Original scientific paper
https://doi.org/10.21278/brod76305
Deep reinforcement learning for integrated vessel path planning with safe anchorage allocation
Gil-Ho Shin
; Graduate School of Korea Maritime and Ocean University, Busan, Republic of Korea
Hyun Yang
; Division of Maritime AI & Cyber Security, Korea Maritime and Ocean University, Busan, Republic of Korea
*
* Corresponding author.
Abstract
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.
Keywords
Maritime safety; reinforcement learning; Vessel Traffic Services (VTS); path planning; deep reinforcement learning
Hrčak ID:
332374
URI
Publication date:
1.7.2025.
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