Visual AI Applications on Smart Delivery Units

Abstract

As actors in an IoT production environment, smart delivery units are tasked with identifying loaded components and acquiring shopfloor events such as consumption of material. Conventional identification procedures rely heavily on tags and markers that are applied on components. For processes that require marker-less identification procedures, AI-based object identification can be incorporated. In this paper, we present a novel integration of such visual applications on smart delivery units. We address the main challenges of this approach, namely the need for computational resources and integration with low-cost components. Additionally, we propose a scalable IoT concept for the distribution of the AI functionalities on those delivery units by utilizing containerized applications. We demonstrate the validity of this AI integration with a real-world implementation on delivery units, tested in an application near environment.