Chu, Y., Feng, D., Liu, Z., Zhang, L. , Zhao, Z., Wang, Z., Feng, Z. and Xia, X. (2023) A fine-grained attention model for high accuracy operational robot guidance. IEEE Internet of Things Journal, 10(2), pp. 1066-1081. (doi: 10.1109/JIOT.2022.3206388)
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278188.pdf - Accepted Version 10MB |
Abstract
Deep learning enhanced Internet of Things (IoT) is advancing the transformation towards smart manufacturing. Intelligent robot guidance is one of the most potential deep learning+IoT applications in the manufacturing industry. However, low costs, efficient computing, and extremely high localization accuracy are mandatory requirements for vision robot guidance, particularly in operational factories. Therefore in this work, a low-cost edge computing based IoT system is developed based on an innovative Fine-Grained Attention Model (FGAM). FGAM integrates a deep-learning based attention model to detect the Region Of Interest (ROI) and an optimized conventional computer vision model to perform fine-grained localization concentrating on the ROI. Trained with only 100 images collected from real production line, the proposed FGAM has shown superior performance over multiple benchmark models when validated using operational data. Eventually, the FGAM based edge computing system has been deployed on a welding robot in a real-world factory for mass production. After the assembly of about 6000 products, the deployed system has achieved averaged overall process and transmission time down to 200 ms and overall localization accuracy up to 99.998%.
Item Type: | Articles |
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Additional Information: | This work was supported in part by the National Science and Technology Major Project under Grant 2020YFB1807601, the Shenzhen Science, and Technology Program under Grants JCYJ20210324095209025, and the Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under Grant SKLNST2020-1-11. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Zhang, Professor Lei |
Authors: | Chu, Y., Feng, D., Liu, Z., Zhang, L., Zhao, Z., Wang, Z., Feng, Z., and Xia, X. |
College/School: | College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity |
Journal Name: | IEEE Internet of Things Journal |
Publisher: | IEEE |
ISSN: | 2327-4662 |
ISSN (Online): | 2327-4662 |
Published Online: | 15 September 2022 |
Copyright Holders: | Copyright © 2022 IEEE |
First Published: | First published in IEEE Internet of Things Journal 10(2): 1066-1081 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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