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Comparative analysis of model-based systems engineering and traditional systems engineering approaches for architecting robotic space systems through knowledge categorization, automatic information transfer, and automatic knowledge processing measures




Younse, Paulo, author
Bradley, Thomas, advisor
Borky, John, committee member
Sega, Ron, committee member
Reising, Steven, committee member

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Robotic space systems have enabled us to explore the far reaches of our solar system. However, these missions are high-cost, high-risk, and prone to accidents due to their complex nature. As these systems continue to grow even more capable and complex, spacecraft costs and mission success risk are also expected to grow. Current systems engineering approaches are finding it challenging to manage this growth in system complexity. Model-Based Systems Engineering (MBSE) offers techniques to aid in the development of complex systems, aiming to reduce design errors, reduce cost through prevention of costly rework, and improve system quality and project performance over traditional systems engineering techniques. Robotic space systems have much to benefit from an MBSE approach due to their intrinsic complexity, particularly if MBSE is implemented during the early architecting phase of the project. Case studies from the literature assert that there are benefits to using MBSE when applied to developing complex systems. However, none of these studies perform in-depth quantitative comparative analysis of applying MBSE vs. non-MBSE approaches, and there currently is a lack of substantial and compelling evidence to establish broad adoption of MBSE within the systems engineering community. This research measures the benefits of MBSE approaches over traditional, non-MBSE approaches for architecting robotic space systems though comparative analysis, focusing on quantitative evidence supporting how MBSE better describes, develops, and evaluates the system architecture, all which can aid in the adoption of MBSE within the robotics space systems domain. These advantages will be investigated through studying 1) how an MBSE approach better captures the information content for describing a robotic space system architecture relative to a non-MBSE approach, 2) how an MBSE approach reduces the implementation effort required to developing a robotic space system architecture relative to a non-MBSE approach, and 3) how an MBSE approach more efficiently evaluates a robotic space system architecture relative to a non-MBSE approach. A Mars orbiting sample Capture and Orient Module (COM) system for a Capture, Contain, and Return System (CCRS) payload concept for the notional Mars Sample Return (MSR) campaign develop at the NASA Jet Propulsion Laboratory was used as a case study to investigate the advantages of MBSE. The MBSE approach provided measurable advantages to architecting the COM robotic space system in terms of a higher fraction of formally captured architecture content in the appropriate knowledge category, a higher quantity of automatic information transfer between architecting tasks, and a higher quantity of automatic knowledge processing during modeling and simulation activities.


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knowledge management
modeling and simulation
system architecture
model-based systems engineering
cognitive psychology
robotic space systems


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