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標題: 進化式多目標最佳化之混合類神經網路控制器於多足機器人導航之應用
Evolutionary Multi-objective Optimization of Mixed Neural Network Controller for Hexapod Robot Navigation Application
作者: 陳彥銘
Yan-Ming Chen
關鍵字: 進化式
Neural Network
Hexapod Robot
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摘要: 本論文提出了一種用於六足機器人運動控制的混合類神經網路。該混合類神經網路內有一用於直線行走的七節點全連接遞迴類神經網路控制器,以及一用於沿著物體邊緣移動的雙節點感測器反饋類神經網路控制器。其中,七節點全連接遞迴類神經網路行走控制器以獨立的信號來控制機器人每一隻腳的髖部關節,以提高其行走之性能。雙節點感測器反饋類神經網路沿物體邊緣移動控制器使用少量的參數讓機器人實現偵測物體、並沿著其邊緣移動的功能,並採用一左右對稱之架構來建立完整的避障控制器,以減少訓練成本以及所需的神經網路空間。上述的兩種控制器,皆以設計的訓練環境與目標函數來搭配非支配排序基因演算法實現進化式參數學習。此外,一基於比例-積分-微分控制架構建立的尋標控制器用以與混合類神經網路避障控制器結合,以實現六足機器人的導航控制功能。最後,在訓練環境與各種測試環境下的模擬結果驗證了本論文提出之控制方法的效能。
This thesis proposes a mixed neural network (NN) for a hexapod robot locomotion control. The mixed NN consists of a seven-node fully connected recurrent NN (FCRNN) controller for straight forward walking and a two-node sensor-feedback NN (SFNN) controller for obstacle boundary following (OBF). The seven-node FCRNN locomotion controller controls each hip joint of the robot by independent signals to improve the walking performance. The two-node SFNN OBF controller uses a few parameters to realize the function of detecting an obstacle and walking along its boundary. A left-right symmetric structure is adopted to reduce training cost and NN model size in building a complete collision-avoidance controller. A training environment and multi-objective functions are designed to perform evolutionary parameter learning of the two controllers using the non-dominated sorting genetic algorithm-II (NSGA-II). Besides, a proportional-integral-derivative (PID)-based target searching (TS) controller is used to merge with the mixed NN collision-avoidance controller to realize the hexapod robot navigation. In the end, simulation results in training and test environments verify the effectiveness of the control methods proposed in this thesis.
文章公開時間: 2020-08-21
Appears in Collections:電機工程學系所



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