A Systems Engineering Methodology for System of Autonomous Systems: Architecture and Integration

ABSTRACT Artificial intelligence and machine learning (AI/ML) rapidly transform systems by providing autonomous capabilities. This new class of systems can become a constituent system in a system of systems (SoS) to evolve it into a system of autonomous systems (SoAS). SoAS is fraught with new systems engineering (SE) challenges for architecture development, integration, testing, and evaluation that originate from the level of autonomy (LoA). The LoA refers to the level of autonomous capabilities of a system depending on its AI/ML technology. This paper examines SoAS architecture and integration challenges such as interface compatibility, safety, and security. We propose a model‐based systems engineering (MBSE) method for architecture development that falls under the system engineering for AI (SE4AI) umbrella, where SE principles are tailored to accommodate challenges posed by the integration of autonomy. The proposed method builds upon the object‐oriented systems engineering method (OOSEM) and modifies it to facilitate autonomy integration by leveraging the MBSE SWOT (i.e., Strength , Weakness , Opportunity , Threat ) analysis and SoAS taxonomy. It also tailors the unified architecture framework (UAF) to develop SoAS architectures with varying LoAs. This study leads to generating necessary evaluation data for a trade study and selecting an architecture with the most suitable LoA. We also present a conceptual example of a search‐and‐rescue SoS to demonstrate the implementation and effectiveness of the proposed method in investigating the evolution of LoA in constituent systems.