Path Planning for Ferry Crossing Inland Waterways Based on Deep Reinforcement Learning

Abstract

Path planning is a key issue for safe navigation of inland ferries. With the development of ship intelligence, how to enhance the decision–support system of a ferry in a complex navigation environment is one of the key issues. The inland ferries need to cross the channel frequently and, thus, risky encounters with target ships in the waterway are more frequent, so they need an intelligent decision–support system that can deal with complex situations. In this study, a reinforced deep learning method is proposed for path planning of inland ferries during crossing of the waterways. In the study, the state space, action space and reward function of the Deep Q-network (DQN) model are designed and improved to establish an autonomous navigation method for ferries considering both economy and safety. The DQN model also takes into account the crossing behavior, navigation economy and safety. Finally, the model is applied to case studies to verify its effectiveness.

Review of Intelligent Ship Path Planning Algorithms

“…Guan, W. et al (2024) proposed an intelligent navigation method using PRM and PPO algorithms, considering the collision avoidance behavior specified by COLREG regulations in the reward function to improve the autonomous navigation and collision avoidance decision-making capabilities of MASS. Wen, N. et al (2023) devised an action evaluation network aligned with COLREGs and introduced an action selection network based on reward functions for diverse encounter scenarios, constructing a dual action selection strategy to effectively manage ship collision avoidance issues. Liu, L. et al (2023) trained neural networks and Monte Carlo tree search, employing the AlphaZero algorithm to maximize cumulative reward values and derive optimal decision-making strategies, thereby enhancing navigation safety and efficiency.…”