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研究生:許弘毅
研究生(外文):Hung-Yi Hsu
論文名稱:結合視覺辨識與手臂抓取功能之智慧型自走車的控制設計與實現
論文名稱(外文):Control Design and Implementation of Intelligent Vehicle with Robot Arm and Computer Vision
指導教授:林容杉
指導教授(外文):Jung-Shan Lin
口試委員:洪志偉黃秋杰林容杉
口試委員(外文):Jeih-weih HungChiou-Jye HuangJung-Shan Lin
口試日期:2013-07-30
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:70
中文關鍵詞:自走車機械手臂影像辨識
外文關鍵詞:VehicleRobot ArmImage Recognition
相關次數:
  • 被引用被引用:0
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  • 下載下載:57
  • 收藏至我的研究室書目清單書目收藏:0
現今科技的發展越來越迅速,許多原本只能依賴人力的工作漸漸的開始被機械所取代。最簡單的例子在於工廠的重複性高的一些作業,這些作業使用人力反而容易出錯誤。然而也是有很多工作是機械所無法取代的,相對於重複性高的,這些大都是判別度很高的工作。而機器在重複性高的工作相當容易設計,判別度高的工作則難以設計。

本研究計畫主要可以分三個主要方向,分別是自走車的控制系統設計、機械手臂的運動模組規劃、以及影像辨識的研究與應用。

在自走車的控制系統方面,我們利用安裝在手臂上的Camera來抓取影像,並利用分析此影像來判斷車體所要轉動與行進的方位,之後再操作車體達到追物系統的目的。

在機械手臂方面,我們藉由了解機械手臂的運動方式,來建構出機械手臂的數學模組,利用順向運動學來建構出基礎的手臂動作,並利用影像分析得到的物體資訊,使用逆向運動學來推斷出各軸關節所要轉動的角度,最後操作各軸的馬達已達到我們所預定的工作目標。

於影像辨識方面,我們依然利用機械手臂上的Camera來抓取影像。我們將分析影像來得到物體的位置、形狀、以及顏色等資訊,分析的影像主要為三個,分別為車體移動、抓取物體、以及放置物體。

Applications of intelligent robot or vehicle systems have become more and more important and popular in our daily life. This thesis can be divided into three main directions for the design of intelligent vehicle, including trajectory planning of mobile vehicle, position control of robot arm system and application of image recognition. For the trajectory planning of vehicle system design, the camera installed on the clip of robot arm is applied to capture images with appropriate correction. The moving angle of vehicle is calculated by analyzing the captured images. And then, the vehicle is able to be manipulated in order to achieve the purpose of object tracking.

For the position control of robot arm, the mathematical model is developed by understanding the movement pattern. The basic movement of robot arm system is constructed by using the method of forward and inverse kinematics. The information of object obtained by appropriate image analysis could be employed to obtain the angle of rotation for each joint, so the desired goal is achieved by correct motor operation. In the part of image recognition, useful images are captured by the camera installed on the clip of robot arm. These images must be effectively analyzed to obtain the position, shape and color information of the object. The utilization of image analysis is applied for vehicle moving, object grabbing and object placing. The part of vehicle moving is to find the desired angle to track objects. The parts of grabbing and placing objects are related to the analysis of the information of bricks and sockets, respectively.

As a result, the intelligent vehicle has the potentials to move and stop in front of the target wooden box. And then, after the robot arm grabs the brick placed on the wooden box, the vehicle would turn back to its starting point. Finally, the robot arm places the brick into the socket with correct color and shape. In addition, the implementation of intelligent vehicle design is given to illustrate the successful achievement of the pick-and-place control objective.

Abstract i
Contents iii
List of Tables iv
List of Figures vii
1 Introduction 1
2 Vehicle System Construction 5
2.1 Workspace Description . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Control Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Vehicle Trajectory Planning . . . . . . . . . . . . . . . . . . . . . . . 9
3 Robot Arm Motion Design 15
3.1 Simpli cation of Robot Arm Model . . . . . . . . . . . . . . . . . . . 15
3.1.1 Vertical Movement of Clip . . . . . . . . . . . . . . . . . . . . 16
3.1.2 Simpli cation of Three-Dimensional Space . . . . . . . . . . . 21
3.2 Inverse Kinematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4 Image Recognition 26
4.1 Vehicle Moving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1.1 Image Cutting . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
ii
CONTENTS iii
4.1.2 Reduce Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.1.3 Direction Calculation . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 Object Grabbing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2.1 HSV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.2 Position of Object . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2.3 Shape Recognition . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.4 Hough Transform . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.5 Matrix Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2.6 Mean Shift Clustering . . . . . . . . . . . . . . . . . . . . . . 49
4.2.7 Height Detection . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3 Object Placing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5 Practical Experiment 56
6 Conclusions and Future Works 65
Bibliography 67
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