DQN-Based Operational Path Planning for Autonomous Fishing Vessel Safety in Waves

Abstract

Developing autonomous navigation systems for small fishing vessels is required to improve path-tracking robustness and mitigate severe wave-induced roll motions. This study proposes a Deep Q-Network (DQN)-based operational path planning methodology that explicitly incorporates roll motion reduction into the reward function, combining a maneuvering model with hydrodynamic analyses. Simulation results under varying wave directions and heights demonstrate that the vessel actively adjusts its heading to minimize the roll response. Based on statistical evaluations across five independent runs, the proposed model effectively reduced the average and maximum roll responses by an average of 3% and 2%, respectively, under the evaluated wave headings at a wave height of 1.0 m, while maintaining operational path tracking, despite a slight increase in the total operational path length. Future research will focus on integrating complex environmental conditions with wind and current, and performing the model test for the validation of the established DQN model.