AI Augmented GNC for Unmanned Underwater Vehicles: Case Studies of Contemporary UUV Systems

It is anticipated that the demand for underwater transportation will increase in both the military and commercial sectors. This is due to its potential to reduce the chances of detection and enable the precise positioning of offshore underwater equipment and installations. Nonetheless, there is currently no established method for transporting a payload using multiple Hovering Autonomous Underwater Vehicles (HAUVs). Therefore, the purpose of this study is to identify the most effective AI-augmented GNC (Guidance, Navigation, and Control) approach for successfully transporting a payload using multiple HAUVs while minimizing the risk of collision with other surfaces and objects.

AI Augmented GNC for Unmanned Underwater Vehicles: Case Studies of Contemporary UUV Systems dives into a much deeper knowledge of GNC for unmanned systems and how conventional GNC systems can be replaced or augmented with AI-based strategies for effective dynamic analysis of the UUVs. The MathWorks Inc. software product MATLAB is employed for all the analyses presented in this book.