Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years

Specification and Verification of Collaborative Transport Robots

A collaborative embedded system is an intelligent agent in a cyber-physical system which cooperates with others by negotiation to fulfill individual and common goals. Examples are self-driving cars, soccer-playing robots, or adaptive production plants. In this contribution, we present the industrial case study of autonomous transport robots in factory environments. In our setting, the robots collaborate by competing for transport jobs issued by the production machines. Each robot calculates its individual cost incurring with the job (in terms of distance, time, energy, wear and tear, etc.) and places a bid based on this cost. Then a distributed voting takes place, where the lowest cost bid wins the job. Here, we present our results of specifying and verifying this scenario. We collected requirements via user stories for the scenario, formulated these in suitable specification languages, designed executable models as simulation environments, and used statistical model checking and runtime monitoring for analysing the scenario. We argue that for different aspects of the case study, different analysis methods are to be used. However, all of these methods can make use of the fact that the goals of the individual agents coincide. Our results indicate that by the individual optimization of the cost function, reliability and performance of the collaborative group increases. We believe that this result is typical for a large number of similar systems