結論與未來發展 - 全方位移動平台網路化控制及感測融合實現

5.1 結論

根據以上的實驗結果，在此位本論文做出以下幾點結論：

1、全方位移動平台各軸控制誤差問題

首先分析單軸使用網路式與集中式的控制方式差異，並藉由導出四軸平台的運動模型，以inverse kinematics 技術實現全向性平台之運動控制器，提升了在直線加旋轉路徑上的追跡能力。將平台控制於集中式與網路式在不同取樣時間下，於低速的運動下，則兩者會有相當的表現，若於高速時，則必須仰賴較快的取樣週期，所以必須使用集中式控制。

2、Encoder 與影像感測融合

由於 Encoder 對於平台的打滑不容易偵測出來，影像對於距離上的偵測可能會因為光亮度的影響會產生不穩定的現象，因此加入Kalman Filter 來估測由影像所得到的位置，加入感測融合的機制，並使得平台移動穩定及準確性提升，於集中式與網路式的控制下，平台位置精密度約提升了90%。

3、實現目標物追蹤與遠端控制平台

使得平台可以追蹤所設定的目標物，並當目標物移動時，平台可以穩定又快速的追蹤至目標物，證明此平台可以快速且穩定追蹤到目標物。並且平台可以藉由網路傳輸，將平台上影像傳回給操作者觀看，操作者亦可傳送平台速度命令來操控平台，進而實現整體平台網路化控制。

5.2 未來發展

本論文位全方位移動平台建立了基礎的架構，包含影像與底層的控制部份，未來還有許多地方可以繼續研究發展下去，在此列出數點未來發展的方向：

1、全方位移動平台運動控制方面

目前平台控制器在XW、Y_W、φ 三個方向所設計的 PID 參數皆相同，這對於不同路徑下的運動控制並不夠穩健，若能加入非線性的控制器，如 Fuzzy[28]

等，具有協調、適應能力的控制，將提升運動控制得追跡能力。

2、馬達控制方面

目前馬達控制的方式為電壓命令與 RS-232 命令兩種，由於 RS-232 的傳輸速率有限，因此整體花費在傳輸上的時間居多，未來可以加入更快的網路控制系統(NCS)，如 CAN 等，都可以提高 NCS 的控制的精密度。

3、感測融合方面

影像所偵測到的目標物距離及角度可以輔助 Encoder 降低距離與角度的誤差，但由於影像容易受到光亮度的變動影響，因此必須要在光亮度穩定的區域中實驗才不會造太大的誤差，若是能加上其他感測器，如電子羅盤、加速度計等，可以與影像及 Encoder 結合，將可更加改善平台的穩定性與精確性。

4、目標物追蹤方面

目前由影像中偵測一目標物進行相對的定位，若能加上定位點偵測來進行判別，將可使平台可以做到整體的定位，將增加整體平台運動的精確性。

參考文獻

[1] J. Yu, S. Yu and H. Wang, “ Survey on the Performance Analysis of Networked Control Systems, ” IEEE International Conference on Systems, Man and Cybernetics, Vol. 6, pp. 5068-5073, October 2004.

[2] C. W. Cheng, C. L. Lai, B. C. Wang, and P. L. Hsu, “ The Time-delay Effect of Multiple-Network Systems in NCS, ” SICE Annual Conference 2007, 1C11-4, Sep.

18, 2007, Takamatsu JAPAN.

[3] F. G. Pin and S. M. Killough, “A New Family of Omnidirectional and Holonomic Wheeled Platforms for Mobile Robots,” IEEE Transactions on Robotics and Automation, Vol. 10, No. 4, pp. 480-489, 1994.

[4] K. Watanabe, “Control of an Omnidirectional Mobile Robot,” Proceedings of the 1998 IEEE Second International Conference on Knowledge-Based Intelligent Electronic Systems, Adelaide, Australia, pp. 51-60, 1998.

[5] K. Watanabe, Y. Shiraishi, S. G. Tzafestas, J. Tang and T. Fukuda, “Feedback Control of an Omnidirectional Autonomous Platform for Mobile Service Robots,” Journal of Intelligent and Robotic Systems, Vol. 22, No. 3, pp.315-330, 1998.

