近年來在工業4.0的推動下，建立智慧工廠成為業界共同的目標，力求產線自動化、資訊數位化、流程智慧化。德國BOSCH Rexroth所建立的mMS4.0系統涵蓋智慧製造中的各式應用，主要由三個彼此相連的工作站展示組合件的檢測、組裝、倉儲的一條龍產線，儼然是一個縮小版的智慧工廠，然而考量到實際場域中的工作站的離散配置，mMS4.0系統的架構顯得過於理想，因此本研究提出一個以機械手臂應用為核心之智慧工廠架構，並將智慧工廠的應用情境以機械手臂為主體進一步延伸出機械手臂運動模擬、機械手臂整合視覺、感測器應用與機台狀態監控、多機整合與人機互動等四個研究分支。在機械手臂運動模擬中，本文針對利茗機械手臂進行運動學與動力學分析，並建立三維模擬介面與動態模型，接著針對機械手臂與視覺的整合，本研究以間接校正的方式將機器人座標系與視覺座標系疊合，進而形成一個視覺引導系統，在感測器應用與機台狀態監控的分支，本研究除了針對感測器發展延伸應用，亦以既有軟體工具建立監控面板，將內部機台資訊與外部感測器訊號進行整合，針對多機整合與人機互動的部分，本研究透過物聯網工具建立機台間的資料交換機制，並以利茗機械手臂與無人搬運車的搬運情境為例進行實現，同時引入外部感測器使機械手臂取得感知人員靠近的功能，建立智慧工廠中人機互動的安全機制。綜合以上的研究結果，本文將機械手臂作為主要載台，以較低的成本實現智慧工廠的要素，期望本研究的方法與成果在未來可作為中小企業在使用機械手臂升級產線時的參考。

A modified framework of smart factory centering on industrial robot applications is proposed in this work. Under this framework, the scenarios of smart factory are realized by implementing dynamic modelling, vision guidance, sensor networking, status monitoring, multi-machine integration, and human-robot interaction. For dynamic modelling of robot manipulators, a 3D simulation interface and a dynamic model are built based on kinematic and dynamic analysis. For integrating robot manipulator with vision system, an indirect calibration method is conducted to set up a reference frame for both the robot system and the vision system, resulting in a vision guided robot system. For sensor networking and status monitoring, sensor fusion is studied as well as a dashboard is built for integrating internal information from machine controllers with external data from sensors. To achieve multi-machine integration and human-robot interaction, the IoT platforms are utilized for establishing the communication mechanism among different equipment. Furthermore, the industrial robot and automated guided vehicle are integrated to implement an object transportation scenario. In addition, external sensors are integrated so that the industrial robot can sense the approaching of human beings. Hence, the safety mechanism for the human-robot interaction can be established. Eventually, by using industrial robot as a platform, the smart factory scenarios can be realized. It is believed that the methodologies and results developed in this work can be applied to fulfill smart factory in a pragmatic way in the future.

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