An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

• Main Authors: Muslikhin Muslikhin, Jenq-Ruey Horng, Szu-Yueh Yang, Ming-Shyan Wang, Baiti-Ahmad Awaluddin
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Sensors
• Subjects: artificial intelligence of things; data-driven mode; deep learning; industry 4.0; modified YOLOv2; society 5.0
• Online Access: https://www.mdpi.com/1424-8220/21/8/2813

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.