臺灣博碩士論文加值系統

本論文旨在探討分別應用兩種外部感測器：超音波及雷射掃描器，於自動導航車姿態追蹤與姿態初值化之方法學與實現技術。本文首先發展一種以FPGA為基礎的超音波姿態追蹤系統，透過超音波飛行時間(time of flight)測量法，並結合模糊適應增廣訊息濾波技術(fuzzy adaptive extended information filtering)來增進姿態估測的精確度與強健度。模糊適應增廣訊息濾波技術在於避免非線性增廣訊息濾波技術的發散問題。其次，本文提出一種以矩形模型及2D雷射掃描器為基礎的低複雜性和精確的姿態追蹤演算法，並結合增廣型卡爾曼濾波策略(extended Kalman filter)。最後，在環境模型已知的情況下，本文另提出一種以2D雷射掃描器為基礎的姿態初值化演算法，以便決定自動導航車在環境中的姿態。透過電腦模擬及實驗數據足以證明本論文所提之姿態追蹤與姿態初值化技術的可行性與有效性。

This thesis develops methodologies and techniques for pose-tracking and pose initialization of an autonomous mobile robot (AMR) using two different external sensors: ultrasonics and laser scanner. First, a novel FPGA-based ultrasonic pose-tracking system by fusing the time-of-flight (TOF) readings together with the FAEIF algorithm is proposed to improve the accuracy and robustness of pose estimation for the AMR. The FAEIF-based sensor fusion approach is presented to circumvent the nonlinear filter divergence problems. Second, a low-complexity and accurate pose-tracking EKF algorithm is proposed based on rectangular model and a 2-D laser scanner. Finally, a pose initialization scheme based on a 2-D laser scanner is presented to determine the initial pose of the AMR given that the environmental model is known. Numerous simulations and experimental results are provided to verify the feasibility and effectiveness of the proposed pose-tracking and pose initialization methods.

Chinese Abstract i
English Abstract ii
Acknowledgments iii
Contents iv
List of Figures viii
List of Tables xii
Nomenclature xiii
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Literature Review 3
1.3 Contributions of the Thesis 5
1.4 Organization of the Thesis 6
Chapter 2 Ultrasonic-Based Pose-Tracking 8
2.1 Introduction 8
2.2 Description of the Autonomous Mobile Robot and Control System 10
2.3 FPGA-based Ultrasonic Pose-Tracking System 12
2.3.1 Field Programmable Gate Array (FPGA) 13
2.3.2 Design Software 15
2.4 Physical Configuration and Mathematical Description of the FPGA-based Ultrasonic Pose-Tracking System 18
2.5 FAEIF-based Dynamic Pose Estimation Algorithm 22
2.5.1 Fuzzy Adaptive EIF (FAEIF) 25
2.5.2 FAEIF-based Pose Estimation Algorithm 28
2.6 Simulation, Experimental Results and Discussion 31
2.6.1 Computer Simulation 31
2.6.2 Dynamic Experiments 34
2.6.2.1 Static Pose Estimate 35
2.6.2.2 Dynamic Pose-Tracking 36
2.7 Concluding Remarks 39
Chapter 3 Laser-Based Pose-Tracking 40
3.1 Introduction 40
3.2 Laser Scanning System 41
3.2.1 Basics of Laser Measurement System 42
3.2.2 Hardware Setup 44
3.2.2.1 Required Components 44
3.2.2.2 Serial Interface for Data Exchange 46
3.2.2.3 Communication Setup and Software 46
3.3 Pose-Tracking Algorithm 48
3.3.1 Environmental Model 49
3.3.2 Measurement Model 50
3.3.3 EKF-based Pose-Tracking Algorithm 52
3.3.4 Line Extraction 54
3.3.4.1 Validation Gate 55
3.3.4.2 Range-Weighted Hough Transform 57
3.4 Simulations, Experimental Results and Discussion 60
3.4.1 Computer Simulations 60
3.4.2 Experiments 63
3.5 Concluding Remarks 67
Chapter 4 Pose Initialization 68
4.1 Introduction 68
4.2 Clustering and Line Segment Extraction 69
4.3 Pose Initialization 77
4.3.1 Static Pose Initialization 77
4.3.2 Dynamic Pose Initialization 80
4.4 Experimental Results and Discussion 81
4.4.1 Static Experiment 82
4.4.2 Dynamic Experiments 83
4.5 Concluding Remarks 88
Chapter 5 Conclusions and Future Work 89
5.1 Conclusions 89
5.2 Future Work 90
References 92
Appendix A 94

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