Unmanned systems have gained immense popularity due to their ability to carry out complex operations without human intervention. Imagine a ship that can dynamically adjust engine conditions to get just a little bit farther on its remaining fuel – spelling the difference between mission success and heading back to port early – that is the goal for these complex systems. The operation of these systems is heavily dependent on many of their subsystems and components that need to work together reliably. This article contributes to a new and general framework for operational planning of unmanned systems. Embedded in the framework are several techniques such as deep learning for predicting subsystem health state, dynamic Bayesian networks for system reliability analysis, and multi-objective optimization for reliability-based design of operational profiles. An application of the framework is demonstrated with a case study in engine cooling and control system for an unmanned surface vessel. For this case study, the operational profiles along a trade-off frontier are obtained while expected vessel speed and system reliability are maximized for the vessel mission.

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A Reliability-Based Optimization Framework for Planning Operational Profiles for Unmanned Systems | J. Mech. Des. | ASME Digital Collection