Using deep reinforcement learning for Autonomous Vessel Path Planning in Offshore Wind Farms

This study delves into the development of an advanced path planning algorithm for autonomous service vessels within offshore wind farms, leveraging the power of Deep Reinforcement Learning (DRL) to surmount the navigational complexities and unpredictable conditions prevalent in marine environments. Central to this innovation is the strategic incorporation of S-57 nautical chart data, meticulously processed to train a modified Deep Q-network (DQN) algorithm. This approach, coupled with an innovative two-channel state design, enables a refined state representation that distinguishes between collision volumes and navigational waypoints with unparalleled clarity, enhances the system's ability to differentiate between critical navigation elements even under overlapping conditions. Together with fine-tuned reward design and implementation of experience pre-processing, the proposed algorithm's performance was evaluated against a conventional baseline model, revealing significant enhancements in learning speed and operational efficiency in both complex unknown marine conditions and the specialized context of offshore wind farms. The findings underscore the algorithm's rapid adaptability and robustness to diverse navigational challenges, and its effective functionality within offshore wind farm settings without requiring explicit scenario-specific training. This research lays a foundation for further exploration into the efficacy and applicability of AI in enhancing maritime navigation and operational decision-making.