本篇論文提出以感測資料為基礎，利用模糊系統來模擬人類的推論模式，經由使用者的經驗和感測器的整合，建立一個兼具控制及判斷的方法，不需要非常複雜的數學方程式，就可以設計出一個效果不錯的控制器。本文主要利用室內定位系統來提供自走車的位置以及頭向夾角，結合模糊邏輯建立停車的行為模式與使用超音波及紅外線感測器作為停車安全的考量，再搭配基因演算法所搜尋之參考路徑，達到自走車追跡效能最佳化控制。在停車模式分為:車庫已知、車庫未知及運送病歷之應用。另外亦使用攝影機與影像處理技術來擷取路面特定顏色的線條，並且計算其長度藉以判斷停車格地點，再透過超音波感測器完成路邊停車動作。進行硬體實驗前先經由套裝軟體Matlab模擬影像處理及模糊理論，在WMR實體測試中，使用LabView 8.5來撰寫人機介面。硬體實驗結果顯示，使用室內定位系統，能有效提供車體精確的位置，避免使用WMR編碼值運算所造成的累積誤差，提高停車控制的準確性及成功率；而使用攝影機與超音波感測器辨識停車地點及完成停車動作，亦符合吾人駕車之行為，經由硬體的實現，證明此智慧型控制器的可行性及可靠性。

Abstract

This thesis presents a wheeled mobile robot WMR using sensor data in path following and parking control. An intelligent control scheme is proposed, which includes the fuzzy system, genetic algorithm, localization system, and image processing technique. Through system integration, the control system can effectively control the robot without using any complicated mathematical equations. Ultrasonic sensors are used to detect vacancy and examine safety distance during parking process. A CCD camera is utilized to search the location of a parking lot. The localization system provides the position and heading direction of the robot. Fuzzy logic is used to form parking behavior pattern. Reference paths are searched by a genetic algorithm. Difference parking modes are introduced, they are forward garage parking, backward garage parking, multiple forward parking, and parallel parking. At the WMR entity tests, we use the LabView 8.5 to encode the interface between human and machine and utilize a personal computer to compute the expected trajectory. Recursive computations are used in WMR to achieve trajectory tracking control. Experiments show that the proposed control scheme can guide the WMR in different parking modes successfully.

Contents

Abstract（Chinese） I
Abstract（English） II
Acknowledgement (Chinese) III
Contents IV
List of Figures VII
List of Table X
1 Introduction 1
1.1 Research Motivation and Goal 1
1.2 Literature Reviews 1
1.3 Thesis Contribution 3
1.4 Thesis Scheme 4
2 WMR System Step 5
2.1 WMR System Description 5
2.2 WMR Body Apparatus 6
2.2.1 PMS5005 sensor and motion control card 6
2.2.2 DC motor 8
2.2.3 Quadrature encoder 9
2.3 Kinematic Equations 10
2.4 Localization System (StarGazzer) 14
2.5 Camera 16
2.6 Ultrasonic Sensor 19
2.7 Infrared Sensor 20
3 Fuzzy Motion Control 22
3.1 Introduction 22
3.2 Intelligent Control Scheme 22
3.2.1 Wall-following 23
3.2.2 The position of garage is known 28
3.2.3 The position of garage is unknown 32
3.3 Human-machine Interface 35
3.4 LabView 8.5 Operating Interface 38
3.5 Real-time Motion Control 40
3.5.1 Wall-following 41
3.5.2 The position of garage is known 45
3.5.3 The position of garage is unknown 46
3.5.4 Application in hallway of hospital to deliver the anamnesis 48
4 Image Motion Control 53
4.1 Image Processing 53
4.1.1 Color space 53
4.1.2 RGB color space 53
4.1.3 HSV color space 55
4.1.4 Histogram method 57
4.1.5 Application 59
4.2 Simulations 60
4.2.1 Histogram process 60
4.2.2 Camera distance estimation 62
4.3 LabView 8.5 Operating Interface 65
4.4 Real-time Motion Control 66
4.4.1 Real-time image process experiment 66
4.4.2 Backward parallel-parking control experiment 70
5 Conclusions 73
5.1 Discussions 73
5.2 Future Investigation and Suggestion 74
5.2.1 Algorithm 74
5.2.2 Hardware 75
References 76

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