臺灣博碩士論文加值系統

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 本篇論文主要研究自走車動態避障控制，結合CCD 攝影機和室內定位系統所提供之資訊作為模糊控制器的輸入，以這兩種感測器為基礎，加上經過模糊控制器運算的輸出，不需要非常複雜的數學方程式，就可以設計出一個不錯的動態避障控制器。本論文第一部分是利用室內定位系統所給予的座標和角度，使自走車行進在預設路線上，再以模糊理論設計模糊控制法則，使自走車經過動態避障後能及時自我修正回預設路徑上。第二部分主要是藉由CCD 攝影機捕捉的影像，經過影像處理去擷取出動態障礙物的座標資料，並且計算出動態障礙物距離自走車的距離，以及判斷動態障礙物的移動方向角度，再以模糊法則進行動態避障控制。硬體部分使用LabView 8.5 來撰寫人機介面，將感測器資料傳回電腦運算，產生輸出轉速，藉由WiFi(802.11b)無線網路傳遞運算數值及自走車回授信號，影像處理用C 語言編寫程式並結合LabView 8.5 來控制自走車。經由實驗證實，本論文所設計的動態避障控制器運行良好。
 This thesis presents dynamic obstacle avoidance control of awheeled mobile robot (WMR). A CCD camera and a localization systemare integrated in the control scheme, which provide the inputs of thefuzzy controller. Based on these sensors and output operation of the fuzzycontroller, an adequate dynamic obstacle avoidance controller can bedesigned without complex mathematical equation. The first part of thisthesis is the use of localization system coordinates and angles to make theWMR move on the default path, then design fuzzy control rules by fuzzytheory, so that after the WMR avoiding dynamic obstacle, it can beself-corrected and return to default path promptly. The second part is theuse of CCD camera to capture images and through image processing toobtain the coordinate data from the image of a dynamic obstacle. Thedistance between dynamic obstacle and the WMR, as well as the directionof movement of the moving obstacle can be calculated. Fuzzy rules areapplied to realize dynamic obstacle avoidance control. Hardwareimplementation uses LABVIEW 8.5 to realize interface between humanand machine. Command signals are transferred through WiFi (802.11b)wireless to communicate data between the WMR and the computer.Image processing is handled by C code and then is integrated byLABVIEW 8.5 to control the WMR. The experiments confirm that theproposed dynamic obstacle avoidance control scheme works properly.
 Abstract（Chinese） IAbstract（English） IIAcknowledgement (Chinese) IIIContents IVList of Figures VIIList of Table XI1 Introduction 11.1 Preface 11.2 Research motivation and goal 11.3 Literature reviews 21.4 Thesis contribution 31.5 Organization of this thesis 42 WMR System Setup 52.1 WMR system description 52.2 WMR body apparatus 72.2.1 DC motor 72.2.2 MDM5253 DC motor driving module 82.2.3 Quadrature encoder 102.2.4 PMS5005 sensor and motion control card 122.3 Dynamic equations 132.4 Localization system (StarGazer) 162.5 Camera 183 Dynamic Obstacle Detection by Visual Sensor 213.1 Color space 21V3.1.1 RGB color space 213.1.2 HSV color space 233.2 Binary space 263.3 Erode and dilate 273.3.1 Erode 273.3.2 Dilate 293.4 Tag dynamic obstacle 313.5 Distance estimation 334 Fuzzy Control System 374.1 Introduction 374.2 Type-1 fuzzy control system 374.2.1 Fuzzification 384.2.2 Fuzzy rule base 394.2.3 Inference engine 394.2.4 Defuzzification 404.3 Simplified interval type-2 fuzzy control system 404.3.1 Type-2 fuzzy theory 414.3.2 Simplified interval type-2 fuzzy theory 444.4 Intelligent control scheme 474.4.1 Straight line path following 474.4.2 Adjusting optimal avoiding distance and turning angle 524.4.3 Dynamic obstacle avoidance 545 Experimental Results 635.1 The straight line path following 635.2 The dynamic obstacle avoidance 675.2.1 Single dynamic obstacle avoidance 68VI5.2.1.1 Dynamic obstacle moving to the left 685.2.1.2 Dynamic obstacle moving to the right 715.2.1.3 Dynamic obstacle moving on oblique line 745.2.1.4 Head-on collision avoidance 775.2.2 Multiple dynamic obstacles avoidance 806 Conclusions 876.1 Discussion 876.2 Future investigation and suggestion 876.2.1 Control scheme 886.2.2 Hardware 88References 89
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