近年來自動化設備開始導入生產線系統，物料的供給或搬運，決定工廠的生產效率，無人搬運車(Autonomous Guided Vehicle, AGV)系統，應用雷射光學雷達(Light Dection and Ranging, LiDAR)與光學反射稜鏡(Corner Cube)等先進光學測距儀偵測器，在一特定專用地圖範圍內的空間場域，進行室內定位與同步建構輪廓地圖。無人搬運車先期研究，由傳統移動機器人建立系統模型出發，建立結構靜力分析、剛體機構運動學，應用麥克納姆輪(Mecanum Wheel)，設計研製無人搬運車載具系統。
本文將聚焦於AGV室內定位技術與自主導航控制研究，應用光學雷達偵測器與反射稜鏡，標定各位置座標，構建同步定位與地圖構建(Simultaneous Localization And Mapping, SLAM)的核心技術，利用機器人作業系統(Robot Operating System, ROS)，執行AGV系統運動與控制邏輯的模擬、測試與驗證分析。建立無人搬運車載系統動力模型、光學位置參數估計與自適應蒙特卡羅定位(Adaptive Monte Carlo Localization, AMCL)結合光學室內定位演算法研發應用。

In recent years, automation equipment has begun to be introduced into the system o f the production line in Industry 4.0. To supply and handle of material or the work pieces determine the production efficiency of the factory. The Autonomous Guided Vehicle (AGV) system uses a laser optical radar (Light Dection and Ranging, LiDAR), reflection ridges (Corner Cubes) and the other advanced optical rangefinder detectors to perform indoor positioning and synchronous construction of the contour in a spatial field within the range of a specific dedicated map. The preliminary researches of AGV started from the establishment of the system model of traditional mobile robots, and establishment the static analysis of its structure, a kinematics of rigid body, dynamic and mathematic models of structure of its control drives. The Mecanum wheels were used to design and develop an unmanned transport vehicle system in our study. We conducted a numerical analysis of the kinematics model and its dynamic vibration modes of the mechanism, and fully described the kinematics characteristics of the unmanned transport vehicle and the dynamic behavior of its dynamic system.
In this thesis we have focused on the researches of AGV indoor positioning technology and autonomous navigation control technologies, using optical radar detectors and reflections to calibrate the coordinates of each position, building the core technology of indoor simultaneous positioning and mapping (Simultaneous Localization And Mapping, SLAM), using Robots Operating System (ROS), which performed simulation, testing and the verification analysis of AGV mechanism, constructing the motion and navigation control logic. It was well established the dynamic model of the unmanned transport vehicle system, estimated of optical position parameters and developed to apply for the Adaptive Monte Carlo Localization (AMCL) that combined with optical indoor positioning algorithm.

[1] Z.-Y. Chen, P.-R. Liaw, V. L. Nguyen, and P. T. Lin, "Design of a high-payload Mecanum-wheel ground vehicle (MWGV)," Robotic Systems Applications, vol. 1, no. 1, 2021.
[2] D. Fox, W. Burgard, and S. Thrun, "Markov localization for mobile robots in dynamic environments," Journal of artificial intelligence research, vol. 11, 1999.
[3] Z. Zeng, L. Wang, and S. Liu, "An introduction for the indoor localization systems and the position estimation algorithms," in 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4), 2019: IEEE.
[4] X. Zhu, W. Zhu, and Z. Chen, "Direct localization based on motion analysis of single-station using TOA," in 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2018: IEEE.
[5] C. Liu, J. Yang, and F. Wang, "Joint TDOA and AOA location algorithm," Journal of Systems Engineering Electronics, vol. 24, no. 2, 2013.
[6] Y. Wang and K. Ho, "An asymptotically efficient estimator in closed-form for 3-D AOA localization using a sensor network," IEEE Transactions on Wireless Communications, vol. 14, no. 12, 2015.
[7] M. Hofbauer, J. Seiter, M. Davidovic, and H. Zimmermann, "A processing approach for a correlating time-of-flight range sensor based on a least squares method," in 2014 IEEE Sensors Applications Symposium (SAS), 2014: IEEE.
[8] L. Yi, L. Tao, and S. Jun, "RSSI localization method for mine underground based on RSSI hybrid filtering algorithm," in 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), 2017: IEEE.
[9] R. Mautz, "Indoor positioning technologies," 2012.
[10] 席瑞，李玉軍，侯孟書, "室内定位方法綜述," vol. 43, no. 4, 2016.
