臺灣博碩士論文加值系統

本論文旨在探討應用模糊適應增廣訊息濾波策略(Fuzzy adaptive extended information filtering)於自動導航車自我定位之方法與技術。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)及指數加權增廣訊息濾波技術(Extended information filtering)所組成的模糊適應增廣訊息濾波技術，並對其主要特性作詳盡之研究。本文另提出兩種新式自動導航車定位系統結合模糊適應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，其一是使用雷射掃描器及反光板之三角測量法(Three-point triangulation)，其二是使用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。這兩種定位方法不但可決定自動導航車相對於慣性參考座標的絕對位置及車頭方向，而且能應用模糊適應增廣訊息濾波技術取得導航車之動態位置及車頭方向估測值。透過電腦模擬及實驗數據足以證明本論文所提之定位系統與模糊適應增廣訊息濾波信號處理方法的有效性與可行性。

This thesis develops methodologies and techniques for self-localization of an autonomous mobile robot (AMR) using the fuzzy adaptive extended information filtering (FAEIF) scheme. The FAEIF, 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 FAEIF scheme are studied in some details. Two novel localization systems together with the FAEIF signal processing methods are proposed to improve the accuracy and robustness of pose estimation for the AMR. The first one based on the three-point triangulation uses a laser scanner and at least three retro-reflectors. The second one fuses ultrasonic time-of-flight (TOF) readings measured from two ultrasonic transmitters and three receivers. In these two methods, not only the static position and orientation of the AMR can be determined uniquely with respect to an inertial frame of reference, but also the dynamic pose estimates can be obtained by the FAEIF-based sensor fusion approach. Numerous simulation and experimental results are provided to show the effectiveness and feasibility of the proposed localization systems and the FAEIF signal processing methods.

Contents
Chinese Abstract i
English Abstract ii
Acknowledgments iii
Contents iv
List of Figures vii
List of Tables x
Nomenclature xi
Chapter 1：Introduction 1
1.1 Introduction 1
1.2 Literature Review 3
1.3 Contributions of the Thesis 7
1.4 Organization of the Thesis 8
Chapter 2：Fuzzy Adaptive Extended Information Filtering 10
2.1 Introduction 10
2.2 The Information Filter and Extended Information Filter (EIF) 12
2.3 Exponential Weighted EIF 16
2.4 Fuzzy Adaptive EIF 19
2.4.1 Fuzzy Tuner 20
2.4.2 Fuzzy Adaptive EIF (FAEIF) Algorithm 25
2.5 Simulation Results and Discussion 25
2.6 Concluding Remarks 33
Chapter 3：Self-Localization of an Autonomous Mobile Robot
Using a Laser Scanner 35
3.1 Introduction 35
3.2 Laser Scanner and Triangulation 36
3.3 Sensitivity to Measurement Errors 42
3.4 Static Localization Estimation Algorithm 46
3.5 Dynamic Localization Estimation Algorithm 50
3.6 Simulation, Experimental Results and Discussion 53
3.6.1 Computer Simulation 53
3.6.2 Static Experiment 60
3.7 Concluding Remarks 62
Chapter 4：Self-Localization of an Autonomous Mobile Robot
Using Ultrasonic Measurements 64
4.1 Introduction 64
4.2 Physical Configuration and Mathematical Description of the
Ultrasonic Location System 65
4.3 Static Localization Estimation Algorithm 71
4.4 Dynamic Localization Estimation Algorithm 73
4.5 Simulation, Experimental Results and Discussion 76
4.5.1 Computer Simulation 77
4.5.2 Static Experiment 83
4.6 Concluding Remarks 86
Chapter 5：Summaries and Recommendations 88
5.1 Summaries 88
5.2 Recommendations 90
Bibliography 91
Appendix A 95
Appendix B 98

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