Toward Undersea Persistence

Office of Naval Research

The current challenge impeding advances in the U.S. Navy’s mobility is significant interruptions during undersea missions. Missions such as studying arctic physical environments; understanding the effects of sound on marine mammals; submarine detection and classification; and mine detection and neutralization in both the ocean and littoral environment require persistent operation of unmanned systems in challenging and dynamic environments. The proposed work will create an architecture that integrates three elements of energy, communication, and docking to guarantee undersea persistence where limited power resources and unknown environmental dynamics pose major constraints. The architecture will take into account: the number of operational AUVs required for different operation periods, recharging specifications, communication and localization means, and environmental variables.

The overall goal of this project is: to develop a mobile power delivery system that lowers deployment and operating costs while simultaneously increasing network efficiency and response in dynamic and often dangerous physical conditions. The aim is to create network optimization and formation strategies that will enable a mobile power deliver system to meet overall mission specifications by: 1) reconfiguring itself depending on the number of operational AUVs and; 2) responding to energy consumption needs of the network, situational condition, and environmental variables. The outcome of this work will be a theoretical, computational, and experimental roadmap for building and implementing an autonomous distributed system with mobile power delivery and onsite recharging capability. This roadmap will address fundamental hardware and network science challenges. The long-term outcome of this work will be a persistent and stealthy large area presence of AUV fleets able to perform undersea Navy missions by accurately and autonomously responding to energy needs, situational dynamics and environmental variables.

Investigator: Nina Mahmoudian