Abstract
Model-based systems engineering (MBSE) has made significant strides in the last decade and is now beginning to increase coverage of the system life cycle and in the process generating many more digital artifacts. The MBSE community today recognizes the need for a flexible framework to efficiently organize, access, and manage MBSE artifacts; create and use digital twins for verification and validation; facilitate comparative evaluation of system models and algorithms; and assess system performance. This paper presents progress to date in developing a MBSE experimentation testbed that addresses these requirements. The current testbed comprises several components, including a scenario builder, a smart dashboard, a repository of system models and scenarios, connectors, optimization and learning algorithms, and simulation engines, all connected to a private cloud. The testbed has been successfully employed in developing an aircraft perimeter security system and an adaptive planning and decision-making system for autonomous vehicles. The testbed supports experimentation with simulated and physical sensors and with digital twins for verifying system behavior. A simulation-driven smart dashboard is used to visualize and conduct comparative evaluation of autonomous and human-in-the-loop control concepts and architectures. Key findings and lessons learned are presented along with a discussion of future directions.
Highlights
Accepted: 25 February 2021Model-based systems engineering (MBSE) is making impressive strides both in increasing systems life-cycle coverage [1] and in the ability to model increasingly more complex systems [2,3]
Defining the key modeling formalisms that enable flexible modeling based on operational environment characteristics and knowledge of the system state space
This paper has presented the system concept, architecture, prototype implementation, and quantitative analysis supported by a MBSE testbed that enables experimentation with different models, algorithms, and operational scenarios
Summary
Accepted: 25 February 2021Model-based systems engineering (MBSE) is making impressive strides both in increasing systems life-cycle coverage [1] and in the ability to model increasingly more complex systems [2,3]. From digital engineering (DE) to enhance system verification and validation. Not surprisingly, these developments are increasingly producing more MBSE artifacts [5] that need to be organized, metadata-tagged, and managed to facilitate rapid development, integration, and “test drive” of system models in simulation in support of what-if experimentation. MBSE researchers work with specific models and simulations to address a particular problem, thereby producing mostly point solutions. They seldom document assumptions and lessons learned. This practice implies that most MBSE researchers are largely starting without the benefit of the knowledge gained by others
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