Doctoral Dissertations

Keywords and Phrases

Computer Vision; Industrial AI; Machine Learning; Smart Manufacturing

Abstract

"Advancements in sensors, computational intelligence, and big data have been rapidly transforming and revolutionizing the manufacturing sector, leading to robot-rich and digitally connected factories. To facilitate such a transformative process, workforce training and coordination between the human workforce and robots have remained a major challenge. This study aims to create intelligent workforce training systems for human-computer interaction and human-robot collaboration. It proposes the use of dynamic gestures and speech commands for seamless communication between a worker and a robot, leveraging Convolutional Neural Networks (CNN) and multiple threading for recognition and integration. Additionally, a fine-grained activity recognition model has been crafted using transfer learning and Recurrent Neural Networks (RNN) to accurately recognize and predict assembly operations. To further enhance workforce training, a gaze-oriented workforce training system using CNN and Long Short-Term Memory (LSTM) for step recognition, video transformers for repetitive action counting, and gaze estimation for real-time instructional guidance has been developed. The effectiveness of the proposed comprehensive workforce training methods and systems is underscored by promising outcomes and superior performance metrics, including gesture recognition accuracy above 98.00%, speech recognition accuracy over 95.00%, fine-grained activity recognition accuracy at 99.98%, and a 59.98% Off-By-One-Accuracy (OBOA) in repetitive action counting, surpassing current benchmarks. The real-time demonstrations have convincingly illustrated the practicality and efficiency of the proposed methods in human-robot collaboration and advancing workforce training" -- Abstract, p. iv

Advisor(s)

Leu, M. C. (Ming-Chuan)

Committee Member(s)

Bristow, Douglas A.
Chandrashekhara, K.
Song, Yun Seong
Yin, Zhaozheng

Department(s)

Mechanical and Aerospace Engineering

Degree Name

Ph. D. in Mechanical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2024

Pagination

xvii, 195 pages

Note about bibliography

Includes_bibliographical_references_(pages 25, 68, 112, 147, 183 & 191-194)

Rights

©2024 Haodong Chen , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12376

Electronic OCLC #

1460022203

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