Autonomous navigation of marine surface vessel in extreme encounter situation

With the development of artificial intelligence (AI) technology, the autonomous navigation and behavior decision-making capabilities of MASS (marine autonomous surface ship) are constantly being innovated, thereby ensuring their safe navigation. However, the recent algorithms exhibit limited efficacy in navigating in unknown and complex environments, while also lacking the capability to effectively handle the encounters resulting from the uncertain behavior of other ships. Consequently, this study proposes an intelligent navigation methodology utilizing the PRM (Probabilistic Roadmap) and PPO (Proximal Policy Optimization) algorithm to facilitate autonomous navigation and collision avoidance decision-making for MASS. Moreover, the MASS disciplined behaviors prescribed by COLREGs are taken into the consideration of the reward function design. Particularly, in extreme encounter situation, it becomes necessary for MASS to depart from COLREGs, thus requiring a corresponding definition of the reward function. Finally, the autonomous navigation and decision-making capability of the MASS is evaluated using real-time ship traffic in a voyage scenario, while various extreme encounter situations are also simulated to demonstrate the generality and practicality of the proposed PRM-PPO method.