於ROS架構下應用SLAM之導航機器人開發

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姓名

蔡承祐(Cheng-Yu Tsai) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

於ROS架構下應用SLAM之導航機器人開發
(Navigation Robot Development by Applying SLAM Based on ROS)

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摘要(中)

本論文基於機器人作業系統(Robot Operating System, ROS)開發室內導航機器人，機器人以樹莓派為運行機器人作業系統的核心，利用2D光學雷達(LiDAR)作為機器人的感測器，掃描並獲得周遭環境的平面資訊，以此進行同步定位與建圖(Simultaneous Localization and Mapping, SLAM)演算法並獲得即時位姿與建構平面地圖。接著透過在地圖上給予目標點資訊，機器人進行最佳路徑規劃。最後，樹莓派以ROSSerial與Arduino進行通訊，將規劃好的路徑轉換成速度與角速度數據輸出至Arduino後，Arduino以脈衝寬度調變(Pulse Width Modulation, PWM)訊號控制馬達轉速，進行機器人的移動控制，以此實現室內機器人的導航開發。

摘要(英)

This thesis develops a indoor navigation robot based on Robot Operating System (ROS). The robot uses RaspberryPi to operate ROS, and applies 2D LiDAR as the sensor of the robot to scan and acquire the 2D information of surroundings, then uses the infromation above for conducting Simultaneous Localization and Mapping (SLAM) algorithm in order to obtain instant pose of the robot and build 2D map. After that, the robot applies optimal path planning by giving the information of specific point targets on the map. Finally, Raspberry Pi connects with Arduino by ROSSerial, transfers the planned path to velocity and angular velocity data then exports to Arduino, and Arduino controls the motor speed with Pulse Width Modulation (PWM) to operate the movement control of the robot by following the planned path, thus achieve the goal of indoor navigation robot development.

關鍵字(中)

★ 機器人作業系統
★ 同步定位與建圖
★ 路徑規劃
★ 光學雷達

關鍵字(英)

★ Robot Operating System (ROS)
★ Simultaneous Localization and Mapping (SLAM)
★ Path Planning
★ LiDAR

論文目次

摘要 iv
英文摘要 v
目錄 vi
一、緒論 1
1.1 背景介紹 1
1.2 研究動機與目的 1
1.3 文獻回顧 2
1.4 論文架構 7
二、硬體架構與元件介紹 8
2.1 硬體架構 8
2.2 元件介紹 9
2.2.1 主控制板 9
2.2.2 感測器 10
2.2.3 控制元件與馬達 13
2.2.4 電源系統 16
2.2.5 車體架構 17
三、背景知識 19
3.1 機器人作業系統 19
3.2 同步定位與建圖 22
3.2.1 同步定位與建圖流程 22
3.2.2 Hector 同步定位與建圖 24
3.3 自適應蒙地卡羅定位 27
3.4 路徑規劃 31
3.4.1 全域路徑規劃 31
3.4.2 區域路徑規劃 33
四、系統運作流程與實驗結果 36
4.1 系統運作流程 36
4.2 地圖建置 37
4.3 機器人自身定位 44
4.3.1 雷射掃描定位 44
4.3.2 自適應蒙地卡羅定位 46
4.4 路徑規劃與移動控制 50
4.4.1 路徑規劃 50
4.4.2 通訊協議 55
4.4.3 移動控制 58
五、總結 65
5.1 結論 65
5.2 未來展望 65
參考文獻 67

參考文獻

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4.R. Negenborn "Robot localization and kalman filters" Utrecht Univ., Utrecht, Netherlands, Master′s thesis INF/SCR-0309 (2003)

5.M. Korkmaz, N. Yılmaz and A. Durdu "Comparison of the SLAM algorithms: Hangar experiments" MATEC Web of Conferences, Vol. 42 (2016)

6.F. Dellaert, D. Fox, W. Burgard and S. Thrun "Monte Carlo localization for mobile robots" Proceedings 1999 IEEE International Conference on Robotics and Automation, Vol.2, pp. 1322-1328 (1999)

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9.J. Leonard and H. Durrant-Whyte "Simultaneous map building and localization for an autonomous mobile robot" IROS, Vol. 3 (1991)

10.A. Davison, I. Reid, N. Molton, O. Stasse “MonoSLAM:Real-time single camera SLAM” Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 29, No.6, pp. 1052-1067 (2007)

11.G. Klein, D. Murray “Parallel tracking and mapping for small AR workspaces” In International Symposium on Mixed and Augmented Reality (ISMAR), pp.225-234 (2007)

12.R. Mur-Artal, J. Montiel, J. Tardos “ORB-SLAM: a versatile and accurate monocular SLAM system” Transactions on Robotics, Vol.31, pp.1147–1163 (2015)

13.G. Grisetti, C. Stachniss,W. Burgard “Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters” IEEE Transactions on Robotics, Vol.23, pp.34-46 (2007)

14.M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem” Proceedings of the AAAI National Conference on Artificial Intelligence, pp.593-598 (2002)

15.S. Kohlbrecher, O. von Stryk, J. Meyer and U. Klingauf "A flexible and scalable SLAM system with full 3D motion estimation" IEEE International Symposium on Safety, Security, and Rescue Robotics, pp.155-160 (2011)

16.W. Hess, D. Kohler, H. Rapp and D. Andor "Real-time loop closure in 2D LIDAR SLAM" In:2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, pp.1271-1278 (2016)

17.D. Wahyuningsih and E. Syahreza "Shortest Path Search Futsal Field Location With Dijkstra Algorithm" IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12.2, pp. 161-170 (2018)

18.A. Goyal, P. Mogha, R. Luthra and N. Sangwan "Path finding: A* or dijkstra’s?" International Journal in IT and Engineering 2.1, pp. 1-15 (2014)

19.D. Rachmawati and L. Gustin "Analysis of Dijkstra’s algorithm and A* Algorithm in shortest path problem" Journal of Physics: Conference Series, Vol.1566, No. 1 (2020)

20.D. Fox, W. Burgard and S. Thrun "The dynamic window approach to collision avoidance" IEEE Robotics & Automation Magazine, Vol.4, No.1, pp. 23-33 (1997)

21.光達三角測距示意圖 https://www.slamtec.com/en/News/Detail/190

22.光達三角測距示意圖 https://reurl.cc/kEVorn

23.ROS訊息傳遞架構圖 https://ithelp.ithome.com.tw/articles/10216423

24.SLAM流程圖 https://zhuanlan.zhihu.com/p/37161498

25.掃描匹配示意圖 J. Konecny, P. Kromer, M. Prauzek and P. Musilek "Scan matching by cross-correlation and differential evolution" Electronics 8.8: 856 (2019)

26.Dijkstra演算法與A*演算法比較圖 http://theory.stanford.edu/amitp/GameProgramming/AStarComparison.html

27.DWA運行示意圖 http://wiki.ros.org/dwa\_local\_planner

28.機器人座標系關係圖 http://wiki.ros.org/hector\_slam/Tutorials/SettingUpForYourRobot

29.navigation架構圖 https://wiki.ros.org/navigation/Tutorials/RobotSetup

30.代價地圖分層圖 D. Lu, D. Hershberger and W. Smart "Layered costmaps for context-sensitive navigation" IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2014)

31.UART傳輸示意圖 https://www.analog.com/cn/design-center/landing-pages/002/tech-articles-taiwan/uart-a-hardware-communication-protocol.html

指導教授

羅吉昌(Ji-Chang Lo)

審核日期

2022-8-25

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