本文關(guān)鍵詞：大型復(fù)雜曲面三維測量的立體視覺規(guī)劃研究　出處：《哈爾濱理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 大型復(fù)雜曲面 三維測量 多目標優(yōu)化 遺傳算法 立體視覺規(guī)劃

【摘要】：現(xiàn)在大型裝備制造業(yè)對產(chǎn)品精度與生產(chǎn)效率的要求越來越高,三維外形檢測技術(shù)作為質(zhì)量控制的有效手段,正逐漸在生產(chǎn)制造過程中起到越來越重要的作用。立體視覺測量對大型零部件三維外形檢測具有測量精度高、靈活性好的特點,獲得廣泛的認可。但是,在大型復(fù)雜曲面零部件測量過程中,傳統(tǒng)手持式雙目立體視覺測量裝置往往存在局部漏檢、精度低以及工作效率低等問題,因此本文設(shè)計了一套基于雙目立體視覺的全自動三維測量裝置,并深入研究了三維測量中的立體視覺規(guī)劃問題,建立了相應(yīng)的規(guī)劃算法。首先,根據(jù)對雙目立體視覺測量過程的研究,確定了視覺測量裝置的結(jié)構(gòu)方案和硬件配置,并開發(fā)了測量裝置的上位機和下位機系統(tǒng),上位機系統(tǒng)主要包含通信參數(shù)設(shè)置、圖像的顯示與采集、路徑及姿態(tài)的輸入與回顯等模塊,下位機系統(tǒng)主要完成數(shù)據(jù)采集及對測量機構(gòu)的運動控制。在給出了視覺測量網(wǎng)絡(luò)的測量覆蓋率與特征分辨率的規(guī)劃目標的基礎(chǔ)上,分析確定了視覺測量網(wǎng)絡(luò)規(guī)劃中的決策變量和約束條件,設(shè)計了視覺測量網(wǎng)絡(luò)規(guī)劃的多目標規(guī)劃數(shù)學(xué)模型,為求解最優(yōu)規(guī)劃Pareto解集奠定基礎(chǔ)。運用多目標遺傳算法對視覺測量網(wǎng)絡(luò)規(guī)劃進行應(yīng)用研究。具體設(shè)計了算法中的編碼方式、種群處理方式、約束條件處理方法、個體適應(yīng)度函數(shù)、遺傳算法及算法的控制參數(shù)。以大型螺旋槳葉片為例,對其進行了測量網(wǎng)絡(luò)規(guī)劃,得到了一組滿足規(guī)劃目標及約束條件的全局Pareto解。并通過實驗驗證了規(guī)劃結(jié)果的可行性。本文最后提出了一種可滿足區(qū)域約束條件的最短路徑規(guī)劃方法,以解決立體視覺測量中的路徑規(guī)劃問題。算法借助區(qū)域內(nèi)尋路與區(qū)域間尋路的兩層搜索實現(xiàn)了最短路徑規(guī)劃,通過與窮舉法的對比,驗證了算法的正確性。本文不但設(shè)計了一種視覺測量裝置,而且為大型復(fù)雜曲面三維測量提供了立體視覺規(guī)劃方法,對提高大型零件的測量精度具有重要意義。
[Abstract]:Nowadays, the requirement of product precision and production efficiency for large-scale equipment manufacturing industry is more and more high, 3D shape detection technology as an effective means of quality control. Stereo vision measurement has the characteristics of high measurement precision, good flexibility, and has been widely recognized. In the process of measuring large and complex curved surface parts, the traditional hand-held binocular stereo vision measurement device often has some problems, such as local missing detection, low precision and low working efficiency. Therefore, this paper designs a set of automatic 3D measurement device based on binocular stereo vision, and deeply studies the stereo vision planning problem in 3D measurement, and establishes the corresponding planning algorithm. According to the research of binocular stereo vision measurement process, the structure and hardware configuration of the vision measuring device are determined, and the upper and lower computer systems of the measuring device are developed. The upper computer system mainly includes communication parameter setting, image display and acquisition, path and attitude input and echo module. The lower computer system mainly completes the data acquisition and the motion control of the measuring mechanism. The planning target of the measurement coverage and feature resolution of the vision measurement network is given. The decision variables and constraints in vision measurement network planning are analyzed and the multi-objective programming mathematical model of vision measurement network planning is designed. In order to solve the optimal programming Pareto solution set, the application of multi-objective genetic algorithm to the vision measurement network planning is studied. The coding method and population processing method of the algorithm are designed in detail. The control parameters of constraint condition processing method, individual fitness function, genetic algorithm and algorithm. Taking the large propeller blade as an example, the measurement network planning is carried out. A set of global Pareto solutions satisfying the planning objectives and constraints are obtained. The feasibility of the planning results is verified by experiments. At last, a shortest path planning method is proposed to satisfy the regional constraints. Law. In order to solve the problem of path planning in stereo vision measurement, the algorithm realizes the shortest path planning with the help of the two-layer search between the region and the region. The algorithm is compared with the exhaustive method. In this paper, not only a vision measuring device is designed, but also a stereo vision planning method is provided for the 3D measurement of large complex surfaces, which is of great significance to improve the measuring accuracy of large parts.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TG806

【參考文獻】

相關(guān)期刊論文 前10條

1 張曉龍;尹仕斌;任永杰;郭寅;楊凌輝;王一;;基于全局空間控制的高精度柔性視覺測量系統(tǒng)研究[J];紅外與激光工程;2015年09期

2 符純明;姜潮;劉桂萍;鄧善良;;基于網(wǎng)格支配的微型多目標遺傳算法[J];中國機械工程;2015年16期

3 孫全穎;王藝霖;杜須韋;;遺傳算法在機械優(yōu)化設(shè)計中的應(yīng)用研究[J];哈爾濱理工大學(xué)學(xué)報;2015年04期

4 王金濤;徐金亭;;復(fù)雜曲面上機器人自動噴涂路徑規(guī)劃方法[J];中國機械工程;2015年14期

5 柯俊;史文庫;錢琛;袁可;李國民;;采用遺傳算法的復(fù)合材料板簧多目標優(yōu)化方法[J];西安交通大學(xué)學(xué)報;2015年08期

6 楊陽;李春;繆維跑;葉舟;;基于多目標遺傳算法的風(fēng)力機葉片全局優(yōu)化設(shè)計[J];機械工程學(xué)報;2015年14期

7 吳登峰;梅志千;尹力偉;李向國;;一種未知環(huán)境下室內(nèi)移動機器人路徑規(guī)劃新算法[J];機電工程;2015年03期

8 秦玉鑫;王紅旗;杜翠杰;;基于雙層A*算法的移動機器人路徑規(guī)劃[J];制造業(yè)自動化;2014年24期

9 隆昌宇;邾繼貴;郭寅;林嘉睿;;移動視覺測量中基于空間交會的匹配方法[J];光學(xué)學(xué)報;2014年08期

10 隆昌宇;邾繼貴;孫博;王偉;林嘉睿;;移動視覺測量中基于編碼網(wǎng)絡(luò)的特征點匹配方法研究[J];光電子.激光;2014年05期

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