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Target Detection-Based Control Method for Archive Management Robot

With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access...

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Detalles Bibliográficos
Autores principales: Yan, Cheng, Ren, Jieqi, Wang, Rui, Chen, Yaowei, Zhang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256058/
https://www.ncbi.nlm.nih.gov/pubmed/37300070
http://dx.doi.org/10.3390/s23115343
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author Yan, Cheng
Ren, Jieqi
Wang, Rui
Chen, Yaowei
Zhang, Jie
author_facet Yan, Cheng
Ren, Jieqi
Wang, Rui
Chen, Yaowei
Zhang, Jie
author_sort Yan, Cheng
collection PubMed
description With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access system with adaptive recognition for handling complex archive box access scenarios. The system comprises a vision component that employs the YOLOV5 algorithm to identify feature regions, sort and filter data, and to estimate the target center position, as well as a servo control component. This study proposes a servo-controlled robotic arm system with adaptive recognition for efficient paper-based archive management in unmanned archives. The vision part of the system employs the YOLOV5 algorithm to identify feature regions and to estimate the target center position, while the servo control part uses closed-loop control to adjust posture. The proposed feature region-based sorting and matching algorithm enhances accuracy and reduces the probability of shaking by 1.27% in restricted viewing scenarios. The system is a reliable and cost-effective solution for paper archive access in complex scenarios, and the integration of the proposed system with a lifting device enables the effective storage and retrieval of archive boxes of varying heights. However, further research is necessary to evaluate its scalability and generalizability. The experimental results demonstrate the effectiveness of the proposed adaptive box access system for unmanned archival storage. The system exhibits a higher storage success rate than existing commercial archival management robotic systems. The integration of the proposed system with a lifting device provides a promising solution for efficient archive management in unmanned archival storage. Future research should focus on evaluating the system’s performance and scalability.
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spelling pubmed-102560582023-06-10 Target Detection-Based Control Method for Archive Management Robot Yan, Cheng Ren, Jieqi Wang, Rui Chen, Yaowei Zhang, Jie Sensors (Basel) Article With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access system with adaptive recognition for handling complex archive box access scenarios. The system comprises a vision component that employs the YOLOV5 algorithm to identify feature regions, sort and filter data, and to estimate the target center position, as well as a servo control component. This study proposes a servo-controlled robotic arm system with adaptive recognition for efficient paper-based archive management in unmanned archives. The vision part of the system employs the YOLOV5 algorithm to identify feature regions and to estimate the target center position, while the servo control part uses closed-loop control to adjust posture. The proposed feature region-based sorting and matching algorithm enhances accuracy and reduces the probability of shaking by 1.27% in restricted viewing scenarios. The system is a reliable and cost-effective solution for paper archive access in complex scenarios, and the integration of the proposed system with a lifting device enables the effective storage and retrieval of archive boxes of varying heights. However, further research is necessary to evaluate its scalability and generalizability. The experimental results demonstrate the effectiveness of the proposed adaptive box access system for unmanned archival storage. The system exhibits a higher storage success rate than existing commercial archival management robotic systems. The integration of the proposed system with a lifting device provides a promising solution for efficient archive management in unmanned archival storage. Future research should focus on evaluating the system’s performance and scalability. MDPI 2023-06-05 /pmc/articles/PMC10256058/ /pubmed/37300070 http://dx.doi.org/10.3390/s23115343 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yan, Cheng
Ren, Jieqi
Wang, Rui
Chen, Yaowei
Zhang, Jie
Target Detection-Based Control Method for Archive Management Robot
title Target Detection-Based Control Method for Archive Management Robot
title_full Target Detection-Based Control Method for Archive Management Robot
title_fullStr Target Detection-Based Control Method for Archive Management Robot
title_full_unstemmed Target Detection-Based Control Method for Archive Management Robot
title_short Target Detection-Based Control Method for Archive Management Robot
title_sort target detection-based control method for archive management robot
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256058/
https://www.ncbi.nlm.nih.gov/pubmed/37300070
http://dx.doi.org/10.3390/s23115343
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AT zhangjie targetdetectionbasedcontrolmethodforarchivemanagementrobot