ABSTRACT
Digital twins (DTs) provide virtual representations of both historical and potential stages of life of real-world entities, including systems and processes. Since they can be equipped with contexts and skills, DTs are likely to revolutionize how we develop, operate, and decommission physical robotic systems with agility, foresight, and efficiency in diverse applications. DTs form a central semantic unit, merging system knowledge from multiple domains and engineering disciplines. They introduce an evolving abstraction and are actionable for the technical and economic management of robotic assets. Also, DTs flourish in combination with modern simulation technology by revealing and establishing new values and potentials. Especially their operationalization for decision support is instrumental for the successful usage of robotic systems in ongoing as well as future applications. Despite this tremendous potential, a unifying methodology and infrastructure to realize DTs is hardly available yet. An integral part of using DTs in robotized applications is their formal description and dynamic simulation. Performances of robotic assets are thereby mirrored, enhanced, and quickly projected onto desired domains with low effort and costs. This chapter introduces a structuring approach for the development and employment of DTs to capture and give access to the dynamic behavior of robotized assets in operational environments. Advantages include cost-effective feasibility assessment and deeply informed decision-making. This chapter outlines core components of a simulation back-end that brings DTs to life. Three applications highlight the performance and usefulness of the framework to meet challenging objectives.
