Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7543
標題: 機器人沿牆走行為模式之模糊控制設計器設計
Design of a Fuzzy Controller for the Wall Following Behavior of a Robot
作者: 徐承佑
HSU, Cheng-You
關鍵字: Fuzzy Control;模糊控制;Neuro-Network;Robot Structure;Wall-Following Behavior;類神經網路;機器人架構;沿牆走行為模式
出版社: 電機工程學系所
引用: [1] 蘇木春, 張孝德, “機器學習-類神經網路、模糊系統以及基因演算法則,” 全華科技圖書股份有限公司, Jul. 2001 [2] R. A. Brooks, “A robust layered control system for a mobile robot,” IEEE Journal of Robotics and Automation, vol. 2, no. 7, pp. 14-23, 1986. [3] P. Rusu, E. M. Petriu, T. E. Whalen, A. Cornell, and H. J. W. Spoelder, “Behavior-based neuro-fuzzy controller for mobile Robot Navigation,” IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 4, pp. 1335-1340, Aug. 2003 [4] S. Thongchai, S. Suksakulchai, D. M. Wilkes, and N. Sarkar, “Sonar behavior-based fuzzy control for a mobile robot,” IEEE International Conference on System, Man, and Cybernetics, vol. 5, pp. 3532-3537, Oct. 2000 [5] T. Yata, L. Kleeman, S. Yuta, “Wall following using angle information measured by a single ultrasonic transducer,” IEEE International Conference on Robotics and Automation, vol. 2, pp. 1590-1596, May 1998 [6] S. Fazli and L. Kleeman, “Wall following and Obstacle avoidance results from a multi-DSP sonar ring on mobile robot,” IEEE International Conference on Mechatronics and Automation, vol. 1, pp. 432-437, Jul. 2005 [7] P. Van Turennout, G. Honderd, L. J. Van Schelven, “Wall following control of a mobile robot,” IEEE International Conference on Robotics and Automation, vol. 1, pp. 280-285, May 1992 [8] K. Izumi, K. Watanabe, S.-H. Jin, “Obstacle avoidance of mobile robot using fuzzy behavior-based control with module learning,” IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 454-459, Oct. 1999 [9] J. E. Meng and D. Chang, “Obstacle avoidance of a mobile robot using hybrid learning approach,” IEEE Transaction on Industrial Electronics, vol. 52, no. 3, pp. 898-905, Jun. 2005 [10] T.-H. S. Li, Shih-Jie Chang, and Wei Tong, “Fuzzy target tracking control of autonomous mobile robots by using infrared sensors,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 491-501, Aug. 2004 [11] E. Zalama, J. Gomez, M. Paul, and J. R. Peran. “Adaptive behavior navigation of a mobile robot,” IEEE Transactions on Systems, Man ,and Cybernetics-PART A: Systems and Humans, vol. 32, no. 1, pp. 160-169, Jan. 2002 [12] J. Yen, R. Langari, “Fuzzy logic intelligence, control, and information,” Prentice Hall, 1999 [13] K.-T. Song, Y.-S. Lin, “Landmark-based guidance of an autonomous vehicle using fuzzy logic velocity control and real-time computer vision,” Journal of the Chinese Fuzzy Systems Association, vol. 5, no. 1, pp. 43-51, 1999 [14] C. C. Wong, H. Y. Wang, S. A. Li, and C. T. Cheng, “Fuzzy controller designed by GA for two-wheeled mobile robots,” IEEE International Journal of Fuzzy Systems, vol. x, no. y, 2004 [15] C. Ye, N. H. C. Yung, and D. Wang, “A fuzzy Controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance,” IEEE Transactions on Systems, Man ,and Cyberetics-PART B:Cybernetics, vol. 33, no. 1, pp. 17-27, Feb. 2003 [16] H. R. Beom and H. S. Cho, “A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning,” IEEE Transactions on Systems ,Man ,and Cybernetics, vol. 25, no. 3, pp. 464-477, March 1995 [17] N. Rahman, A. R. Jafri, “Two layered behaviour based navigation of mobile robot in an unstructured environment using fuzzy logic,” IEEE International Conference on Emerging Technologies , pp. 230-235, Sep. 2005 [18] Y. Fu, H. Xu, S. Wang, J. Liu, H. Xu, and H. Li, “Mobile robot control based on fuzzy behavior and robot safety body in unknown environment,” IEEE International Symposium on Conputaional Intelligence in Robotics and Automation , pp. 547-552, Jun. 2005 [19] X. Xue, S. X. Yang, and M. Q.-H. Meng, “Remote Sensing and Teleoperation of a mobile robot via the internet,” IEEE Interational Conference on Information Acquisition , pp. 6, Jun. 2005 [20] K. C. Ng and M. M. Trivedi, “A neuron-fuzzy controller for mobile robot navigation and multirobot convoying”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no 6, pp. 829-840 , Dec. 1998 [21] C. Barret, M. Benreguieg, H. Maaref, “Fuzzy agents for reactive navigation of a mobile robot,” IEEE First International Conference on Knowledge-Based Intelligents Electronic Systems, vol 2, pp. 546-548, May 1997 [22] K.-T. Song and J. Y. Lin, “Behavior fusion of robot navigation using a fuzzy neural network,” IEEE Intermational Cnference on System, Man, and Cybernetics, Oct. 8-11, 2006 [23] S. X. Yang, Hao Li and M. Meng. “Fuzzy control of a behavior-based mobile robot,” The 12th IEEE International Conference on Fuzzy Systems, vol. 1, pp. 319-324 , May 2003 [24] WiRobot X80 User Manual, Dr. Robot [25] F.-J. Lin, C.-H. Lin, and P.-H. Shen, “Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 5, pp. 751-759, Oct. 2001 [26] J. S. Taur, G. H. Lee, C. W. Tao, “A two-stage design of adaptive fuzzy controllers for time-delay systems with unknown models,” IEEE 2003 [27] T. Das and I. N. Kar, “Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots,” IEEE Transactions on Control Systems Technology, vol. 14, no. 3, pp. 501-510, May 2006 [28] S.-T. Li, Y.-C. Li, “Neuro fuzzy behavior-based control of a mobile robot in unknown environments,” Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol 2, pp. 806-811, Aug. 2004 [29] F. M. Raimondi, M. Melluso, L. S. 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摘要: 
本論文之主旨在設計機器人以沿牆走行為模式之模糊控制系統,在未知環境下執行機器人沿牆走行為模式,並使機器人能依照各種環境變化來以各種相關的行為模式替換,例如:閃避障礙物、轉彎規劃、牆搜尋。行為架構為基礎的機器人能夠在環境平面繞牆行走。

