由於雲端運算的普及，使得需要即時因應運算負荷與資料儲存來擴增彈性的工作得以透過網路交由雲端系統來運算。對於機器人領域來說，各種的程式計算與資料儲存也可以透過雲端服務來處理，機器人本身不需搭載過多的運算硬體與儲存空間，機器人進行任務所需的各種資料與程式可存放在雲端，由雲端系統來進行計算、儲存與備援的工作。本研究建置一個雲端地圖合併系統，將機器人所繪製的多個室內部分地圖合併儲存，再由使用者下載合併後的地圖以進行機器人的路徑規劃。地圖合併程式採用霍夫頻譜的演算法來對兩張部分地圖的合併方向與位移提出假設，並將地圖合併後儲存。本研究並以實驗找出部分地圖應以何種策略合併以得到正確的地圖合併結果，並以此策略建置雲端地圖合併系統。

Cloud computing can now be accessed everywhere, so tasks that need instant responses and adjustments according to computational load and data storage space can be achieved with cloud systems. Similarly, for different robotic tasks, cloud computing can also be used for various kinds of computation and storage, robots do not need to be equipped with excessive computing hardware and data storage space. Computationally intensive programs and data can be stored in cloud systems, robots can exploit cloud systems to deal with highly loaded tasks such as computation, data storage and backup. In this research, a cloud system for map merging was implemented, in which partial indoor maps created by robots were merged and stored. Users can then download the merged maps to have path planned for their robots. Hough spectrum is used in this map merging mechanism and hypotheses are computed while merging maps, this process continues until all maps are merged. Experiments were conducted to find out the strategies able to generate more precise results of map merging and eventually the best strategy can be used in the presented cloud system.

第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 論文架構 3
第二章 文獻探討 4
2.1 雲端機器人的優點 4
2.2 Robot Operating System(ROS)簡介 5
2.3 同步定位與地圖建立(SLAM)及GMapping演算法 7
2.4 機器人使用的地圖格式 8
2.5 格點佔據地圖(occupancy grid map) 8
2.6 本研究使用之機器人地圖合併的演算法 9
2.6.1 霍夫轉換之原理 9
2.6.2 離散化霍夫轉換與霍夫頻譜 11
2.6.3 合併可接受指數 12
2.7 亞馬遜雲端服務(Amazon web services) 12
第三章 研究方法與步驟 13
3.1 雲端地圖合併系統的系統架構 13
3.2 不同效能之雷射掃描器對於GMapping演算法結果的影響 14
3.3 地圖合併之策略 15
3.4 地圖合併之流程 16
3.5 雲端地圖合併系統的地圖儲存方式 18
3.6 雲端地圖合併及儲存對機器人路徑規劃的優點 19
第四章 實驗結果與系統建置 22
4.1 遠距與短距雷射掃描器對於地圖繪製結果的差異 22
4.2 地圖合併策略 28
4.2.1 地圖合併方法的實驗方法 28
4.2.2 地圖合併方法的實驗步驟 28
4.2.3 繪製六張部分地圖 30
4.2.4 合併實驗一：地圖有效面積最大者先合併 33
4.2.5 合併實驗二：地圖有效面積最小者先合併 38
4.2.6 合併實驗三：以亂數順序合併部分地圖 42
4.2.7 合併實驗的資料紀錄 45
4.2.8 地圖合併實驗結果之量化結果 46
4.3 雲端地圖合併系統 47
4.3.1 雲端地圖合併系統的作業系統與軟體架構 47
4.3.2 ROS系統使用地圖進行路徑規劃 49
4.3.3 地圖合併的優點 52
第五章 結論、研究限制與未來展望 56
5.1 結論 56
5.2 研究限制 56
5.3 未來展望 56
參考文獻 58
附錄 計算地圖有效面積之函式 62

