CARDINAL ENGINEERING, LLC — Department of Defense SBIR Phase I: N231-028

Description

An autonomous domain control system for US Navy surface ships that deconflicts, decomposes, and triages tasks in Hull, Mechanical, & Electrical (HM&E) control systems may reduce operator burden related to the monitoring, operation, troubleshooting, and recovery of HM&E systems.  A domain control system in operation that can automatically adapt to novel environments from actual experience operating in these situations may improve long-term operations and sustainment of Navy surface combatants. However, for domain control systems to autonomously perform well under significant uncertainties in the system and environment for extended periods of time, the system must be able to learn and compensate for both internal and external changes in a trustworthy manner without significant external intervention.  The Artificial Intelligence Based Ship Domain Autonomous Controller effort will address several key challenges necessary to confidently employ artificial intelligence for the operation of several interconnected systems necessary to meet the mission objectives of the ship.  These challenges include but are not limited to the sufficiency of the sensor and signal data available on the ship, the rules of engagement between the human operator and the supervisory control system, and the trustworthiness of the commands and action from the artificial intelligence-based algorithms as they adapt to the changing environment.  Cardinal Engineering and Applied Simulation Technologies will apply proven tools and techniques to solve these challenges and demonstrate the solutions in a proof-of-concept demonstration. The products and processes resulting from the Artificial Intelligence Based Ship Domain Autonomous Controller effort will demonstrate applicability and scalability to a more capable supervisory control system for US Navy ships, as well as for uses in other markets requiring improved safety, security, and operational effectiveness, such as industrial controls systems, self-driving automobiles, robotics, and other such control-intensive fields.