本論文除了利用模糊控制器外,更提出了改善的方法,加入類神經網路的觀念,以倒傳遞演算法設計出可適應性的模糊控制器,以感測器收集周圍環境參數的資訊,依照這資訊來計算機器人的控制變數,並且用迴授的方式以增加錯誤函數藉著倒傳遞演算來增進整體系統的穩定度,並且探討此種適應性模糊控制器的各種變化。

最後,經由電腦模擬與實際導航操作,驗證本論文的設計具有更加改善的穩定效果。

The aim of this thesis is to design a fuzzy controller for a robot system, which is capable of executing the wall-following behavior and the adaptation under varying conditions, such as obstacle avoidance, making turns, and wall-searching. With these capabilities, the proposed robot is competent to walk along the walls in any environment. Furthermore, with the concept of neural network, an adaptive fuzzy controller is designed based on the back-propagation algorithm. Using the information obtained from the infrared sensor, the control output is computed. To improve the performance of the system, the error of the system is utilized to modify the parameters of the adaptive fuzzy controller according to the gradient descent updating rule. Through the simulation and experiments, the proposed robot system has been proved to be reliable and effective.

Through the simulation and experiments, the robot system proposed has been proved to be reliable and effective.
URI: http://hdl.handle.net/11455/7543
其他識別: U0005-1707200716041900
Appears in Collections:電機工程學系所

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