Object detection in picking: Handling variety of a warehouse’s articles

Abstract

Purpose: The automation of picking is still a challenge as a high amount of flexibility is needed to handle different articles according to their requirements. Enabling robot picking in a dynamic warehouse environment consequently requires a sophisticated object detection system capable of handling a multitude of different articles. Methodology: Testing the applicability of object detection approaches for logistics research started with few objects producing promising results. In the context of warehouse environments, the applicability of such approaches to thousands of different articles is still doubted. Using different approaches in parallel may enable handling a plethora of different articles as well as the maintenance of object detection approach in case of changes to articles or assortments occur. Findings: Existing object detection algorithms are reliable if configured correctly. However, research in this field mostly focuses on a limited set of objects that need to be distinguished showing the functionality of the algorithm. Applying such algorithms in the context of logistics offers great potential, but also poses additional challenges. A huge variety of articles must be distinguished during picking, increasing complexity of the system with each article. A combination of different Convolutional Neural Networks may solve the problem. Originality: The suitability of existing object detection algorithms originates from research on automation of established processes in existing warehouses. A process model was already introduced enabling the transformation of laboratory trained CNNs to industrial warehouses. Experiments with CNNs according to this approach are published now.