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Author: 陳星宇
Hsin-Yu Chen
Thesis Title: 智慧製造中的機器教學:以機器人組裝為研究案例
Machine Tutoring for Intelligent Manufacturing: A Case Study from Robot Assembly
Advisor: 鮑興國
Hsing-Kuo Pao
Committee: 項天瑞
Tien-Ruey Hsiang
Chuan-Kai Yang
Degree: 碩士
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 英文
Pages: 19
Keywords (in Chinese): 動作辨識智慧製造機器教學
Keywords (in other languages): Action Recognition, Intelligent Manufacturing, Machine Tutoring
Reference times: Clicks: 170Downloads: 3
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(IoT)進行資料的蒐集和人工智能 (AI) 來進行資料的分析,藉此來大幅
用戶需要分五個階段組裝 Meccanoid 機器人,系統會針對每個階段給予
適當的指導。我們會透過用戶手腕上的 IMU 來進行資料分析,並根據他
或是老手,藉此能更有針對性的給出建議。在本文中,我們會先將 IMU

Smart factory are already the trend in the industry. One of the focuses of
smart factory the industrial Internet of Things (IIoT) for data collection
and artificial intelligence (AI) for data analysis, thereby greatly improving
productivity. In this research, we focus on artificial intelligence and assist
assembly tasks in a factory. We simulate an assembly scenario in a factory
and designed a system to assist people in the assembly task. The user needs
to assemble the Meccanoid robot in five stages, and we give appropriate
guidance for each stage. To better understand users, we analyze the data
through the IMU on the user’s wrists and adjust the guidance. Our goal
is to make our system to better understand the user’s behavior and give
guidance, so we first perform action recognition and identity recognition
on the data. We divide the user’s actions into six categories and identify the
actions so that we can understand the user’s actions and behaviors. Identity
recognition identifies whether the user’s behavior pattern is more like a
novice or a master so that more targeted suggestions can be given. In this
paper, we first perform action recognition and identity recognition on the
data obtained by the IMU, and then sort out five features based on those
results. We give appropriate guidance based on these features. Finally,
we compare whether users can make progress through the guidance of our

1 Introduction 1.1 Intelligence manufacturing 1.2 Action Recognition in Manufacturing Assembly 1.3 Intelligent tutoring in robot assembly 2 Related Work 2.1 Inertial Measurement Unit (IMU) Sensor 2.2 Action Recognition 3 System Design 3.1 Dataset 3.2 System Architecture 3.2.1 Action recognition 3.2.2 Identity Recognition 3.2.3 Guide Classifier 4 Experiments and Results 4.1 Guide Classification Model 4.1.1 Action Recognition 4.1.2 Identity Recognition 4.1.3 Guide Classifier 4.2 Intelligent tutoring in robot assembly 4.2.1 Scenario 4.2.2 Result 5 Conclusions 6 Future work References

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