Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mok, Chunghyup | - |
dc.contributor.author | Baek, Insung | - |
dc.contributor.author | Cho, Yoon Sang | - |
dc.contributor.author | Kim, Younghoon | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2022-02-12T16:41:13Z | - |
dc.date.available | 2022-02-12T16:41:13Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/135523 | - |
dc.description.abstract | As the need for efficient warehouse logistics has increased in manufacturing systems, the use of automated guided vehicles (AGVs) has also increased to reduce travel time. The AGVs are controlled by a system using laser sensors or floor-embedded wires to transport pallets and their loads. Because such control systems have only predefined palletizing strategies, AGVs may fail to engage incorrectly positioned pallets. In this study, we consider a vision sensor-based method to address this shortcoming by recognizing a pallet's position. We propose a multi-task deep learning architecture that simultaneously predicts distances and rotation based on images obtained from a visionary sensor. These predictions complement each other in learning, allowing a multi-task model to learn and execute tasks impossible with single-task models. The proposed model can accurately predict the rotation and displacement of the pallets to derive information necessary for the control system. This information can be used to optimize a palletizing strategy. The superiority of the proposed model was verified by an experiment on images of stored pallets that were collected from a visionary sensor attached to an AGV. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.3390/app112411808 | - |
dc.identifier.scopusid | 2-s2.0-85121211002 | - |
dc.identifier.wosid | 000735752300001 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.11, no.24 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 11 | - |
dc.citation.number | 24 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | AGV | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | forklift | - |
dc.subject.keywordAuthor | multi-task learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.