Self-Aware Control for Autonomous Underwater Vehicles

We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.