Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9234
標題: 多台合作型機器人的進化模糊控制與導航
Evolutionary Fuzzy Control and Navigation of Multiple Cooperative Robots
作者: 賴明志
Lai, Ming-Zhi
關鍵字: 多台合作型機器導航;Navigation of Multiple Cooperative Robots;進化模糊控制;Evolutionary Fuzzy Control
出版社: 電機工程學系所
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摘要: 
本論文提出了未知環境中多台輪式機器人合作搬運物體的導航方法。在導航過程中,多台機器人合作進行沿牆走或尋標行為以到達目標。論文提出兩台與三台機器人執行合作沿牆走行為的進化模糊控制法,並以自適應融合的連續型蟻群與粒子群體最佳化演算法(AF-CACPSO)來完成學習。模糊控制器中的所有自由參數是透過AF-CACPSO來學習,如此可避免耗時的手動設計任務。AF-CACPSO設計的模糊控制器首先應用於訓練環境中單一台機器人沿牆走行為控制。此學習方法接著用來解決兩台與三台合作型機器人的合作沿牆走,其中AF-CACPSO是用來設計其餘機器人的輔助模糊控制器。在合作尋標行為上,論文提出了兩台與三台機器人的協調規則。於導航中,論文提出合作行為的管理者以協調所學習的合作沿牆走與合作尋標行為,並考慮並避免了死循環的問題。AF-CACPSO的效能是透過比較各個基於群體為基礎的優化演算法於合作沿牆走行為學習的好壞來驗證。兩台與三台合作機器人於模擬與實驗中的成功導航驗證了提出的所提進化模糊控制與導航方法的有效性。

This paper proposes navigation of multiple wheeled mobile robots cooperatively carrying an object in unknown environments. In the navigation process, multiple robots cooperatively perform either an obstacle-boundary-following (OBF) or a target seeking (TS) behavior to reach a target. Evolutionary fuzzy control of two/three robots in executing the cooperative OBF behavior through adaptive fusion of continuous ant colony and particle swarm optimization algorithms (AF-CACPSO) is proposed. All of the free parameters in a fuzzy controller (FC) are learned through the AF-CACPSO, which avoids the time-consuming manual design task. The AF-CACPSO-designed FC is first applied to the control of a single robot for the OBF behavior in a training environment. The learning approach is then applied to address the cooperative OBF problem of two/three cooperative robots, where auxiliary FCs for the other robots are designed using the AF-CACPSO. For the cooperative TS behavior, a rule for coordination of the two/three robots is proposed. In navigation, a cooperative behavior supervisor is proposed to coordinate the learned cooperative OBF behavior and the cooperative TS behavior, where the problem of dead cycles is considered. Performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms (POAs) in the cooperative OBF behavior learning problem. Successful navigation of two/three cooperative robots in simulations and experiments verify effectiveness of the proposed evolutionary fuzzy control and navigation approaches.
URI: http://hdl.handle.net/11455/9234
其他識別: U0005-1608201317084500
Appears in Collections:電機工程學系所

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