Deep-Learning-Based Accurate Identification of Warehouse Goods for Robot Picking Operations

Huwei Liu, Li Zhou, Junhui Zhao, Fan Wang, Jianglong Yang, Kaibo Liang and Zhaochan Li
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Huwei Liu: School of Information, Beijing Wuzi University, Beijing 101149, China
Li Zhou: School of Information, Beijing Wuzi University, Beijing 101149, China
Junhui Zhao: School of Information, Beijing Wuzi University, Beijing 101149, China
Fan Wang: School of Information, Beijing Wuzi University, Beijing 101149, China
Jianglong Yang: School of Information, Beijing Wuzi University, Beijing 101149, China
Kaibo Liang: School of Information, Beijing Wuzi University, Beijing 101149, China
Zhaochan Li: School of Information, Beijing Wuzi University, Beijing 101149, China

Sustainability, 2022, vol. 14, issue 13, 1-16

Abstract: In order to explore the application of robots in intelligent supply-chain and digital logistics, and to achieve efficient operation, energy conservation, and emission reduction in the field of warehousing and sorting, we conducted research in the field of unmanned sorting and automated warehousing. Under the guidance of the theory of sustainable development, the ESG (Environmental Social Governance) goals in the social aspect are realized through digital technology in the storage field. In the picking process of warehousing, efficient and accurate cargo identification is the premise to ensure the accuracy and timeliness of intelligent robot operation. According to the driving and grasping methods of different robot arms, the image recognition model of arbitrarily shaped objects is established by using a convolution neural network (CNN) on the basis of simulating a human hand grasping objects. The model updates the loss function value and global step size by exponential decay and moving average, realizes the identification and classification of goods, and obtains the running dynamics of the program in real time by using visual tools. In addition, combined with the different characteristics of the data set, such as shape, size, surface material, brittleness, weight, among others, different intelligent grab solutions are selected for different types of goods to realize the automatic picking of goods of any shape in the picking list. Through the application of intelligent item grabbing in the storage field, it lays a foundation for the construction of an intelligent supply-chain system, and provides a new research perspective for cooperative robots (COBOT) in the field of logistics warehousing.

Keywords: warehouse picking; neural network; goods identification; intelligent grabbing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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