[6] J. Y. Choi, S. H. Ock, S. Kim and D. H. Kim, “Autonomous Omni-Directional Cleaning Robot System Design,” International Conference on Control, Automation, and Systems, Kintex, Korea, pp. 2019-2023, 2005.

[7] Mark Ashmore, Nick Barnes, “Omni-drive robot motion on curved paths：The fastest path between two points is not a straight-line,” Australian Joint Conference on Artificial Intelligence, Australia, pp. 225-236, 2002.

[8] C.C. Tsai, Li-Bin Jiang, Tai-Yu Wang, Tung-Sheng Wang, “Kinematics control of an Omnidirectional Mobile Robot,” Proceedings of 2005 CACS Automatic Control Conference Tainan, Taiwan, d-three pp.13~18, 2005.

[9] L. Huang, Y. S. Lim, David Li and Christopher E. L. Teoh, “Design and Analysis of a Four-wheel Omnidirectional Mobile Robot,” Proceedings of the 2nd International Conference on Autonomous Robots and Agents, Palmerston North, New Zealand, pp.425~428, 2004.

[10] M. Yachida, “Omnidirectional Sensing and Combined Multiple Sensing,”

Proceedings of 1998 IEEE and ATR Workshop on Computer Vision for Virtual Reality Based Human Communications, pp. 20-27, 1998.

[11] K. Kondo, Y. Yagi and M. Yachida, “Non-isotropic Omnidirectional Imaging System for an Autonomous Mobile Robot,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 1228-1233, 2005.

[12] M. Betke and L. Gurvits, “Mobile Robot Localization Using Landmarks,” IEEE Transaction on Robotics and Automation, Vol. 13, No. 4, pp. 251-263, 1997.

[13] Zi-Yuan Chen, Yu-Cheng Chen, Yin-Tien Wang, “Image-based Control of an Autonomous Robot with Omnidirectional Vision,” International Symposium on Industrial Embedded Systems, pp. 142-147, 2007.

[14] Mana Saedan, Chee Wang Lim and M. H. Ang, “Omnidirectional Image Matching for Vision-Based Robot Localization,” Proceedings of the 2006 IEEE International Conference on Mechatronics, pp.17-22, 2006.

[15] Yingjie Sun, Qixin Cao and Weidong Chen, “An Object Tracking and Global Localization Method using Omnidirectional Vision System,” Proceedings of the 5^th World Congress on Intelligent Control and Automation, Hangzhou, P.R. China, Vol. 6, pp. 4730-4735, 2004.

[16] N. Winters, J. Gaspar, G. Lacey and J. Santos-Victor, “Omni-directional Vision for Robot Navigation,” Proceedings of IEEE Workshop on Omnidirectional Vision, pp.

21-28, 2000.

Inference for Sensor Fusion,” Proceedings of 2004 IEEE International Conference on Fuzzy Systems, Vol. 1, pp. 305-310, 2004.

[18] http://www.kornylak.com/

[19] http://www.montrol.com.tw/

[20] http://www.ame-group.com/

[21] J. Bruce, T. Balch and M.Veloso, “Fast and Inexpensive Color Image Segmentation for Interactive Robots,” Proceedings of 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 3, pp. 2061-2066, 2000.

[22] Greg Welch and Gary Bishop, “An Introduction to the Kalman Filter,” Department of Computer Science University of North Carolina at Chapel Hill, NC 27599-3175, September 17, 1997.

[23] 莊孝麟, 全向性移動平台之精密運動控制設計, 國立交通大學電機與控制工程學系碩士論文, 2006.

[24] 陳致成, 智慧型CAN-based汽車雷達防撞警告系統, 國立交通大學電機與控制工程學系碩士論文, 2003.

[25] 黃文吉, “C++ Builder與影像處理,” 儒林, 2005.

[26] 連國珍, “數位影像處理,” 儒林, 1992.

[27] Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing,” Prentice-Hall, 2002.

[28] C. C. Shing, P. L. Hsu and S. S. Yeh, “T-S Fuzzy Path Controller Design with the Kinematics Inversion for an the Omnidirectional Mobile Robot,” accepted for the 32^nd Annual Conference of the IEEE Industrial Electronics society, Paris, France, 2006.

[29] M. S. Gast, 802.11 Wireless Networks: The Definitive Guide, O’Reilly,2002.

在文檔中全方位移動平台網路化控制及感測融合實現 (頁 81-85)