IoT-Driven Autonomous Maritime Vessels with Reinforcement Learning for Navigation and Safety Improvements

This paper examines the integration of IoT-enabled autonomous marine boats with Reinforcement Learning (RL) to enhance navigation and safety. IoT sensors aboard gather real-time data, including ambient conditions, vessel speed, proximity to other boats, and meteorological information. The objective is to use IoT with RL to enhance autonomous marine vessel navigation, hence increasing safety, fuel economy, and overall operational performance. The vessel navigation system employs RL algorithms to continually learn and adjust its course for optimal safety, fuel economy, and time efficiency. A simulation model is created to evaluate the system's operation under diverse situations, such as increased traffic congestion, inclement weather, and obstructions. The findings indicate a 25% decrease in navigation time, a 15% enhancement in fuel economy, and a 30% reduction in collision hazards relative to traditional marine navigation systems. These results highlight the potential of RL with the IoT to enhance the autonomy, safety, and efficiency of maritime vessels. The integration of real-time data with astute decision-making markedly reduces human mistake and operational expenses, facilitating more intelligent and secure marine transportation.