本文選題：AGV避障 + 深度攝像��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：自動(dòng)導(dǎo)航運(yùn)輸車是一類通過傳感器探測地形,實(shí)現(xiàn)在物理空間中定向移動(dòng)的自動(dòng)機(jī)器人車輛。AGV避障為該領(lǐng)域的研究重點(diǎn)之一,其主要內(nèi)容是在實(shí)際的生產(chǎn)環(huán)境中,AGV能夠按照特定的避障策略,保證車輛能夠無碰撞地從起點(diǎn)運(yùn)行到終點(diǎn)。AGV避障方法中的一個(gè)核心技術(shù)就是障礙物識別:探測和分析空間環(huán)境,定位環(huán)境中的障礙物信息。環(huán)境探測是AGV進(jìn)行障礙物識別的前提條件,機(jī)器人可以通過計(jì)算機(jī)視覺來獲取實(shí)時(shí)環(huán)境數(shù)據(jù)。本論文采用Kienct作為主傳感器獲取深度圖,再運(yùn)用梯度檢測和DBSCAN聚類算法分析環(huán)境中的障礙物,然后將分類后的障礙信息映射到柵格圖中,最后采用D*尋路算法完成AGV的路徑規(guī)劃。本論文的主要內(nèi)容為:1.根據(jù)AGV車輛生產(chǎn)環(huán)境的特殊性,采用ARM架構(gòu)的樹莓派作為AGV的主控制器。由于Kinect攝像頭缺乏在ARM平臺上的設(shè)備驅(qū)動(dòng),先自主編寫完成ARM平臺上的Kinect設(shè)備驅(qū)動(dòng)。2.引入梯度檢測算法作為地平面檢測手段,結(jié)合深度圖梯度檢測算法和DBSCAN聚類算法,快速有效地解析深度數(shù)據(jù)。首先闡述了Kienct和其生成深度圖的原理,再提出了深度圖像的梯度概念,以地平面和物體間的三維空間關(guān)系作為參考信息,運(yùn)用梯度檢測算法快速區(qū)分可行駛區(qū)域和障礙。3.設(shè)計(jì)了基于Kinect深度攝像頭和ARM架構(gòu)樹莓派主控制器的AGV系統(tǒng)的整體框架。AGV利用Kienct探測運(yùn)行環(huán)境,獲取物體分布數(shù)據(jù)生成深度圖。并利用經(jīng)梯度檢測和聚類算法處理后的深度圖,生成描述AGV與障礙間位置關(guān)系的柵格圖,最后運(yùn)用啟發(fā)式的D*路徑規(guī)劃方法的輔助下,進(jìn)行AGV的動(dòng)態(tài)路徑規(guī)劃。
[Abstract]:Automatic navigation vehicle is a kind of automatic robot vehicle. AGV, which detects terrain through sensors and realizes directional movement in physical space, is one of the key research points in this field. The main content is that in the actual production environment, AGV can follow a specific obstacle avoidance strategy, and one of the core technologies in the collision free vehicle running from the starting point to the end. AGV obstacle avoidance method is obstacle identification: detecting and analyzing space environment. Locate obstacle information in the environment. Environment detection is the precondition of obstacle recognition for AGV. Robot can acquire real-time environment data by computer vision. In this paper, Kienct is used as the main sensor to obtain the depth map, then gradient detection and DBSCAN clustering algorithm are used to analyze the obstacles in the environment, and then the classified obstacle information is mapped to the grid map. Finally, the path planning of AGV is completed by using the D * path finding algorithm. The main content of this thesis is 1: 1. According to the particularity of AGV vehicle production environment, raspberry pie based on arm is used as the main controller of AGV. Because Kinect camera lacks the device driver on arm platform, we write the Kinect device driver on arm platform. 2. The gradient detection algorithm is introduced as a ground plane detection method, combining with the depth map gradient detection algorithm and DBSCAN clustering algorithm, the depth data can be analyzed quickly and effectively. Firstly, the principle of Kienct and its depth map generation is expounded, then the concept of gradient of depth image is proposed. The 3D spatial relationship between ground plane and object is taken as the reference information, and the gradient detection algorithm is used to quickly distinguish the tradable area from the obstacle. 3. The whole framework of AGV system based on Kinect depth camera and arm raspberry master controller is designed. The AGV uses Kienct to detect the running environment to obtain the depth map of object distribution data. Using the depth map processed by gradient detection and clustering algorithm, the grid map describing the relationship between AGV and obstacle position is generated. Finally, the dynamic path planning of AGV is carried out with the aid of heuristic D* path planning method.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242

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