[11] 王星星，從思安, "室内定位研究方法綜述," vol. 18, no. 9, 2019.
[12] A. R. Khairuddin, M. S. Talib, and H. Haron, "Review on simultaneous localization and mapping (SLAM)," in 2015 IEEE international conference on control system, computing and engineering (ICCSCE), 2015: IEEE.
[13] J. P. M. dos Santos, SmokeNav-simultaneous localization and mapping in reduced visibility scenarios. University of Coimbra, 2013.
[14] Z. Xuexi, L. Guokun, F. Genping, X. Dongliang, and L. Shiliu, "SLAM algorithm analysis of mobile robot based on lidar," in 2019 Chinese Control Conference (CCC), 2019: IEEE.
[15] I. Z. Ibragimov and I. M. Afanasyev, "Comparison of ROS-based visual SLAM methods in homogeneous indoor environment," in 2017 14th Workshop on Positioning, Navigation and Communications (WPNC), 2017: IEEE.
[16] Y. Abdelrasoul, A. B. S. H. Saman, and P. Sebastian, "A quantitative study of tuning ROS gmapping parameters and their effect on performing indoor 2D SLAM," in 2016 2nd IEEE international symposium on robotics and manufacturing automation (ROMA), 2016: IEEE.
[17] S. Xu and W. Chou, "An improved indoor localization method for mobile robot based on WiFi fingerprint and AMCL," in 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, vol. 1: IEEE.
[18] X. Zhou, Z. Su, D. Huang, H. Zhang, T. Cheng, and J. Wu, "Robust global localization by using global visual features and range finders data," in 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018: IEEE.
[19] H. Zhang, C. Zhang, W. Yang, and C.-Y. Chen, "Localization and navigation using QR code for mobile robot in indoor environment," in 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2015: IEEE.
[20] 溫倩雯，蘇毅輝，李庚, "基於單目相機與里程計的室内機器人定位研究［J］," 電視技術, 2021.
[21] 欣橋機械有限公司. (2020). MiR 無人搬運車. Available: http://www.symbridge.com.tw/automation/zh/prod.php?class=28
[22] 台灣塔奇恩科技-AGV無人搬運車. (2019). AGV無人搬運車. Available: https://www.tircgo.com/page/about/index.aspx?kind=38
[23] IAmech.com, "iAmech漢錸科技產品總覽," 2020.
[24] R. A. P/L. (2014-2020). AUTOMATED GUIDED VEHICLES (AGV). Available: https://www.roboticautomation.com.au/ra_solutions/automated-guided-vehicles-agv/
[25] BionicHIVE, "BionicHIVE," 2021.
[26] E. LTD, "EROWA Robot Dynamic 150L," 2021.
[27] 先構技研, "自主移動機器人 Autonomous Mobile Robot," 2017.
[28] B. E. Ilon, "Wheels for a course stable selfpropelling vehicle movable in any desired direction on the ground or some other base," ed: Google Patents, 1975.
[29] PEPPERL+FUCHS, "2-D LiDAR Sensor OMD30M-R2000-B23-V1V1D-HD-1L " 2007.
[30] PEPPERL+FUCHS, "R2000 UHD 二維激光掃描儀(通用篇)," 2014.
[31] PEPPERL+FUCHS, "R2000 2-D Laser Scanner," 2019.
[32] Wikipedia. (2020). Corner reflector. Available: https://en.wikipedia.org/wiki/Corner_reflector
[33] AmandaDattalo. (2018). ROS/Introduction. Available: http://wiki.ros.org/ROS/Introduction
[34] S. Rivera, S. Lagraa, C. Nita-Rotaru, S. Becker, and R. State, "Ros-defender: Sdn-based security policy enforcement for robotic applications," in 2019 IEEE Security and Privacy Workshops (SPW), 2019: IEEE.
[35] WIKIPEDIA, "Monte Carlo localization," 2021.
[36] D. Fox, W. Burgard, F. Dellaert, and S. Thrun, "Monte carlo localization: Efficient position estimation for mobile robots," AAAI/IAAI, vol. 1999, 1999.
[37] cig01. (2016). Robot Localization. Available: https://aandds.com/blog/robot-localization.html?fbclid=IwAR0P1uqwqjI-SGWoR1EGLxsChbyytgY0p4XMDBlXDgMEaz5FBc04ROv_mgU
[38] D. Fox, "KLD-sampling: Adaptive particle filters and mobile robot localization," Advances in Neural Information Processing Systems, vol. 14, no. 1, 2001.