臺灣博碩士論文加值系統

智慧載具廣泛應用於各種需要無人搬運系統的產業，其中，定位與控制技術是核心關鍵。本文透過卡爾曼濾波器融合實時動態衛星定位技術（Real Time Kinematic Global Positioning System, RTK-GPS）和慣性測量單元（Inertial measurement unit, IMU），實現室外移動載具的三維公分級定位及控制精度。本文採用小型自走車作為實驗控制載具，使用微型電腦樹莓派（Raspberry Pi 4B）來執行訊號處理與智慧型控制的任務。IMU訊號經由MAX232模組轉換成UART，由樹莓派直接讀取。這些數據進一步被傳輸到樹莓派並使用MATLAB軟體分析系統性能。為了驗證本文所提方法的有效性，設計並使用了圓形路徑來模擬小型自走車的追蹤軌跡，以展示成功的定位與控制效果。實驗結果顯示，卡爾曼濾波器能有效融合RTK-GPS及IMU訊號，即使在訊號受干擾或部分缺失的情況下，控制系統仍展現出優異的性能。

The extensive use of intelligent vehicles in industries requiring unmanned transport systems underscores the critical role of positioning and control technology. This study utilizes a Kalman filter to integrate Real-Time Kinematic GPS (RTK-GPS) and Inertial Measurement Unit (IMU) data, achieving centimeter-level 3D positioning accuracy for outdoor mobile vehicles. A Raspberry Pi 4B microcomputer handles signal processing and intelligent control tasks, reading IMU signals via UART through an MAX232 module, with data analyzed using MATLAB. A circular path experiment was conducted to validate the method, demonstrating that the Kalman filter effectively merges RTK-GPS and IMU signals, ensuring robust control even under signal interference or partial loss conditions.

摘要.......................................i
Abstract..................................ii
誌謝.....................................iii
目錄......................................iv
表目錄...................................vii
圖目錄..................................viii
符號說明...................................x
第一章 緒論.................................1
1.1 研究動機與目的..........................1
1.2 文獻回顧...............................2
1.3 論文架構...............................5
第二章 硬體和系統結構.......................7
2.1 RTK-GPS...............................8
2.1.1 GNSS L1/L2 多頻段磁性天線...........10
2.1.2 遙測無線電..........................11
2.1.3 基站與移動站的設置...................12
2.1.3.1 現場佈置..........................12
2.2 IMU..................................17
2.2.1 MAX232模組.........................19
2.2.2 CP2102模組.........................20
2.3 樹莓派系統............................21
2.4 履帶車控制系統........................23
第三章 感測器融合.........................25
3.1 預測狀態..............................26
3.2 卡爾曼濾波器(Kalman Filter，KF).......28
3.2.1 卡爾曼濾波器架構....................28
第四章 實驗過程與結果.....................32
4.1 系統控制架構..........................32
4.2 系統控制策略..........................33
4.2.1 角度比例控制器......................35
4.2.2 半徑比例控制器......................36
4.3 實驗結果..............................37
4.3.1 實驗設計............................37
4.3.2 實驗步驟............................38
4.3.2.1 無卡爾曼濾波......................38
4.3.2.2 卡爾曼濾波........................42
第五章 研究結論與展望......................49
5.1 討論與結論............................49
5.2 研究展望..............................50
參考文獻..................................52
Extended Abstract........................55

[1]張耕魁，2008，利用卡曼濾波器整合全球定位系統及慣性導航系統，國立臺北科技大學，機電整合研究所。
[2]曹爾習，2018，多核心嵌入式系統的效能與功耗之研究，國立臺灣科技大學，電子工程系。
[3]楊適鴻，2007，同步多線程架構提取與發派引擎改善之研究，大同大學， 資訊工程學系(所)。
[4]連政南，2007，以DSP平台實現RTK-DGPS為基礎之車輛自動駕駛系統，國立交通大學，電資工程學類。
[5]Melanson, Donald (April 2010). "GPS pioneer Roger L. Easton inducted into National Inventors Hall of Fame". Retrieved October 18, 2013.
[6]J. Kang and S. Park, "Positioning Performance Improvement of RTK Using Kalman Filter for Autonomous Vehicles," 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Hualien City, Taiwan, 2021, pp. 1-2.
[7]黃良吉，2006，GPS與感測器整合於三維地面車輛定位之應用，國立臺灣科技大學，機械工程系。
[8]Hoon-Je Woo, B. -J. Yoon, Bong-Geun Cho and J. -H. Kim, "Research into navigation Algorithm for unmanned ground vehicle using Real Time Kinemtatic (RTK)-GPS," 2009 ICCAS-SICE, Fukuoka, Japan, 2009, pp. 2425-2428.
[9]J. Roskan, Airplane Flight Dynamics ＆Automatic Flight Controls, Part Ⅰ 滄海書局 25～28頁。
[10]R. E. Kalman, 1960, A New Approach to Linear Filtering and Prediction Problems,pp.35-41, Research Institute for Advanced Study, Baltimore, Md.
[11]吳冠昇，2022，基於擴展性卡爾曼濾波器之GPS定位估測，國立陽明交通大學，電機學院電機與控制學程。
[12]黃毅瑄，2023，應用於卡爾曼濾波器汽車導航的共變異數矩陣更新策略， 國立清華大學，通訊工程研究所。
[13]S. Habbachi, M. Sayadi and N. Rezzoug, "Partical filtering for orientation determining using inertial sensors IMU," 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia, 2018, pp. 1-5.
[14]L. Huihui and A. J. Zhang, "Improved adaptive Kalman filtering algorithm for vehicular positioning," 2015 34th Chinese Control Conference (CCC), Hangzhou, China, 2015, pp. 5125-5129.
[15]H. Jasim Habil, Q. A. Al-Jarwany, M. Nema Hawas and M. Jabbar Mnati, "Raspberry Pi 4 and Python Based on Speed and Direction of DC Motor," 2022 4th Global Power, Energy and Communication Conference (GPECOM), Nevsehir, Turkey, 2022, pp. 541-545.
[16]陳星佑，2019，基於RTK-GPS之割草機器人控制設計，國立雲林科技大學，資訊工程系。
[17]H. G. Nguyen, S. J. Hwang and S. W. Kim, "Compiler Construction for Lockstep Execution of Multithreaded Processors," 7th IEEE International Conference on Computer and Information Technology (CIT 2007), Aizu-Wakamatsu, Japan, 2007, pp. 829-834.
[18]E. Edwan, S. Knedlik, J. Zhou and O. Loffeld, "GPS/INS integration for GF-IMU of twelve mono-axial accelerometers configurations," 2009 6th Workshop on Positioning, Navigation and Communication, Hannover, Germany, 2009, pp. 183-188.
[19]蘇柏睿，2020，田口方法及模糊調整PID控制器設計應用於輪型移動機器人循跡，國立金門大學，電子工程學系碩士班。
[20]T. -Y. Wang and C. -D. Chang, "Hybrid Fuzzy PID Controller Design for a Mobile Robot," 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, Japan, 2018, pp. 650-653.