A Reinforcement Learning-Based Path Optimization for Autonomous Underwater Vehicle Mission Execution in Dynamic Marine Environments

Hyojun Ahn, Shincheon Ahn, Emily Jimin Roh, Ilseok Song, Jooeun Kwon, Sei Kwon, Youngdae Kim, Soohyun Park, Joongheon Kim

http://doi.org/10.5626/JOK.2025.52.6.519

This paper proposes an AOPF (Autonomous Underwater Vehicle Optimal Path Finder) algorithm for AUV mission execution and path optimization in dynamic marine environments. The proposed algorithm utilizes a PPO (Proximal Policy Optimization)-based reinforcement learning method in combination with a 3-degree-of-freedom (DOF) model, enabling a balanced approach between obstacle avoidance and effective target approach. This method is designed to achieve faster convergence and higher mission performance compared to the DDPG (Deep Deterministic Policy Gradient) algorithm. Experimental results demonstrated that the algorithm enabled stable learning and generated efficient paths. Furthermore, the proposed approach shows strong potential for real-world deployment in complex marine environments. It offers scalability to multi-AUV cooperative control scenarios.