Closed-Loop Systems Engineering (CLOSE): Integrating Experimentable Digital Twins with the Model-Driven Engineering Process.

Author(s):  
Marco Di Maio ◽  
George-Dimitrios Kapos ◽  
Niklas Klusmann ◽  
Linus Atorf ◽  
Ulrich Dahmen ◽  
...  
Author(s):  
Francis Bordeleau ◽  
Loek Cleophas ◽  
Benoit Combemale ◽  
Romina Eramo ◽  
Mark Van Den Brand ◽  
...  

2020 ◽  
Vol 70 (1) ◽  
pp. 54-59
Author(s):  
Zhi Zhu ◽  
Yonglin Lei ◽  
Yifan Zhu

Model-driven engineering has become popular in the combat effectiveness simulation systems engineering during these last years. It allows to systematically develop a simulation model in a composable way. However, implementing a conceptual model is really a complex and costly job if this is not guided under a well-established framework. Hence this study attempts to explore methodologies for engineering the development of simulation models. For this purpose, we define an ontological metamodelling framework. This framework starts with ontology-aware system conceptual descriptions, and then refines and transforms them toward system models until they reach final executable implementations. As a proof of concept, we identify a set of ontology-aware modelling frameworks in combat systems specification, then an underwater targets search scenario is presented as a motivating example for running simulations and results can be used as a reference for decision-making behaviors.


Author(s):  
Marco Di Maio ◽  
Linus Atorf ◽  
Ulrich Dahmen ◽  
Michael Schluse ◽  
Juergen Rossmann ◽  
...  

An adaptive system is any system that can self-conform according to changes that occur inhis environment. Self-adaptation includes self-reconfiguration, self-restructuring, self-repair, self-optimization or allat the same time. The realization of this kind of systems, in spite of the efforts made, suffers from a deficiency of engineering approaches. One of the most promising techniques in this quest is model-driven engineering. In the model-driven engineering paradigm, the model is the backbone of the systems engineering process. In this paper, we outline a model-based approach that offers a way to explicitly design self-adapting standard systems. We define it based on the UML profiling technique which allows to specify models for the most application domain frameworks. Through this profile we clearly define the components involved in the management of adaptation of systems, as well as the relationships between them. We present, for practical validation, an example application based on the approach.


Author(s):  
Giuseppina Lucia Casalaro ◽  
Giulio Cattivera ◽  
Federico Ciccozzi ◽  
Ivano Malavolta ◽  
Andreas Wortmann ◽  
...  

AbstractMobile robots operate in various environments (e.g. aquatic, aerial, or terrestrial), they come in many diverse shapes and they are increasingly becoming parts of our lives. The successful engineering of mobile robotics systems demands the interdisciplinary collaboration of experts from different domains, such as mechanical and electrical engineering, artificial intelligence, and systems engineering. Research and industry have tried to tackle this heterogeneity by proposing a multitude of model-driven solutions to engineer the software of mobile robotics systems. However, there is no systematic study of the state of the art in model-driven engineering (MDE) for mobile robotics systems that could guide research or practitioners in finding model-driven solutions and tools to efficiently engineer mobile robotics systems. The paper is contributing to this direction by providing a map of software engineering research in MDE that investigates (1) which types of robots are supported by existing MDE approaches, (2) the types and characteristics of MRSs that are engineered using MDE approaches, (3) a description of how MDE approaches support the engineering of MRSs, (4) how existing MDE approaches are validated, and (5) how tools support existing MDE approaches. We also provide a replication package to assess, extend, and/or replicate the study. The results of this work and the highlighted challenges can guide researchers and practitioners from robotics and software engineering through the research landscape.


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