Deep reinforcement learning for integrated vessel path planning with safe anchorage allocation - Gil-Ho Shin; Hyun Yang (Brodogradnja)
This study addresses vessel path planning and anchorage allocation through a reinforcement learning approach. To improve maritime safety and efficiency, we developed an integrated system that combines Deep Q-Network and Artificial Potential Field concepts for path generation. The model implements a specialized grid extension method that accounts for actual vessel dimensions and wind direction, while incorporating differentiated safety distances for each anchorage area. Experimental validation using Automatic Identification System (AIS) data demonstrated that the system successfully generated efficient routes while maintaining all safety distance requirements during both navigation and anchoring phases. Additionally, the system ensured practicality through path simplification using the Douglas-Peucker algorithm while maintaining safety standards. The visualized optimal paths enhance navigational guidance, thereby improving both maritime traffic safety and port operational efficiency.