本文關(guān)鍵詞： 視覺引導(dǎo) 工業(yè)機器人 目標識別 目標定位 分揀　出處：《陜西科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：智能制造已成為生產(chǎn)制造業(yè)的發(fā)展方向,其核心是工業(yè)機器人。在工業(yè)生產(chǎn)中應(yīng)用工業(yè)機器人可以提高產(chǎn)品的產(chǎn)量與質(zhì)量并降低生產(chǎn)成本。為工業(yè)機器人配備視覺引導(dǎo)系統(tǒng),可以提高機器人對外界環(huán)境的感知和適應(yīng)能力,對實現(xiàn)制造業(yè)的智能化有非常重要的意義。本文以分揀系統(tǒng)為背景,研究了視覺引導(dǎo)技術(shù)在工業(yè)機器人中的應(yīng)用,具體的工作可分為以下三個部分:(1)基于輪廓匹配的目標識別和定位方法研究。首先利用基于邊緣的方法將圖像分割成相互獨立的小塊圖像,然后提取圖像中目標物體的輪廓特征,分別計算目標物體輪廓圖像和模板輪廓圖像的幾何不變矩(Hu矩)和兩個輪廓之間的對比度量值,最后根據(jù)度量值閾值去除錯誤的匹配結(jié)果。利用圖像的標準矩計算出目標物體的中心像素坐標,分別利用目標圖像和模板圖像的中心坐標和重心坐標構(gòu)成向量,計算兩個向量之間的夾角,并根據(jù)實際系統(tǒng)對夾角做一定的偏移,得到目標物體相對于模板圖像的旋轉(zhuǎn)角度。(2)基于特征點匹配的目標識別和定位方法研究。首先利用基于邊緣的方法將圖像分割成相互獨立的小塊圖像,然后檢測目標物體和模板圖像上的特征點,分別計算對應(yīng)特征點之間的漢明距離,并根據(jù)閾值判斷特征點是否匹配,最后根據(jù)最佳特征點的個數(shù)去除掉錯誤的匹配結(jié)果。分別利用目標圖像和模板圖像的中心坐標以及任意的兩個特征點坐標構(gòu)成的三角形,計算出目標物體的像素坐標,并利用任意兩個特征點構(gòu)成的特征點向量,計算兩個向量之間的夾角,并做一定的偏移,得到目標物體相對于模板圖像的旋轉(zhuǎn)角度。(3)基于視覺引導(dǎo)的分揀系統(tǒng)設(shè)計。系統(tǒng)以簡單工件和象棋棋子作為分揀對象,首先利用工業(yè)數(shù)字相機獲取工作區(qū)域的圖像,在工業(yè)控制計算機上利用C++編寫視覺處理程序?qū)崿F(xiàn)目標的識別和定位,并采用以太網(wǎng)通信將目標信息數(shù)據(jù)發(fā)送到工業(yè)機器人,然后利用MELFA-BASIC語言編寫機器人程序?qū)崿F(xiàn)數(shù)據(jù)的解析及對工業(yè)機器人的控制,最后采用三菱RV-13F六自由度工業(yè)機器人作為主體,氣動吸嘴作為末端執(zhí)行器抓取目標物體,實現(xiàn)自動分揀。為了驗證目標識別算法的有效性,進行了目標識別與定位實驗,與利用示教器獲取的目標實測世界坐標進行比較,利用絕對誤差分析識別定位結(jié)果的準確性。為了檢驗分揀系統(tǒng)的性能,進行了物體的抓取與分揀實驗。實驗結(jié)果表明,計算坐標與實測世界坐標之間的誤差在0.6mm以內(nèi),可以準確的抓取到目標物體,同時利用計算出的旋轉(zhuǎn)角度可以將圖像區(qū)域中的所有目標物體按照模板圖像的方向整齊擺放,實現(xiàn)了物體的自動分揀。
[Abstract]:Intelligent manufacturing has become the development direction of manufacturing industry. The application of industrial robots in industrial production can improve the output and quality of products and reduce the cost of production. The industrial robots are equipped with visual guidance system. It can improve the perception and adaptability of the robot to the outside environment, and it is very important to realize the intelligence of manufacturing industry. This paper takes the sorting system as the background. The application of visual guidance technology in industrial robot is studied. The specific work can be divided into the following three parts: 1) Target recognition and localization based on contour matching. Firstly, the edge based method is used to segment the image into independent blocks. Then the contour features of the target object in the image are extracted, and the geometric invariant moments Hu moments of the contour image and the template contour image are calculated, respectively, and the contrast measures between the two contours are calculated. Finally, according to the measure threshold to remove the wrong matching results, the center pixel coordinates of the target object are calculated by using the standard moments of the image, and the center coordinates and the barycentric coordinates of the target image and the template image are used to form the vector respectively. The angle between the two vectors is calculated, and a certain deviation of the angle is made according to the actual system. The rotation angle of the target object relative to the template image is obtained. (2) the method of target recognition and location based on feature point matching is studied. Firstly, the image is segmented into independent small images by edge based method. Then the feature points of the target object and the template image are detected, the hamming distance between the corresponding feature points is calculated, and the matching of the feature points is judged according to the threshold value. Finally, according to the optimal number of feature points to remove the wrong matching results, respectively using the target image and template image center coordinates and arbitrary two feature point coordinates of the triangle. The pixel coordinates of the target object are calculated, and the angle between the two vectors is calculated by using the eigenpoint vector of any two feature points, and a certain deviation is made. The rotation angle of the target object relative to the template image is obtained.) the visual guided sorting system is designed. The system takes the simple workpiece and the chess piece as the sorting object. Firstly, the image of the working area is obtained by using the industrial digital camera, and the visual processing program is written on the industrial control computer to realize the recognition and localization of the target. The target information data is sent to the industrial robot by Ethernet communication, and then the robot program is written by MELFA-BASIC language to realize the data analysis and the control of the industrial robot. Finally, Mitsubishi RV-13F six-degree-of-freedom industrial robot is used as the main body, and the pneumatic suction nozzle is used as the end actuator to grab the target object to realize automatic sorting, in order to verify the effectiveness of the target recognition algorithm. The experiments of target recognition and location are carried out, and compared with the measured world coordinates of the target obtained by the teacher, and the accuracy of the identification and location results is analyzed by using absolute error. In order to test the performance of the sorting system. The experimental results show that the error between the calculated coordinates and the measured world coordinates is within 0.6 mm, and the object can be captured accurately. At the same time, all the target objects in the image region can be arranged neatly according to the direction of the template image by using the calculated rotation angle, and the automatic sorting of objects is realized.
【學(xué)位授予單位】：陜西科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP242.2

