Towards automating stocktaking in warehouses: Challenges, trends, and reliable approaches

被引:2
作者
Daios, Adamos [1 ]
Xanthopoulos, Alexandros [2 ]
Folinas, Dimitrios [1 ]
Kostavelis, Ioannis [1 ]
机构
[1] Int Hellen Univ, Dept Supply Chain Management, Katerini 60100, Greece
[2] Democritus Univ Thrace, Sch Engn, Dept Prod & Management Engn, Xanthi 67100, Greece
来源
5TH INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING, ISM 2023 | 2024年 / 232卷
关键词
supply chain; logistics; hybrid simulation; system dynamics; reinforcement learning; business process re-engineering; MODEL RETRIEVAL;
D O I
10.1016/j.procs.2024.01.142
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Stocktaking refers to the examination or counting over of materials, supplies, goods or products inside a stockroom, store, warehouse. There are many challenges concerning this important business function, which are even more demanding inside an unstructured large warehouse. Huge variance in size, volume, shape of the stock-keeping units that are stored in bulk without packaging, leads to many emerging problems that necessitate significant research endeavors so as to develop solutions that can automate this process. Nowadays, there are many approaches related to computer vision-based stocktaking in unstructured warehouses with products stored in bulk, which employ several existing technologies namely Deep Learning, Optical Character Recognition, and 3D model retrieval. The paper at hand aims at presenting the i-Count Air concept which outlines the utilization of Unmanned Aerial Vehicles, endowed with cognitive hardware sensors, active vision and an innovative perception and stocktaking software, that will push the boundaries of robotic perception and help automate the inventory counting function in a Warehouse 5.0 environment. (c) 2023 The Authors. Published by Elsevier B.V.
引用
收藏
页码:1437 / 1445
页数:9
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