機器人合作在機器人研究領域，是一個重要且具未來性的議題。本論文提出一基於類神經網路演算法之相互式學習法，讓機器人透過溝通與分享自我學習的成果，提升彼此概念學習的能力，讓人們可以建構複雜的想法，相互溝通，以及理解並預測這個世界。讓機器人具備學習概念的能力，無異是朝向智慧化與人性化的一大步。本論文提出之互相學習類神經網路(MLNN)系統，讓機器人具備學習概念，以及合作與溝通的能力，並實現在智慧箱任務中。在該任務當中，機器人需辨識出桌上物件之形狀與顏色，並將物件投入由另一隻機器人所拿之智慧箱之相對應洞口。互相學習類神經網路，是由倒傳遞的類神經網路(BPNN)所衍生。視覺系統使用HSV 過濾背景，找出顏色與形狀，再利用雙線性方法讓影像正規化。機器人可透過TCP/IP通訊系統互相通訊，分享彼此的辨識結果，並規劃手臂動作。互相學習類神經網路系統，可同時更新雙機器人之類神經網路系統中的權重值。系統比較雙機器人之辨識結果，與過去辨識之正確性，並做出選擇。具有較高正確性的機器人，會將自身學習的權重值，傳送給另一隻機器人，以達到相互學習之目的。經由多次的學習過程，機器人將可以成功地建立概念。本篇論文所提出的方法，透過Matlab模擬學習過程，並且實際運用在兩隻機器人上。實驗的結果，證明本方法之可行性與有效性。

Multi-robot cooperation is an important issue in robotics. This thesis proposes a self and mutual concept learning algorithm based on Neural Network (NN) for robot cooperation. Robots learn a concept not only by themselves but also from each other, and they cooperate to complete a complicated task, that of Form Fitter. In the form fitter game, one robot explores the textures of shapes and grasps a shape on the desk to put on a box held by another robot. The Mutual Learning Neural Network (MLNN) system evolved from the Backpropagation Neural Network (BPNN). This visual system extracts the color and shape of objects by HSV and normalizes the images by bilinear interpolation. Robots utilize the TCP/IP communication system to communicate with each other and generate a series action of arm. The MLNN system updates both weights in the neural network system of each robot at the same time. The system compares the recognition results of both robots and chooses the better one. The robot which has greater accuracy will translate its learning weight to the other one to improve the performance of both robots. After learning many times, both robots can learn a concept. Finally, the proposed method is simulated by Matlab and demonstrated in two home service robots. The experimental results show the efficiency and feasibility of the MLNN system.

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