本論文提出兩種機器人合作系統，分別是機器人同儕交互學習系統，以及物聯網自動包裝系統。在機器人同儕交互學習系統當中，具有相似功能的機器人被視為機器人同儕，它們具有獨立學習與決策的能力，但透過溝通各自的影像資訊、個人決策，以及自身狀態，它們可以制定共同決策以及所需執行的動作。而在此系統中，我們提出一個相互式類神經網路概念學習演算法，讓機器人在學習抽象概念時，可以交換類神經網路中的權重，提升彼此的學習成果。因此，機器人同儕不但可以合作完成複雜的任務，還可以幫助彼此提升學得更好。另一方面，基於工業4.0與物聯網的概念，我們提出一個可用於網路購物倉儲情境下的自動包裝系統，在此系統當中，顧客透過網路訂購商品後，系統將會收到訂單，並利用適應性粒子群最佳化3D包裝演算法，計算合適尺寸的箱子，以及商品在箱中擺放的位置。計算後的結果將同時傳送給機器人與機械手臂，讓它們可以透過溝通規劃彼此的動作，合作無間的從準備區拿取商品，並將商品包裝至箱子中。最後，模擬與實驗結果顯示本論文所提出之學習演算法以及機器人合作系統，可以讓機器人彼此溝通，共同完成指定的任務。

In this dissertation, we propose two systems of robot cooperation, a robot peer reciprocal learning system and an Internet-of-Things (IoT)-based automatic e-fulfillment packaging system, based on two different perspectives. The robot peer reciprocal learning system is inspired by human peer learning theories. In this system, similar function robots are treated as learning partners, they can not only cooperatively accomplish difficult tasks but also help each other to learn better. For learning a new concept, we also propose a mutual concept learning method which allows the robots to learn from each other by exchanging weights in their neural network concept learning system. On the other hand, the IoT-based automatic e-fulfillment packaging system is an implementation of the concept of Industry 4.0. In the system, all components are connected and monitored by a cyber network, and cooperatively accomplish a packaging task in a warehouse e-fulfillment situation. For packing all items inside a suitably sized box, we propose a 3D Adaptive Particle Swarm Optimization (PSO)-based packing algorithm. Finally, the simulations and the experiments demonstrate the efficiency of the proposed robot cooperation systems and learning algorithms.

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