基于强化学习的自动靠泊/停泊跟踪控制碰撞概率降低方法
Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning
AI总结：
针对自动靠泊中跟踪控制难以避免碰撞的问题，提出基于强化学习的训练方法，以降低与静态障碍物碰撞的概率，并通过数值仿真和模型实验验证了其有效性。
摘要：
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所承担的最有压力的任务之一。靠泊控制问题通常通过跟踪预定义的轨迹或路径来解决。在不确定环境下保持跟踪误差为零是不可能的；然而，跟踪控制器仍需将船舶引导至期望的泊位附近。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。本文提出了一种基于强化学习的轨迹跟踪控制器训练方法，该方法降低了与静态障碍物碰撞的概率。通过数值仿真，我们展示了所提方法降低了靠泊操作期间的碰撞概率。此外，本文还展示了模型实验中的跟踪性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
