This comprehensive resource provides systems engineers and practitioners with the analytic, design and modeling tools of the Model Based Systems Engineering (MBSE) methodology of Integrated Systems Engineering (ISE) and Pipelines of Processes in Object Oriented Architectures (PPOOA) methodology. This methodology integrates model based systems and software engineering approaches for the development of complex products, including aerospace, robotics and energy.

Novel robot technologies are becoming available to automate more complex tasks, more flexibly, and collaborating with humans. Methods and tools are needed in the automation and robotics industry to develop and integrate this new breed of robotic systems. In this paper, the ISE&PPOOA methodology for Model-Based Systems Engineering is applied for the development of robotic systems. The methodology is described through its application to reengineer a state-of-the-art collaborative robot application. The challenges that robotic systems present to model-based systems engineering are discussed, together with the benefits of MBSE methodologies.

There is an increasing demand for more autonomous systems. Enhancing systems with self-aware and self- adaptation capabilities can provide a solution to meet resilience needs. This article proposes a general design solution to build autonomous systems capable of run-time reconfiguration. The solution leverages Model-Driven Engineering with Model-Based Cognitive Control. The key idea is the integration of a metacontroller in the control architecture of the autonomous system, capable of perceiving the dysfunctional components of the control system and reconfiguring it, if necessary, at runtime. At the core of the metacontroller's operation lies a model of the system's functional architecture, which can be generated from the engineering modeling of the system.