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 by 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.

航运中靠泊操作的自动化是一个紧迫的问题，因为靠泊操作是海员承担的最有压力的任务之一。停泊控制问题通常通过跟踪预定义的轨迹或路径来解决。在不确定的环境下保持跟踪误差为零是不可能的；尽管如此，跟踪控制器仍需要使船只靠近所需的泊位。跟踪控制器必须优先考虑避免可能导致与障碍物碰撞的跟踪错误。本文提出了一种基于强化学习的轨迹跟踪控制器训练方法，可降低与静态障碍物发生碰撞的概率。通过数值模拟，我们表明所提出的方法降低了停泊操作期间发生碰撞的可能性。此外，本文还展示了模型实验中的跟踪性能。