论文标题
使用加固学习的自动对接 /泊位跟踪控制控制的碰撞概率降低方法
Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning
论文作者
论文摘要
运输中的自动化动作是一个紧迫的问题，因为泊位是海员承担的最紧张的任务之一。通常通过跟踪预定义的轨迹或路径来解决泊位控制问题。在不确定的环境下保持零的跟踪误差是不可能的；尽管如此，跟踪控制器仍需要使船只接近所需的泊位。跟踪控制器必须优先考虑跟踪可能导致障碍物碰撞的错误。本文提出了一种基于轨迹跟踪控制器的强化学习的培训方法，该方法降低了与静态障碍物碰撞的可能性。通过数值模拟，我们表明所提出的方法降低了在泊位操作过程中碰撞的可能性。此外，本文显示了模型实验中的跟踪性能。
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.
