期刊
APPLIED SCIENCES-BASEL
卷 11, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/app112411808
关键词
multi-task learning; deep learning; forklift; AGV
类别
资金
- National Research Foundation of Korea: BK FOUR
- National Research Foundation of Korea [NRF-2019R1A4A1024732]
- Institute of Information & Communications Technology Planning Evaluation [IITP-2020-0-01749]
- Korea Creative Content Agency [R2019020067]
This study introduces a vision sensor-based multi-task deep learning architecture that accurately predicts the rotation and displacement of pallets, providing necessary information for optimizing palletizing strategies in warehouse logistics.
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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