發(fā)布時間：2018-03-04 15:36

  本文選題：智能配送單元　切入點：機器視覺　出處：《合肥工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著工業(yè)4.0概念的提出,智能制造成為今后制造業(yè)的發(fā)展趨勢,它要求工廠、生產(chǎn)和物流等均實現(xiàn)智能化,而其中智能物流是實現(xiàn)智能制造的核心目標之一。本課題正是在這種發(fā)展趨勢下,提出以AGV(Automated Guided Vehicle,自動導(dǎo)引車)為基礎(chǔ),開發(fā)一種智能配送單元(Intelligent Distribution Unit),用于智能工廠中的物料配送等環(huán)節(jié)中。隨著近年來圖像處理技術(shù)的進步和計算機視覺技術(shù)的發(fā)展,以及低功耗高集成度的嵌入式處理器研發(fā)技術(shù)的進步,基于嵌入式Linux系統(tǒng)的視覺導(dǎo)航方式具有非常廣闊的研究和應(yīng)用前景�；跈C器視覺的智能配送單元系統(tǒng)設(shè)計的研究內(nèi)容主要涉及兩個方面:圖像的采集與處理和控制器設(shè)計。圖像的采集與處理方面,本文建立了一個典型的圖像采集系統(tǒng),并對機載攝像頭的安裝位置以及路徑標示線的設(shè)置進行了討論;對于采集后的圖像,在經(jīng)過灰度化和二值化處理后,采用形態(tài)學(xué)處理進行邊緣提取,然后利用逆透視變換將導(dǎo)引軌跡邊緣線映射為世界坐標系中的直線,最終利用霍夫變換得到邊緣線的直線方程并提取出導(dǎo)航偏差參數(shù)�？刂破髟O(shè)計方面,首先以車載視覺傳感器所采集圖像中智能配送單元的控制中心特征與導(dǎo)引軌跡的期望特征之間的偏差為依據(jù),建立智能配送單元運動學(xué)模型,然后采用改進的滑膜變結(jié)構(gòu)控制方法,并通過基于遺傳算法和非線性規(guī)劃的函數(shù)尋優(yōu)算法對控制器參數(shù)進行優(yōu)化。最后,通過在MATLAB/Simulink上進行兩組對比仿真試驗,分別是改進滑模變結(jié)構(gòu)控制仿真和參數(shù)優(yōu)化的滑模變結(jié)構(gòu)控制仿真。經(jīng)過對相關(guān)實驗結(jié)果與數(shù)據(jù)進行分析、對比驗證本課題中導(dǎo)航方式的可行性和導(dǎo)航算法的有效性。
[Abstract]:With the development of the concept of industry 4.0, intelligent manufacturing has become the development trend of manufacturing industry in the future. It requires factories, production and logistics to be intelligent. Among them, intelligent logistics is one of the core goals to realize intelligent manufacturing. This topic is based on AGV(Automated Guided vehicle. An intelligent Distribution unit is developed for material distribution in intelligent factories. With the progress of image processing technology and the development of computer vision technology in recent years, And the development of low power and high integration embedded processor technology, The visual navigation mode based on embedded Linux system has a very broad research and application prospect. The design of intelligent distribution unit system based on machine vision mainly involves two aspects: image acquisition and processing; Controller design. Image acquisition and processing, In this paper, a typical image acquisition system is established, and the installation position of the airborne camera and the setting of the path marking line are discussed. The edge of the guided trajectory is mapped to a straight line in the world coordinate system by using the inverse perspective transformation. Finally, the linear equation of the edge line is obtained by using the Hough transform and the navigation deviation parameters are extracted. Firstly, based on the deviation between the control center feature of intelligent distribution unit and the expected feature of guidance track, the kinematics model of intelligent distribution unit is established. Then the improved synovial variable structure control method is adopted, and the controller parameters are optimized by the function optimization algorithm based on genetic algorithm and nonlinear programming. Finally, two sets of comparative simulation experiments are carried out on MATLAB/Simulink. The simulation of improved sliding mode variable structure control and parameter optimization of sliding mode variable structure control is presented respectively. The feasibility of navigation mode and the effectiveness of navigation algorithm are compared and verified by analyzing the relevant experimental results and data.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;F252.1

【參考文獻】

相關(guān)期刊論文 前10條

1 黃一鈞;;車身車間AGV物料搬運系統(tǒng)小車數(shù)量配置規(guī)劃[J];工業(yè)工程與管理;2015年04期

2 寧志民;;AGV技術(shù)發(fā)展綜述[J];導(dǎo)航與控制;2014年05期

3 劉敏偉;;淺談激光導(dǎo)引AGV系統(tǒng)原理與應(yīng)用[J];科技創(chuàng)新與應(yīng)用;2014年19期

4 武啟平;金亞萍;任平;查振元;;自動導(dǎo)引車(AGV)關(guān)鍵技術(shù)現(xiàn)狀及其發(fā)展趨勢[J];制造業(yè)自動化;2013年10期

5 崔濤;楊永樂;;基于改進的二維OTSU算法的圖像二值化分割[J];科技致富向?qū)?2013年02期

6 吳雄喜;;AGV自主導(dǎo)引機器人應(yīng)用現(xiàn)狀及發(fā)展趨勢[J];機器人技術(shù)與應(yīng)用;2012年03期

7 陸勤;寧宗奇;劉建華;;AGV小車軌跡提取方法[J];信息與電腦(理論版);2011年11期

8 梁金明;魏正曦;;Ostu算法的改進研究[J];四川理工學(xué)院學(xué)報(自然科學(xué)版);2010年05期

9 劉進;齊曉慧;李永科;;基于機器視覺的AGV路徑跟蹤[J];火力與指揮控制;2010年08期

10 高德芝;鄭榜貴;段建民;;基于逆透視變換的智能車輛定位技術(shù)[J];計算機測量與控制;2009年09期

，

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