@inproceedings{f23d18fff1b44972ab60f96e29d448e5,
title = "EHRCoI4: A Novel Framework for Enhancing Human-Robot Collaboration in Industry 4.0",
abstract = "This paper introduces a novel, cost-effective method for enhancing human-robot collaboration in Industry 4.0 manufacturing using the OpenCV AI Kit-Lite (OAK-D-Lite). Our vision-based system employs deep learning models for realtime human detection and dynamic adjustment of robot operations, ensuring safety without needing costly Lidar sensors. We further present an autonomous system utilizing You Only Look Once (YOLO) v5 that accurately identifies, classifies, and handles workpieces, effectively reducing human intervention. We demonstrate the solutions using a Lego Mindstorm kit-based robotic arm, highlighting the flexibility and adaptability of our approach. Our research contributes significantly to safer and economically efficient AI integration in industrial settings, marking a step forward for Industry 4.0 manufacturing.",
keywords = "Human robots interaction, In-dustry 4.0, Industrial Robotics, Industry 5.0, Picking and placing, smart environments",
author = "Iram Arshad and {Bharatwaj K R}, Roopesh and Alsamhi, {Saeed Hamood} and Edward Curry",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023 ; Conference date: 10-10-2023 Through 11-10-2023",
year = "2023",
doi = "10.1109/eSmarTA59349.2023.10293744",
language = "English",
series = "2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023",
address = "United States",
}
