發(fā)布時間：2018-06-03 00:21

  本文選題：工業(yè)機器人 + 視覺系統(tǒng)��； 參考：《浙江大學》2016年碩士論文

【摘要】：為了滿足制造業(yè)轉型升級發(fā)展的需求,集成了視覺系統(tǒng)的智能工業(yè)機器人在現(xiàn)代化工廠中被越來越多地使用。為了適應柔性制造中快速部署生產(chǎn)的發(fā)展特點,并滿足精確識別定位物體的作業(yè)需求,本文對工業(yè)機器人的手眼標定和物體識別定位問題進行了探索和研究。本文的研究內容和成果主要包括以下幾個方面：1.設計并實現(xiàn)了一種在線自動化的手眼標定系統(tǒng)。該系統(tǒng)在執(zhí)行標定算法的同時,可以自動的采集標定數(shù)據(jù),基于攝像機成像模型的標定板運動空間規(guī)劃保證了在采集數(shù)據(jù)時標定板出現(xiàn)在攝像機視野范圍內,使系統(tǒng)獲取有效的標定數(shù)據(jù)。手眼標定算法采用線性化算法,保證了在線計算的實時性。同時,設計了有效的系統(tǒng)流程控制標定的開始和結束,保證采集到充足的標定數(shù)據(jù),以消除觀測誤差的影響。實驗證明,本文設計的自動化標定方法可以得到收斂的標定結果,且整個標定過程僅耗時15min。2.提出并實現(xiàn)了基于最小化重投影誤差的手眼標定優(yōu)化算法。該算法將攝像機成像模型和機器人手眼模型作為一個整體進行建模,采用圖像中的棋盤格角點的像坐標作為直接觀測數(shù)據(jù),在像素空間對模型參數(shù)進行優(yōu)化,將手眼變換矩陣的估計誤差轉換為棋盤格角點的重投影誤差,以最小化重投影誤差作為優(yōu)化目標。同時為了求解含有兩部分未知數(shù)的優(yōu)化問題,采用了迭代優(yōu)化方法。實驗證明,該算法可以實現(xiàn)0.873mm的相對標定精度。3.面向工業(yè)應用設計并實現(xiàn)了一種采用ORB(Oriented FAST and Rotated BRIEF)特征的物體識別算法和基于物體局部形狀特征的物體定位算法。物體識別采用基于特征點匹配的方法,選取旋轉不變性和實時性較好的ORB特征。得到特征匹配關系后,使用RANSAC計算感知圖像和模板圖像之間的單應矩陣,完成物體的初步定位。在此基礎上,提出了基于物體局部形狀特征的定位優(yōu)化算法,對物體進行重定位,校正初定位結果。實驗證明,該識別算法可以實現(xiàn)工業(yè)環(huán)境下電路板類物體的快速穩(wěn)定識別,定位優(yōu)化算法可以將相對定位誤差由0.5658mm降到0.1770mm。
[Abstract]:In order to meet the needs of the transformation and upgrading of manufacturing industry, intelligent industrial robots integrated with visual systems have been used more and more in modern chemical plants. In order to adapt to the development characteristics of rapid deployment of production in flexible manufacturing and to meet the operational requirements of accurate identification of positioning objects, this paper explores and studies the hand-eye calibration and object recognition and positioning of industrial robots. The research contents and achievements of this paper mainly include the following several aspects: 1. An on-line automatic hand-eye calibration system is designed and implemented. The system can automatically collect calibration data while performing calibration algorithm. The moving space planning of calibration board based on camera imaging model ensures that the calibration board appears in the camera field of vision when the data is collected. The system can obtain effective calibration data. Hand-eye calibration algorithm uses linearization algorithm to ensure the real-time of online computing. At the same time, an effective system flow control is designed to control the start and end of calibration to ensure that sufficient calibration data are collected to eliminate the influence of observation errors. Experimental results show that the proposed automatic calibration method can obtain convergent calibration results, and the whole calibration process takes only 15 min. 2. An optimal hand-eye calibration algorithm based on minimizing reprojection error is proposed and implemented. In this algorithm, the camera imaging model and the robot hand-eye model are modeled as a whole, and the image coordinates of the chessboard corner point in the image are used as the direct observation data to optimize the model parameters in the pixel space. The estimation error of the hand-eye transformation matrix is transformed into the reprojection error of the chessboard grid corner, and the optimization objective is to minimize the reprojection error. At the same time, an iterative optimization method is used to solve the optimization problem with two parts unknown numbers. Experiments show that the algorithm can achieve the relative calibration accuracy of 0.873mm. An object recognition algorithm based on ORB(Oriented FAST and Rotated BRIEF) feature and an object location algorithm based on local shape feature are designed and implemented for industrial applications. Object recognition is based on feature point matching, and ORB features with rotation invariance and good real-time performance are selected. After the feature matching relationship is obtained, the monoclinic matrix between the perceptual image and the template image is calculated by using RANSAC to complete the initial location of the object. On this basis, an optimization algorithm based on the local shape features of the object is proposed to relocate the object and correct the initial location results. Experiments show that the algorithm can realize fast and stable recognition of PCB objects in industrial environment, and the location optimization algorithm can reduce the relative positioning error from 0.5658mm to 0.1770 mm.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41;TP242

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本文編號：1970735