Risk-informed control systems for improved operational performance and decision-making

Autonomous systems, including airborne, land-based, marine, and underwater vehicles, are increasingly present in the world. One important aspect of autonomy is the capability to process information and to make independent decisions for achieving a mission goal. Information on the level of risk related to the operation may improve the decision-making process of autonomous systems. This article describes the integration of risk analysis methods with the control system of autonomous and highly automated systems that are evaluated during operation. Four main areas of implementation are identified; (i) risk models used to directly make decisions, (ii) use of the output of risk models as input to decision-making and optimization algorithms, (iii) the output of risk models may be used as a constraint in or modifying constraints of algorithms, and (iv) the output of risk models may be used to inform representations or maps of the environment to be used in path planning. A case study on a dynamic positioning controller of an offshore supply vessel exemplifies the concepts described in this article. In addition, it demonstrates how risk model output may be used within a hybrid controller