【參考文獻】

相關(guān)期刊論文 前10條

1 孟明輝;周傳德;陳禮彬;馮淼;苗純正;;工業(yè)機器人的研發(fā)及應(yīng)用綜述[J];上海交通大學(xué)學(xué)報;2016年S1期

2 胡明星;王小蘭;;模板匹配優(yōu)化耦合圖像校正的旋轉(zhuǎn)工件目標定位算法[J];組合機床與自動化加工技術(shù);2016年06期

3 惠記莊;羅麗;楊永奎;劉瓊;;基于SURF-BRISK的目標識別匹配與定位方法研究[J];長安大學(xué)學(xué)報(自然科學(xué)版);2016年03期

4 栗俊艷;米月琴;弓靖;劉鵬飛;;一種基于機器視覺的工業(yè)機器人分揀系統(tǒng)[J];電子科技;2016年01期

5 趙泉華;高郡;李玉;;基于區(qū)域劃分的多特征紋理圖像分割[J];儀器儀表學(xué)報;2015年11期

6 唐宇;吳清瀟;朱楓;;平面工件的識別與定位方法研究[J];機械設(shè)計與制造;2015年10期

7 周濟;;智能制造——“中國制造2025”的主攻方向[J];中國機械工程;2015年17期

8 王帥;徐方;陳亮;鄒風(fēng)山;李邦宇;;視覺技術(shù)在機器人零部件裝配中的應(yīng)用研究[J];組合機床與自動化加工技術(shù);2015年08期

9 倪自強;王田苗;劉達;;基于視覺引導(dǎo)的工業(yè)機器人示教編程系統(tǒng)[J];北京航空航天大學(xué)學(xué)報;2016年03期

10 ;國務(wù)院關(guān)于印發(fā)《中國制造2025》的通知[J];中華人民共和國國務(wù)院公報;2015年16期

相關(guān)碩士學(xué)位論文 前2條

1 熊健;基于視覺引導(dǎo)的搬運機器人多目標識別及抓取姿態(tài)研究[D];湖北工業(yè)大學(xué);2016年

2 郝明;機器視覺在機器人雜亂工件分揀中的應(yīng)用[D];沈陽工業(yè)大學(xué);2015年

，

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