本文選題：深度學(xué)習 + 幾何模型　； 參考：《廣東工業(yè)大學(xué)》2017年碩士論文

【摘要】：機器人利用機器視覺進行物體抓取是機器人應(yīng)用領(lǐng)域的熱門研究之一。目的在于靠機器視覺檢測出被抓取物體的可靠抓取位置和方向,進而通過運動規(guī)劃算法控制機械臂完成抓取動作。但是在這一領(lǐng)域暫未有普適的方法,特別是在需要同時考慮可靠性和安全性的成堆物體抓取應(yīng)用。此論文提出一種利用深度學(xué)習網(wǎng)絡(luò)結(jié)合軟性機械手幾何模型的方法,用于分析處理安裝在機器人中部的單個深度攝像頭所獲取的3維點云數(shù)據(jù),實現(xiàn)在未知的成堆物體中檢測出可供機械臂進行可靠安全抓取的物體位置和方向。該論文所提出的方法在考慮成堆物體抓取碰撞檢測問題的同時,并沒有進行像其他論文所常用的圖像分割和物體識別的處理技術(shù)。特別地,此論文還分析了深度卷積神經(jīng)網(wǎng)絡(luò)的時間和空間復(fù)雜度,并得出兩個結(jié)論用于為深度卷積神經(jīng)網(wǎng)絡(luò)的設(shè)計提供參考。同時,進行多個實驗來得出不同層數(shù)的神經(jīng)網(wǎng)絡(luò)對應(yīng)的訓(xùn)練時間和精度,不同層數(shù)神經(jīng)網(wǎng)絡(luò)進行假設(shè)可抓取超平面再篩選的結(jié)果對比,從而驗證前面由理論推出來的兩個結(jié)論。經(jīng)上述理論及實驗兩方面分析,為深度學(xué)習模型的設(shè)計起到一定的指導(dǎo)性作用。值得一提的是,本論文中深度學(xué)習模型訓(xùn)練數(shù)據(jù)(所有的假設(shè)超平面)的標簽根據(jù)一些標準進行自動標定。也就是說這些用于判斷該假設(shè)超平面是否符合抓取條件的標準被深度學(xué)習網(wǎng)絡(luò)模型所取代。首先,由于軟性機械手相較于硬性機械手更具靈活性,為軟性機械手設(shè)計合適的幾何模型成為避免碰撞的重點,用于在3維點云數(shù)據(jù)空間中搜索符合可抓取條件的抓取點和抓取方向。該幾何模型內(nèi)部必須包含足夠的點云數(shù)據(jù),并且外表面不會與其他點云數(shù)據(jù)有重合的部分,從而分別保證其抓取的可靠性和安全性。利用幾何模型搜索出來符合條件的抓取點和抓取方向,合稱之為假設(shè)可抓取超平面。第二,進一步考慮抓取可靠性,利用深度學(xué)習網(wǎng)絡(luò)Mod-Le Net對搜索出的假設(shè)超平面進行分類和排序,以便找出較為可靠的抓取位置和方向。經(jīng)過Mod-Le Net與支持向量機技術(shù)的對比實驗,通過Mod-Le Net的篩選過后的可抓取超平面的質(zhì)量和可靠性要比通過支持向量機的高,而且數(shù)量也相對較少,也就是說在運動控制方面會比較節(jié)省時間。
[Abstract]:Robot object capture using machine vision is one of the hot research in robot application field. The aim of this paper is to detect the position and direction of the captured object reliably by machine vision, and then to control the robot arm to complete the grab by motion planning algorithm. However, there is no universal method in this field, especially in the application of stacks of objects which need to consider both reliability and safety. In this paper, a method of using depth learning network combined with geometric model of soft manipulator is proposed to analyze and process 3D point cloud data obtained by a single depth camera installed in the middle of the robot. The position and direction of the object which can be reliably and safely grasped by the manipulator can be detected in the unknown stacks of objects. The method proposed in this paper does not deal with image segmentation and object recognition as commonly used in other papers, while considering the problem of collision detection. In particular, the time and space complexity of the deep convolution neural network is analyzed, and two conclusions are drawn to provide a reference for the design of the deep convolution neural network. At the same time, several experiments were carried out to obtain the training time and accuracy of neural networks with different layers. The neural networks with different layers were supposed to be able to grasp hyperplane and then compared the results of screening, thus verifying the two conclusions deduced from the theory. Through the theoretical and experimental analysis above, it plays a guiding role in the design of the deep learning model. It is worth mentioning that the labels of the depth learning model training data (all hypothesized hyperplanes) in this paper are automatically calibrated according to some criteria. In other words, these criteria used to determine whether the hyperplane meets the grasping condition are replaced by the depth learning network model. First of all, because soft manipulators are more flexible than rigid manipulators, designing suitable geometric models for soft manipulators becomes the focus of collision avoidance. It is used in 3D point cloud data space to search for grab points and grab directions that meet the grabability criteria. The geometric model must contain enough point cloud data, and the outer surface will not overlap with other point cloud data, so as to ensure the reliability and security of its capture. The geometry model is used to search the grasping points and directions which meet the conditions, which is called hypothetically grabbing hyperplane. Secondly, the grabbing reliability is further considered, and the hypothetical hyperplane is classified and sorted by using the deep learning network (Mod-Le Net) in order to find out the more reliable grab position and direction. Through the contrast experiment of Mod-Le Net and support vector machine, the quality and reliability of grabable hyperplane after Mod-Le Net screening is higher than that of support vector machine, and the quantity is relatively small. In other words, it saves more time in motion control.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP242

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本文編號：1909063