精微產品組裝的智能人機協作系統

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姓名

楊仁皓(Jen-Hao Yang) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

精微產品組裝的智能人機協作系統
(Intelligent Human-Robot Collaboration System for Fine Product Assembly)

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摘要(中)

製造業正面臨著勞動力短缺的問題，同時也面臨訂單量少交期短的挑戰。傳統的生產方式已無法應對這些需求。在這種情況下，機器手臂成為了一個關鍵的角色。協作型機器手臂可以與人類協同作業，能適應「少量多樣」的生產模式。這些協作型機器手臂具有安全性、靈活性和人機協作等特點，能夠在同一工作場域與人類共同工作。儘管如此，協作型機器手臂的應用仍然面臨一些挑戰，例如缺少人類行為辨識的功能以及整合各種系統的需求。
本研究的目的是解決人類和機器人在共享工作空間中工作時的效率問題，所提出的智能人機協作系統使用立體相機進行人類行為辨識，並使用穿戴手環來檢測人類手勢。接著發展深度學習模型，依據相機和手環收集的數據識別人類行為，並控制機械臂輔助人類操作。結果證明了所提出的方法在即時識別人類行為方面的可行性和有效性，因此系統能夠更全面地理解使用者的動作意圖，並在合適的時間讓機器人介入組裝。這樣的系統設計能夠充份提升現有的人機協作應用中之人類行為辨識技術的性能，促進人與機器人能夠在同一場域中協同作業的可能性。

摘要(英)

The manufacturing industry is facing labor shortages and the challenge of low order volumes with shorter deliery. Conventional production methods are no longer able to cope with these demands, in which case robotic arms are becoming a key player. Collaborative robotic arms can work in tandem with humans and can adapt to the ′small amount, varied′ production model. These collaborative robotic arms are characterized by safety, flexibility, and human-machine collaboration, and are able to work with humans in the same workplace. Nevertheless, the application of collaborative robotic arms still faces some challenges, such as the lack of human behavior recognition and the need to integrate various systems.
The aim of this research is to address the efficiency of humans and robots when working in a shared workspace.The proposed intelligent human-robot collaborative system uses a stereo camera for human behavior recognition and a wearable bracelet to detect human gestures. Then a deep learning model is developed to recognize human behaviors using the data collected by the camera and the bracelet, in order to control a robotic arm to assist human operations. The results demonstrate that the feasibility and effectiveness of the proposed approach canreal-time recognize human behaviors in . There fore the system can understand the user′s movement intentions more comprehensively and allow the robot to intervene in the assembly at the right time. The proposed system can improve the lack of human behavior recognition in existing human-robot collaborative applications, enabling humans and robots to work together in the same field.

關鍵字(中)

★ 人機協作
★ 手勢辨識
★ 人類行為辨識
★ 深度學習

關鍵字(英)

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 X
第一章、緒論 1
1-1 研究背景 1
1-2 文獻回顧 5
1-3 研究動機 11
1-4 論文架構 12
第二章、相關技術介紹 13
2-1 ROS機器人作業系統 13
2-2 深度學習技術 14
2-3 人體動作辨識 23
第三章、研究方法 24
3-1 系統架構 24
3-2 手勢辨識模組 25
3-3 人類動作辨識模組 31
3-4 決策模組 33
第四章、實驗設計與結果 34
4-1 實驗設備 34
4-2 實驗設計 39
4-3 模型訓練 50
4-4 實驗結果 54
第五章、討論 75
5-1 人類動作辨識模組 75
5-2 手勢辨識模組 75
5-3 決策模組 77
第六章、結果與討論 78
6-1 具體貢獻 78
6-2 限制與範圍 79
6-3 未來展望 79
參考文獻 81

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指導教授

林錦德(Chin-Te Lin)

審核日期

2023-7-27

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