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Fuzzy Controller / Recurrent NN Design by Evolutionary Group-based PSO For Reinforcement Mobile Robot Control
|關鍵字:||PSO;粒子群聚最佳化;Fuzzy System;Hexapod Robot;Wheeled mobile robot;模糊系統;六足機器人;二輪機器人||出版社:||電機工程學系所||引用:|| C. F. Juang, “Combination of on-line clustering and Q-value based GA for reinforcement fuzzy system design,” IEEE Trans. Fuzzy Systems, vol. 13, no. 3, pp. 289-302, June 2005.  R. Alcalá, J. Alcalá-Fdez, J. Casillas, O. Cordón, and F. Herrera, “Local identification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems,” International Journal of Intelligent Systems, vol. 22, no. 9, pp. 909-941, Sep. 2007.  E. G. Mansoori, M. J. Zolghadri, S. D. Katebi, “SGERD: A steady-state genetic algorithm for extracting fuzzy classification rules from data,” IEEE Trans. Fuzzy Systems, vol. 16, no. 4, pp. 1061-1071, Aug. 2008.  A. Chatterjee, K. Pulasinghe, K. Watanabe, and K. Izumi, “A particle swarm-optimized fuzzy-neural network for voice-controlled robot systems,” IEEE Trans. Ind. Electron., vol. 52, no. 6, pp. 1478-1489, Dec. 2005.  C. F. Juang and C. 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本論文提出了一種進化群組粒子群聚最佳化演算法 （EGPSO），來設計模糊控制器（FC）和全連接遞迴類神經網絡（FCRNNs）。 EGPSO使用以群組為基礎的架構，將交配和突變加入到粒子群聚最佳化演算法裡。 EGPSO以動態的形式產生群組，並以群組的方式挑選要交配的母代、更新和取代粒子。 EGPSO並且引入一種新的適應性交叉速度突變（ACVMO），以提高搜索能力。 EGPSO並用於加強式零階 TSK型的FC和FCRNNs之自由參數設計。經由與其它群聚最佳化演算法之比較，結果顯示了EGPSO的有較好的設計能力。
EGPSO設計之FC已應用於機器人在未知環境之之導航，在此應用中，機器人透過EGPSO設計之FC學習跟隨障礙物邊緣的行為。這種行為是在一個未知環境中所建立，且事先沒有收集輸入-輸出的訓練參數。 本論文也提出一種行為管理器，來整合邊界跟隨行為和尋標的行為，以至於能夠在未知環境中導航以及解決死循環 (dead-cycle) 的問題。 在一些複雜的環境中，行動機器人成功的導航，證明了以EGPSO設計之FC為基礎的導航是可行的。 EGPSO設計之FCRNN則應用於六足機器人的移動控制學習。 利用加強式訊號，六足機器可自我學習六隻腳之步伐協調控制，以完成直線前進運動。最後，本論文展示了使用所提出的以EGPSO設計之FC為基礎的導航方法兩輪機器人在真實世界中導航的結果。以EGPSO設計之FCRNN亦實際應用在實體六足機器人的步乏控制。
This thesis proposes an evolutionary group-based particle swarm optimization (EGPSO) algorithm, for fuzzy controller (FC) and fully connected recurrent neural network (FCRNN) design. The EGPSO uses a group-based framework for incorporating crossover and mutation operations into particle swarm optimization. The EGPSO dynamically forms different groups for selecting parents in crossover operations, particle updates and replacements. A new adaptive cross-velocity-mutated operation (ACVMO) is incorporated to improve search ability. The EGPSO is applied to design all of the free parameters in a zero-order Takagi-Sugeno-Kang (TSK)-type FC and FCRNNs. The EGPSO performance is compared with different population-based optimizations in theses design problems and the results demonstrate the superiority of the EGPSO.
The EGPSO-designed FC is applied to mobile robot navigation in unknown environments. In this application, the robot learns the object boundary-following behavior through an EGPSO-designed FC. A simple learning environment is created for building this behavior without an exhaustive collection of input-output training pairs in advance. A behavior supervisor is proposed to combine the boundary-following behavior and the target seeking behavior for navigation, and the problem of dead cycles is considered. Successful mobile robot navigation in several complex environments verifies the EGPSO-designed FC navigation approach. The EGPSO-designed FCRNN is also applied to hexapod robot locomotion control learning. A hexapod robot successfully learns the coordination of gaits for movement in straight line using only reinforcement signals. Finally, this thesis presents practical wheeled mobile robot control results using the proposed robot navigation approach based on the EGPSO-designed FC. The EGPSO-design FCRNN is also practically applied to control the gaits of a real hexapod robot for forward movement.
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