Skill-based robotic reconfigurable cyber-physical production modules (RCPPMs) have an aggregated structure, and their functionality depends on the submodules and components that define their current topology. This application-oriented paper proposes a method for integrating the OPC UA Relative Spatial Location (RSL) Companion Specification models and the ROS2 tf2-library functionality to automatically update the SRL models of the RCPPMs when their structure changes. This enables the proper composition and sharing of the necessary information regarding the kinematic structure of each RCPPM’s submodule. On an example, we illustrate how this approach can be employed to enhance the coupling functionality of the skill-based robotic RCPPMs.

While the benefits of reconfigurable manufacturing systems (RMS) are well-known, there are still challenges to their development, including, among others, a modular software architecture that enables rapid reconfiguration without much reprogramming effort. Skill-based engineering improves software modularity and increases the reconfiguration potential of RMS. Nevertheless, a skills' composition framework with a focus on frequent and rapid software changes is still missing. The behavior trees (BTs) framework is a novel approach that enables the intuitive design of modular hierarchical control structures. BTs have been mostly explored from the AI and robotics perspectives, and little work has been done in investigating their potential for composing skills in the manufacturing domain. This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules (RCPPMs). It is based on distributed BTs and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks. We have implemented the provided models for the IEC 61499-based distributed automation controllers. This shows the instantiation of the proposed framework with specific industrial technology to enable its evaluation by the automation community.

To improve the fault tolerance of production systems against unforeseen events, modern production systems must be able to react more autonomously with suitable solutions to reach their goals. Such events within the company, e.g., resource breakdowns, require the possibility of internal replanning of the production schedule. Multi-Agent Systems (MAS) can provide this required flexibility. To enable standardized interaction for agents, a vendor-independent description of products and production equipment, as well as their capabilities and skills, must be developed. The Asset Administration Shell (AAS) provides the standardized meta-model to achieve this goal. In this paper, a product AAS and the submodel for the required production plan are designed. Moreover, the associated MAS and interaction for the execution and replanning of the production sequence are presented. The system is validated using an assembly use-case in the real-world demonstrator environment of the SmartFactoryKL. Combining the concepts of MAS with the standardized structures of the AAS, skills and capabilities tackles the problem of MAS to be deployed in different companies. This approach enables widespread use and scalability of MAS leveraging their advantages in terms of flexibility and efficient reconfiguration.

A skill-based engineering approach uses the concept of skill to abstract machine-specific functionality with a generic interface and common behavior. A skill is treated as a "control level service" and is used to build service-oriented architectures. Though the question of robust and flexible coordination of skills is still open. Current approaches to skills' composition result in tightly-coupled control structures. To allow frequent and rapid system’s reconfiguration a framework to flexibly build complex behaviors from reusable components is needed. Behavior Trees (BTs) have proven to be a powerful tool for specifying characters' behaviors in the gaming industry and recently is getting a lot of attention in the robotics community as well as academia. An approach of using BTs for coordination of industrial skills is proposed in this paper. Combining behavior trees with the existing skill models enhances separation of concerns and modularity of control systems in the factory automation domain. Firstly, we define the requirements for a skills coordination layer and discuss some key properties of BTs that allow to satisfy these requirements. Secondly, we introduce a skill model, which utilizes BTs and has several advantages over the current skill models. Thirdly, we introduce a concept of distributed BTs to effectively distribute functionality over different computational platforms. Finally, we present a small proof of concept example to test feasibility of the proposed approach.