Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled via tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.

翻译：船舶靠泊操作的自动化是一个紧迫问题，因为靠泊操作是海员承担压力最大的任务之一。靠泊控制问题通常通过跟踪预定义轨迹或路径来解决。在不确定环境下保持零跟踪误差是不可能的；尽管如此，跟踪控制器仍需使船舶接近目标泊位。跟踪控制器必须优先避免可能导致与障碍物碰撞的跟踪误差。本文提出一种基于强化学习的轨迹跟踪控制器训练方法，以降低与静态障碍物碰撞的概率。通过数值模拟，我们证明所提方法能够降低靠泊操作期间的碰撞概率。此外，本文通过模型实验展示了跟踪控制性能。