臺灣博碩士論文加值系統

本論文研製一組3D距離量測的雷射模組，功能上具備伺服馬達與雷射測距儀軟體上的獨立控制，使用者如果想要單獨使用其中一項儀器也能夠直接呼叫模組內的方法。此外也將馬達與感測器兩個儀器整合起來做為一個立體掃描模組，在整合使用與個別使用上有很大的延展性。
製作過程中分析幾何量化的運算方程式並提出一個量化的選擇方式，透過精確度的要求與掃描範圍的限制來選取2D雷射測距儀與預測系統能夠獲取到的精度。另外，也藉由分析發現選擇不同的旋轉軸來轉動2D雷射測距儀，能夠分別呈現與產生變化性與規律性的精確度結果。
軟體架構部份將分成三個部份，雷射測距儀、伺服馬達與3D距離量測模組，內容包含了馬達的控制功能，雷射測距儀的掃描功能與進階量測功能，研究過程的軟體開發平台使用Visual Studio 2008 C#來開發，實做成.NET介面，方便供給各種不同語言的程式開發者使用，儀器整合的硬體設計使用SolidWorks eDrawings機械繪圖軟體來做設計。
驗證部份將利用各種自然環境實際掃描出來的數據，感測器本身掃描的角度，馬達角度與障礙物距離來畫成一張黑白的3D立體圖形。

In this thesis, a 3D laser distance measurement module, function with servo motor and laser range finder independent control software on the user if you want to use one single instrument can also directly call module method. In addition, motors and sensors will also integrate the two instruments as a three-dimensional scanning module, the integrated use and individual use of the great scalability.
Quantify the production process of computing the geometric equation and make the choice of a quantitative way, through the precision the scope of requirements and restrictions to select scan 2D laser range finder and prediction system can get to accuracy. In addition, the analysis also found that by choosing different rotation axis to rotate 2D laser range finder, can produce variability were presented with precision and regularity results.
Part of the software architecture will be divided into three parts, laser range finder, servo motors and 3D distance measurement module includes a motor control function, scanning laser rangefinder and advanced measurement capabilities the research process of software development platform using Visual Studio 2008 C # to develop, really made of. NET interface to facilitate the supply of program development in various languages to use, integrated hardware devices designed for use with “SolidWorks eDrawings” mechanical drawing software for this design.
Validate some of the practical use of scanned data in the natural environment, the angle sensor itself is scanned, the motor angle and distance to obstacles the 3D drawing into a three-dimensional black and white graphics.

中文摘要 --------------------------------------------- i
英文摘要 --------------------------------------------- ii
誌謝 --------------------------------------------- iii
目錄 --------------------------------------------- iv
表目錄 --------------------------------------------- v
圖目錄 --------------------------------------------- vii
一、 緒論----------------------------------------- 1
1.1 前言----------------------------------------- 1
1.2 研究目的------------------------------------- 2
二、 系統架構------------------------------------- 3
2.1 簡介----------------------------------------- 3
2.2 雷射測距儀(LASER SCAN)----------------------- 4
2.3 伺服馬達(SERVOMOTOR) ------------------------ 12
三、 系統模組------------------------------------- 18
3.1 簡介----------------------------------------- 18
3.2 雷射測距儀模組------------------------------- 20
3.3 伺服馬達模組--------------------------------- 30
3.4 三維雷射掃描模組----------------------------- 34
四、 實作驗證與測試------------------------------- 45
4.1 簡介----------------------------------------- 45
4.2 測試方法------------------------------------- 45
4.3 測試結果------------------------------------- 54
五、 結論----------------------------------------- 58
參考文獻------------------------------------- 59
附錄----------------------------------------- 62

[1]Sebastian Thrun, Robotic Mapping: A Survey, Technique Report CMU-CS-02-111, School of Computer Science, Carnegie Mellon University, Pittsburgh, 2002.

[2]R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Transactions of the ASME - Journal of Basic Engineering, Vol. 82, pp. 35-45, 1960.

