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Fuzzy Controller Design by PSO for Two Mobile Robots Cooperation Control
|關鍵字:||Dead-cycle problem;死循環問題;fuzzy-system optimization;robot navigation;particle-swarm optimization (PSO);Cooperation;carry;模糊系統最佳化;機器人導航;粒子群聚最佳化(PSO);合作;搬運||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. 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本論文提出了以粒子群聚最佳化演算法（PSO），來設計模糊控制器（FC）在未知的環境中進行兩台移動式機器人合作導航控制。在導航問題中，一台領導機器人與另一台跟隨機器人合作搬運一個物體，並且同時進行跟隨障礙物邊緣(BF)和尋標的行為(TS)進而到達目標處。第一個課題中，PSO設計之FC被應用於控制單一機器人並且使用加強訊號學習跟隨障礙物邊緣的行為。此學習方法接著被應用於兩台合作型機器人的跟隨障礙物邊緣問題，另外跟隨機器人是藉由一個輔助型的FC來設計。跟隨障礙物邊緣的行為是在一個簡單的環境中學習，且事先沒有收集輸入-輸出的訓練參數，模糊控制器中所有的自由參數皆藉由粒子群聚最佳化演算法學習，以避免費時的手動設計課題。在此提出了對於兩個機器人合作尋標行為的規則。本論文也提出一種行為管理器，來整合合作跟隨障礙物邊緣行為和合作尋標的行為，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，兩台合作式移動機器人成功的搬運和導航，證明了以PSO設計之FC為基礎的導航是可行的。
This thesis proposes navigation of two cooperative mobile robots in unknown environments using a particle swarm optimization (PSO) designed fuzzy controller (FC). In the navigation problem, a leading and a following robot cooperatively carry an object and simultaneously perform either a cooperative boundary following (BF) or a cooperative target seeking (TS) behavior to reach a target. The PSO-designed FC is first applied to the control of a single robot for the obstacle BF learning using only reinforcement signals. The learning approach is then applied to address the BF problem of two cooperative robots, where an auxiliary FC is deigned for the following robot. A simple learning environment is created for building the BF behavior without an exhaustive collection of input-output training pairs in advance. All of the free parameters in the FC are learned through PSO, which avoids the time-consuming manual design task. A rule for coordination of the two robots in the cooperative TS behavior is proposed. A behavior supervisor is proposed to combine the learned cooperative BF behavior and the cooperative TS behavior for navigation, and the problem of dead cycles is considered. Successful navigation of two cooperative mobile robots carrying an object in several complex environments verifies the PSO-designed FC navigation approach.
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