposed vision system has certain practical significance.
【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP241

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