臺灣博碩士論文加值系統

本論文的目的是在探討應用模糊增廣訊息濾波策略(Fuzzy extended information filtering)於自動導航車定位之方法與技術。為偵測及避免非線性濾波技術的發散問題，本文提出由模糊調諧器(Fuzzy tuner)及指數加權訊息濾波技術(Extended information filtering)所組成的模糊增廣訊息濾波技術，並對其主要特性作詳盡之研究。
文中提出四種自動導航車定位系統結合模糊增廣訊息濾波技術之信號處理方法，來增進定位估測的精確度與強健度，其一是使用一個超音波定位系統，由兩個超音波發射器及三個接收器之超音波飛行時間(Time of flight)測量法，然後配合模糊增廣訊息過濾(FEIF)方法，用來改進機器人的動態的定位與定向姿態估計的準確。其二是使用一台雷射掃描器及至少用3 個反光板之三角測量法(Three-point triangulation) 被提出，找到一個機器人的最初姿勢，然後配合模糊增廣訊息過濾方法，用來改進機器人的姿態估計的準確。其三是使用一個超音波定位系統，由兩個超音波發射器及三個接收器之超音波飛行時間測量法，然後並使用一台雷射掃描器配合模糊增廣訊息過濾方法用來改進機器人的動態的定位與定向姿態估計的準確。其四是使用一個主動式RFID定位系統，利用從四個電子標籤 (Tag) 所發射訊號強度RSSI數據，經由Reader接收後分別被轉化成距離，再使用所提出的最小平方法 (Least square method)，可估測出導覽機器人的最初姿勢; 並使用一台雷射掃描器配合模糊增廣訊息過濾方法，來追蹤機器人的動態姿態估計的準確。
以上這四種定位方法，不但可決定機器人相對於慣性參考座標的絕對位置及車頭方向，而且能應用模糊增廣訊息濾波技術，取得機器人之動態位置及車頭方向估測值。透過電腦模擬及實驗數據可以證實，本論文所提之定位系統與模糊增廣訊息濾波信號處理方法，是具有效性與可行性。

This dissertation presents methodologies and techniques for localization of an autonomous mobile robot (AMR) using the fuzzy extended information filtering (FEIF) scheme. The FEIF, composed of a fuzzy tuner and the exponential weighted extended information filter (EIF), is presented in order to detect and avoid the nonlinear filter divergence problems. The main features of the FEIF scheme are studied in some details.
Four novel localization systems together with the FEIF signal processing method are proposed to improve the accuracy and robustness of pose estimation for the AMR. The first one establishes on a novel ultrasonic localization system which consists of two ultrasonic transmitters and three receivers, and uses the FEIF to improve the estimation of both the static and dynamic position and orientation of the AMR. A fuzzy extended information filter is presented to improve estimation accuracy and robustness for the proposed localization system, while the system lacks of sufficient information of complete models or the process and measurement noise varies with time. The second one investigates pose estimation and tracking of the AMR using a laser scanner with at least three retro-reflectors. A three-point laser triangulation method is presented to find an initial posture of the robot and then a FEIF method is used to improve the accuracy of the robot’s pose estimation in motion. The third one employs a FEIF approach to improving global localization of an indoor AMR with ultrasonic and laser scanning measurements. The fourth one applies a least-squares method and a FEIF scheme to global pose estimation of an tour-guide robot with radio-frequency-identification (RFID) and laser scanning measurements.
In these four methods, not only the static position and orientation of the robot can be determined uniquely with respect to an inertial frame of reference, but also the moving pose estimates can be obtained by the FEIF-based sensor fusion approach. Numerous simulation and experimental results are provided to show the effectiveness and merits of the proposed localization systems and the FEIF signal processing method.

Chapter 1 Introduction 1
1.1 Background 1
1.2 Literature Review 3
1.4 Contributions of the Dissertation 9
1.5 Organization of the Dissertation 10

Chapter 2 Fuzzy Extended Information Filtering 12
2.1 Introduction 12
2.2 Information Filter and Extended Information Filter 12
2.3 Exponential Weighted EIF 15
2.4. Fuzzy EIF (FEIF) 17
2.4.1 Fuzzy Tuner 18
2.4.2 Fuzzy EIF (FEIF) Algorithm 24
2.5 Simulation Results and Discussion 24
2.6. Concluding Remarks 29


Chapter 3 Ultrasonic Localization and Pose Tracking of an Autonomous Mobile Robot via Fuzzy Extended Information Filtering 31
3.1 Introduction 31
3.2 Ultrasonic Localization System 33
3.3 Static Pose Estimation Algorithm 39
3.4 FEIF-based Moving Pose Estimation Algorithm 39
3.5 Simulation, Experimental Results and Discussion 43
3.5.1 Computer Simulation 43
3.5.2 Static Experiment 46
3.5.3 Moving Experiments 48
3.6 Concluding Remarks 53

Chapter 4 Pose Estimation and Tracking of an Autonomous Mobile Robot Using a Laser Scanner with Retro-Reflectors 54
4.1 Introduction 54
4.2 Laser Triangulation and Static Pose Estimation 56
4.2.1 Laser Triangulation 56
4.2.2 Static Pose Estimation 60
4.3 FEIF-based Pose Tracking 63
4.4 Simulations, Experimental Results and Discussion 67
4.4.1 Computer Simulations and Discussion 67
4.4.2 Experimental Results and Discussion 73
4.5 Concluding Remarks 81

Chapter 5 Global Pose Initialization and Tracking of an Autonomous Mobile Robot Using Ultrasonic and Laser Scanning Measurements 83
5.1 Introduction 83
5.2 Improved Global Localization 84
5.2.1 Global Localization via Ultrasonics 84
5.2.2 Improved Global Pose Estimation Algorithm 86
5.3 Improved Global Pose Tracking by Fusing Ultrasonic and Laser Scanning Measurements 88
5.3.1 Ultrasonic TOF Measurements 89
5.3.2 Laser Scanning Measurements 91
5.3.3 Overall Measurement Model 93
5.3.4 Real-time FEIF-based Global Pose Tracking Algorithm 94
5.4 Experimental Results and Discussion 96
5.5 Concluding Remarks 104

Chapter 6 Global Pose Estimation of a Tour-guide Robot using RFID and Laser Scanning Measurements 106
6.1 Introduction 106
6.2 Global Pose Initialization Algorithm Using RFID 109
6.2.1 Calibration of the RFID System 109
6.2.2 RFID Localization Using Least-Squares Method 111
6.2.3 Real-Time RFID Global Pose Initialization Algorithm 114
6.3 Laser FEIF-based Pose Estimation 115
6.3.1 Environmental Model and Wall Extraction 116
6.3.2 Overall Measurement Model 116
6.3.3 Real-Time FEIF-based Laser Pose Tracking Algorithm 117
6.4 Experimental Results and Discussion 119
6.4.1 Experimental Tour-Guide Robot 119
6.4.2 Computer Simulation and Discussion 121
6.4.3 Experimental Results and Discussion 121
6.5 Concluding Remarks 129

Chapter 7 Conclusions and Future Work 130
7.1 Conclusions 130
7.2 Future Work 133
Bibliography 135

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