Intralogistics 4.0, a key component of Industry 4.0, requires driverless transport systems such as autonomous mobile robots to operate reliably and flexibly in increasingly complex production environments. However, the today’s manual setup of the Robot Operating System 2 and Software stacks is still very time-consuming, error-prone and dependent on skilled workers. We present an agent-based approach that automates both the installation and configuration processes, as well as the system integration into the factory architecture. Our novel Resource Initializer Agent is able to initialize an blank autonomous mobile robot from a minimal operating system to a fully operational robot. By referencing the required software parts within the Asset Administration Shell Submodels, we can automatically install the runtime system (ROS2), configure its software (launch, parameter files, maps, etc.) and also open interfaces to integrate it into the overall multi-agent system without manual operations. Initial results successfully demonstrate that our approach significantly reduces commissioning time and integration risk compared to traditional manual methods. It also lays the foundation for further enhancements such as dynamic reconfiguration of autonomous mobile robot fleets in modular production setups.

Modern Digital Twin standards and technologies, designed to enable smart factories in line with the Industry 4.0 vision, have not yet addressed all the challenges associated with contemporary digital twins. This paper shows how to leverage the Asset Administration Shell as an Industry 4.0 compliant Digital Twin technology to provide all necessary data for Artificial Intelligence Planning, represented via the Planning Domain Definition Language. For this purpose, a model for preconditions and effects of planning actions is defined. This model is applied to three different use cases showing different problems of fetching data from different partially standardized shell templates including three defined actions of transport, assembly, and storage.

Industrial halls consume a large part of the heat energy resources but are only a tiny part of the building infra-structure. Improving heating efficiency in this sector will signif-icantly contribute to reach climate neutrality. Therefore, a digi-tal twin backend architecture is developed using the Asset Ad-ministration Shell framework to generate a digital twin. This has created a new data basis that lays the foundation for a score for the comparability of heating systems in terms of efficiency. In addition, some client services are implemented, such as visu-alizing the data in dashboards and generating alerts. Another service is a planning support application via augmented reality. The first tests in an industrial scenario show that using the dig-ital twin can reduce energy consumption and improve resource efficiency. Due to the containerized architecture of the gener-ated twin, it can be used as a blueprint for other digital twins of heating systems.

Industrial testbeds are spawning all across Europe, focusing on diverse topics. In our Human-Robot-Collaboration lab, we are researching how the lives of people in production can be made simpler through the use of collaborative robots, but still safe against the backdrop of the necessary changeability. Standardized digital twins based on Asset Administration Shells embedded in a service-oriented architecture are an ideal underlying information technology for this endeavor. In this paper, we describe such an infrastructure and outline its use based on two different use cases. We are convinced that this is not only relevant for testbed operators who want to achieve technical and semantic interoperability of their facilities, but can also serve as a blueprint for the implementation of Industrie 4.0 in small and medium-sized enterprises.