發(fā)布時(shí)間：2018-06-03 00:21

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

【摘要】：為了滿足制造業(yè)轉(zhuǎn)型升級(jí)發(fā)展的需求,集成了視覺(jué)系統(tǒng)的智能工業(yè)機(jī)器人在現(xiàn)代化工廠中被越來(lái)越多地使用。為了適應(yīng)柔性制造中快速部署生產(chǎn)的發(fā)展特點(diǎn),并滿足精確識(shí)別定位物體的作業(yè)需求,本文對(duì)工業(yè)機(jī)器人的手眼標(biāo)定和物體識(shí)別定位問(wèn)題進(jìn)行了探索和研究。本文的研究?jī)?nèi)容和成果主要包括以下幾個(gè)方面：1.設(shè)計(jì)并實(shí)現(xiàn)了一種在線自動(dòng)化的手眼標(biāo)定系統(tǒng)。該系統(tǒng)在執(zhí)行標(biāo)定算法的同時(shí),可以自動(dòng)的采集標(biāo)定數(shù)據(jù),基于攝像機(jī)成像模型的標(biāo)定板運(yùn)動(dòng)空間規(guī)劃保證了在采集數(shù)據(jù)時(shí)標(biāo)定板出現(xiàn)在攝像機(jī)視野范圍內(nèi),使系統(tǒng)獲取有效的標(biāo)定數(shù)據(jù)。手眼標(biāo)定算法采用線性化算法,保證了在線計(jì)算的實(shí)時(shí)性。同時(shí),設(shè)計(jì)了有效的系統(tǒng)流程控制標(biāo)定的開(kāi)始和結(jié)束,保證采集到充足的標(biāo)定數(shù)據(jù),以消除觀測(cè)誤差的影響。實(shí)驗(yàn)證明,本文設(shè)計(jì)的自動(dòng)化標(biāo)定方法可以得到收斂的標(biāo)定結(jié)果,且整個(gè)標(biāo)定過(guò)程僅耗時(shí)15min。2.提出并實(shí)現(xiàn)了基于最小化重投影誤差的手眼標(biāo)定優(yōu)化算法。該算法將攝像機(jī)成像模型和機(jī)器人手眼模型作為一個(gè)整體進(jìn)行建模,采用圖像中的棋盤(pán)格角點(diǎn)的像坐標(biāo)作為直接觀測(cè)數(shù)據(jù),在像素空間對(duì)模型參數(shù)進(jìn)行優(yōu)化,將手眼變換矩陣的估計(jì)誤差轉(zhuǎn)換為棋盤(pán)格角點(diǎn)的重投影誤差,以最小化重投影誤差作為優(yōu)化目標(biāo)。同時(shí)為了求解含有兩部分未知數(shù)的優(yōu)化問(wèn)題,采用了迭代優(yōu)化方法。實(shí)驗(yàn)證明,該算法可以實(shí)現(xiàn)0.873mm的相對(duì)標(biāo)定精度。3.面向工業(yè)應(yīng)用設(shè)計(jì)并實(shí)現(xiàn)了一種采用ORB(Oriented FAST and Rotated BRIEF)特征的物體識(shí)別算法和基于物體局部形狀特征的物體定位算法。物體識(shí)別采用基于特征點(diǎn)匹配的方法,選取旋轉(zhuǎn)不變性和實(shí)時(shí)性較好的ORB特征。得到特征匹配關(guān)系后,使用RANSAC計(jì)算感知圖像和模板圖像之間的單應(yīng)矩陣,完成物體的初步定位。在此基礎(chǔ)上,提出了基于物體局部形狀特征的定位優(yōu)化算法,對(duì)物體進(jìn)行重定位,校正初定位結(jié)果。實(shí)驗(yàn)證明,該識(shí)別算法可以實(shí)現(xiàn)工業(yè)環(huán)境下電路板類(lèi)物體的快速穩(wěn)定識(shí)別,定位優(yōu)化算法可以將相對(duì)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(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41;TP242

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本文編號(hào)：1970735