Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles
- Authors
- Mok, Chunghyup; Baek, Insung; Cho, Yoon Sang; Kim, Younghoon; Kim, Seoung Bum
- Issue Date
- 12월-2021
- Publisher
- MDPI
- Keywords
- AGV; deep learning; forklift; multi-task learning
- Citation
- APPLIED SCIENCES-BASEL, v.11, no.24
- Indexed
- SCIE
SCOPUS
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 11
- Number
- 24
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/135523
- DOI
- 10.3390/app112411808
- ISSN
- 2076-3417
- 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.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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