Comparative analysis of a model-based systems engineering approach to a traditional systems engineering approach for architecting a robotic space system through knowledge categorization

This study compares the types and quantities of knowledge that are captured by a model-based systems engineering (MBSE) approach and a traditional architecting approach to measure the benefits of the MBSE approach in managing the complexity of a robotic space system. The MBSE approach was implemented with Cameo Systems Modeler using Systems Modeling Language (SysML) and applied to architecting an orbiting sample Capture and Orient Module (COM) system concept for a Capture, Containment, and Return System payload concept for potential Mars Sample Return. An architecture framework was established, covering system, subsystem, and assembly levels, along with structure, behavior, data, and requirements perspectives. The COM system architecture was captured in parallel using both the MBSE and non-MBSE approaches in order to provide a side-by-side comparison of the approaches. The approaches were evaluated based on how well each represented the information content of the COM system architecture. A total of 4389 knowledge elements were classified using the Revised Bloom's Taxonomy knowledge dimension and used to quantitatively compare the two approaches. The MBSE approach more completely captured architectural knowledge than the non-MBSE approach. Limitations to the SysML-based MBSE approach were also identified, including its ability to fully represent certain high-level conceptual, procedural, and metacognitive knowledge such as design principles, design approaches and rationales, risks, development strategies and rationales, organizational core competencies, and requirement verification methods. The overall results demonstrate the benefits of MBSE in managing the complexity of robotic space systems and strengthen the case for adopting MBSE within the systems engineering community.