[1]NIST, NIST Definitoon of cloud computing v15, NIST, Editor. 2009, National Institute of Standards and Technology: Gaithersburg, MD (2009).
[2] J. Kuffner, “Cloud-enabled robots,” in Proc. IEEE-RAS Int. Conf. Humanoid Robot., Nashville, TN, USA, 2010.
[3] B. Kehoe, S. Patil, P. Abbeel, and K. Goldberg, “A survey of research on cloud robotics and automation,” IEEE Trans. Autom. Sci. Eng., vol. 12, no. 2, pp. 398–409, Apr. 2015.
[4] Hu, Guoqiang, Wee Peng Tay, and Yonggang Wen. "Cloud robotics: architecture, challenges and applications." Network, IEEE 26.3 (2012): 21-28
[5] M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E. Berger, R. Wheeler, and A. Ng, “ROS: An open-source robot operating system,” in Proc. ICRA Workshop Open Source Software, 2009.
[6] ROS系統官方網站http://www.ros.org/
[7] ROS工業用系統官方網站http://rosindustrial.org/
[8] Gazebo模擬器官方網站 http://gazebosim.org/
[9] Gazebo官網的柵格化程式http://gazebosim.org/tutorials?tut=custom_messages
[10] PGM圖像檔案的格式說明http://netpbm.sourceforge.net/doc/pgm.html
[11] Nordlund, Fredrik, et al. "Designing of a Network-Aware Cloud Robotic Sensor Observation Framework." Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International. IEEE, 2014.
[12] Ng, Miguel Kaouk, et al. "A cloud robotics system for telepresence enabling mobility impaired people to enjoy the whole museum experience." Design & Technology of Integrated Systems in Nanoscale Era (DTIS), 2015 10th International Conference on. IEEE, 2015.
[13] Rosa, Stefano, Ludovico Orlando Russo, and Basilio Bona. "Towards a ROS-based autonomous cloud robotics platform for data center monitoring." Emerging Technology and Factory Automation (ETFA), 2014 IEEE. IEEE, 2014.
[14] Xia, Fei, et al. "Human-aware mobile robot exploration and motion planner." SoutheastCon 2015. IEEE, 2015.
[15] Lee, Kian Seng, et al. "Autonomous Patrol and surveillance system using unmanned aerial vehicles." Environment and Electrical Engineering (EEEIC), 2015 IEEE 15th International Conference on. IEEE, 2015.
[16] 亞馬遜雲端系統官方網站https://aws.amazon.com/
[17] 亞馬遜雲端系統的自動擴展功能說明https://aws.amazon.com/tw/autoscaling/
[18] Durrant-Whyte, Hugh, and Tim Bailey. "Simultaneous localization and mapping: part I." Robotics & Automation Magazine, IEEE 13.2 (2006): 99-110.
[19] Bailey, Tim, and Hugh Durrant-Whyte. "Simultaneous localization and mapping (SLAM): Part II." IEEE Robotics & Automation Magazine 13.3 (2006): 108-117.
[20] Grisetti, Giorgio, Cyrill Stachniss, and Wolfram Burgard. "Improved techniques for grid mapping with rao-blackwellized particle filters." IEEE transactions on Robotics 23.1 (2007): 34-46.
[21] Grisettiyz, Giorgio, Cyrill Stachniss, and Wolfram Burgard. "Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling." Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE, 2005.
[22] OpenSLAM網站的GMapping演算法介紹
https://www.openslam.org/GMapping.html
[23]由文獻[28]提供的原始碼
http://robotics.ucmerced.edu/sites/robotics.ucmerced.edu/files/page/documents/mapmerge.zip
[24]讀取PGM圖像檔案的程式範例
http://research.cs.queensu.ca/home/cisc859/download/859.programs/cprogram_to_read_PGM/
[25] Turtlebot模擬器 http://wiki.ros.org/turtlebot_simulator
[26] ROS系統的navigation stack套件
http://wiki.ros.org/navigation/Tutorials/RobotSetup
[27] ROS系統使用的urdf語言 http://wiki.ros.org/urdf
[28] Carpin, Stefano. "Fast and accurate map merging for multi-robot systems. "Autonomous Robots 25.3 (2008): 305-316.
[29] Carpin, Stefano. "Merging maps via Hough transform." 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2008.
[30] Birk, Andreas, and Stefano Carpin. "Merging occupancy grid maps from multiple robots." Proceedings of the IEEE 94.7 (2006): 1384-1397.
[31] Censi, Andrea, Luca Iocchi, and Giorgio Grisetti. "Scan matching in the Hough domain." Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE, 2005.
[32] Andersone, Ilze. "The Characteristics of the Map Merging Methods: A Survey."Scientific Journal of Riga Technical University. Computer Sciences 41.1 (2010): 113-121.
[33] Andersone, Ilze. "Map Merging in the Context of Image Processing." Scientific Journal of Riga Technical University. Computer Sciences 44.1 (2011): 124-130.
[34] Andersone, Ilze. "The influence of the map merging order on the resulting global map in multi-robot mapping." Applied Computer Systems 13.1 (2012): 22-28.
[35] 微軟Kinect感測器的規格說明
https://developer.microsoft.com/en-us/windows/kinect/hardware
[36] Hokuyo URG-04LX-UG01雷射掃描器的規格說明
http://www.hokuyo-aut.jp/02sensor/07scanner/urg_04lx_ug01.html
[37] Hokuyo UTM-30LX雷射掃描器的規格說明
http://www.hokuyo-aut.jp/02sensor/07scanner/utm_30lx.html
[38] IEEE RAS Map Data Representation Working Group. IEEE standard for robot map data representation for navigation, sponsor: IEEE robotics and automation society.