Architecting the Mars Returned Sample Handling System-of-Systems with Agile MBSE
James S. Wheaton, Paulo J. Younse · 2025
The future proposed Mars Sample Return Campaign aims to retrieve sample tubes containing Martian rock and gas samples from the Mars 2020 Perseverance Rover and return them to Earth for further scientific study. The Sample Receiving Project (SRP) currently in Pre-Phase A is responsible for receiving and curating the Martian samples, and has tasked the Mars Returned Sample Handling (MRSH) sub-project with developing key technologies for safely handling and opening the sample tubes inside double-walled isolators housed in the proposed Sample Receiving Facility (SRF). This paper presents the agile model-based systems engineering (MBSE) approach used in developing the SysML-based system architecture for the MRSH system-of-systems and its context. Our objectives were to quickly demonstrate the value of MBSE to NASA, JPL, and ESA stakeholders, and to improve the quality of project artifacts by leveraging MBSE early in the project life cycle. The agile MBSE methods we employed included: establishing a project lexicon, developing model-based requirements, developing tailored views and generating project artifacts, meta-modeling for project reuse, and model co-development using informal reviews for authoritative feedback prior to Mission Concept Review (MCR). In a period of 9 months, approximately 25,000 model elements, over 100 diagrams, and over 300 requirements and 50 requirement sets were developed for 9 (sub-)systems with hybrid human-robotic operations and interdependent interfaces across three cleanroom isolators. This lightweight MBSE approach focused on delivering an architecture model with diagrams that fit into a traditional system architecture framework while meeting the immediate needs of stakeholders where non-MBSE tools fell short. Results from the project validated commonly cited MBSE benefits such as improved communication, increased ability to manage system complexity, increased traceability, improved consistency, enhanced knowledge capture and reuse, and improved ability to teach and learn SE fundamentals.