Integrating Skills into Digital Twins in Cooperative Systems

Abstract

Digital Twins (DTs) have been used as enablers for applications in optimization, monitoring, and other analyses along the lifecycle of their physical counterparts. In the robotics domain, DTs have been used as high-level components to connect robotic control with extended services. However, DTs do not necessarily include a structured taxonomy for the so-called operations, although it has been approached in the robotics domain, such as the skill-based engineering concept. Additionally, representing particular cases, such as cooperative robotics, is not an easy task, especially due to the complexity involved in system composition and coupled behavior. In this paper, we propose an extension to a modeling approach for composed DTs in cooperative systems that adopts the skill-based engineering concept. The extension ties to an ontological model that represents DTs in four components, namely, attributes, operations, behaviors, and relationships. The operation component is then extended by including the skill-based concept and inheriting the ontological properties of the base modeling approach. This contribution is evaluated using a case study with two cooperative robotic arms. The evaluation consists of two experiments, which are compared in two different settings, a manual fashion implementation and an implementation following this approach. The results show i) an improvement in terms of implementation effort and ii) enhanced reusability when switching to different tasks via the skill-based-enabled DTs.