[3]D. Hahnel, D. Fox, W. Burgard, and S. Thrun, “A Highly Efficient FastSLAM Algorithm for Generating Cyclic Maps of Large-Scale Environments from Raw Laser Range Measurements,” Proceedings of the Conference on Intelligent Robots and Systems, pp. 206-221, 2003.

[4]A. P. Dempster, A. N. Laird, and D. B. Rubin, “Maximum Likelihood from Incomplete Data Via the EM Algorithm,” Journal of the Royal Statistical Society, Series B, Vol. 39, No. 1, pp. 1-38, 1977.

[5]B. Yamauchi, P. Langley, A. C. Schultz, J. Grefenstette, and W. Adams, “Magellan: An Integrated Adaptive Architecture for Mobile Robots,” Technical Report 98-2, Institute for the Study of Learning and Expertise (ISLE), Palo Alto, CA, May 1998.

[6]H. P. Moravec, “Sensor Fusion in Certainty Grids for Mobile Robots,” AI Magazine, Vol. 9, No.2, pp.61-74, 1988.

[7]R. Chatila and J. P. Laumond, “Position Referencing and Consistent World Modeling for Mobile Robots,” Proceedings of the 1985 IEEE International Conference on Robotics and Atomation, pp. 138-145, 1985.

[8]G. Welch and G. Bishop, An Introduction to the Kalman Filter, July 24, 2006.

[9]Lingemann. K, N?佟hter, A, Hertzberg, J, Surmann, H. “High-Speed Laser Localization for Mobile Robots,” Robotics and Autonomous System, Vol. 4, No. 51, pp. 275-296, 2005.

[10]林子閔，“超音波與感測器整合於導盲手杖定位之應用”，國立台灣科技大學機械工程系碩士學位論文，2009.

[11]M. Csorba, “Simultaneous Localisation and Map Building”, PhD thesis, University of Oxford, 1997.

[12]J. Guivant and E. Nebot, “Optimization of the Simultaneous Localization and Map Building Algorithm for Real Time Implementation,” IEEE Transaction on Robotic and Automation, Vol. 17, No. 3, pp. 242–257, 2001.

[13]J. S. Gutmann and K. Konolige, “Incremental Mapping of Large Cyclic Environments,” Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), pp. 318–325, 2000.

[14]J. J . Leonard and H. J. S. Feder, “A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization,” Proceedings of the Ninth International Symposium on Robotics Research, pp. 169–176, 2000.

[15]Y. Liu, R. Emery, D. Chakrabarti, W. Burgard, and S. Thrun, “Using EM to Learn 3D Models with Mobile Robots,” Proceedings of the International Conference on Machine Learning (ICML), 2001.

[16]F. Lu and E. Milios, “Globally consistent range scan alignment for environment mapping,” Autonomous Robots, Vol. 4, pp. 333–349, 1997.

[17]H. P. Moravec and M. C. Martin, “Robot Navigation by 3D Spatial Evidence Grids,” Mobile Robot Laboratory, Robotics Institute, Carnegie Mellon University, 1994.

[18]H. Shum, M. Han, and R. Szeliski, “Interactive Construction of 3D Models from Panoramic Mosaics,” Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 427–433, 1998.

[19]S. Thrun, W. Burgard, and D. Fox, “A Real-Time Algorithm for Mobile Robot Mapping with Applications to Multi-Robot and 3D Mapping,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA, 2000.

[20]U. R. Zimmer, “Robust World-Modeling and Navigation in a Real World,” Neurocomputing, Vol. 13, pp. 2–4, 1996.

[21]HOKUYO, “URG-04LX”, http://www.hokuyo-aut.jp/02sensor/07scanner/urg_04lx.html.

[22]DeltaSphere, “DeltaSphere-3000”, http://www.deltasphere.com/DeltaSphere-3000.htm.

[23]QuellTech, “Q4 Laser Scanner”, http://www.quelltech.de/DE/products-Q4.php.

[24]敏石系統, “SmartMotor伺服馬達”, http://www.montrol.com.tw/WEB/smatrMotor.htm.