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An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0's Context.
Muslikhin, Muslikhin; Horng, Jenq-Ruey; Yang, Szu-Yueh; Wang, Ming-Shyan; Awaluddin, Baiti-Ahmad.
Afiliación
  • Muslikhin M; Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan.
  • Horng JR; Department of Electronics Engineering Education, Universitas Negeri Yogyakarta, Yogyakarta 55281, Indonesia.
  • Yang SY; Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan.
  • Wang MS; Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan.
  • Awaluddin BA; Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan.
Sensors (Basel) ; 21(8)2021 Apr 16.
Article en En | MEDLINE | ID: mdl-33923702
ABSTRACT
In this study, an Artificial Intelligence of Things (AIoT)-based automated picking system was proposed for the development of an online shop and the services for automated shipping systems. Speed and convenience are two key points in Industry 4.0 and Society 5.0. In the context of online shopping, speed and convenience can be provided by integrating e-commerce platforms with AIoT systems and robots that are following consumers' needs. Therefore, this proposed system diverts consumers who are moved by AIoT, while robotic manipulators replace human tasks to pick. To prove this idea, we implemented a modified YOLO (You Only Look Once) algorithm as a detection and localization tool for items purchased by consumers. At the same time, the modified YOLOv2 with data-driven mode was used for the process of taking goods from unstructured shop shelves. Our system performance is proven by experiments to meet the expectations in evaluating efficiency, speed, and convenience of the system in Society 5.0's context.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Taiwán

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Taiwán
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