Automatic collision avoidance of multiple ships based on deep Q-learning
Automatic collision avoidance of multiple ships based on deep Q-learning
复制标题
基于深度Q学习的多船自动避碰
DOI：
10.1016/j.apor.2019.02.020
复制
发表时间：
2019-05-01
影响因子：
4.3
通讯作者：
Guo, Chen
中科院分区：
文献类型：
--
作者：
Shen, Haiqing;Hashimoto, Hirotada;Guo, Chen
随着世界经济全球化，海上运输船舶数量增加，水路变得比以前更加拥挤。这种情况会增加船舶碰撞的风险，因此需要开发自动避碰系统。提出了一种基于深度强化学习（Deep Reinforcement Learning，DRL）的多船自动避碰方法，特别是在限制沃茨中。结合船舶操纵性、人的经验和航行规则，提出了一种船舶避碰训练方法和算法。所提出的方法进行了研究，不仅通过数值模拟，而且通过模型实验，使用三个自航船舶。通过系统的数值计算和实验验证，证明了基于DRL的船舶自动避碰方法在高度复杂的航行环境中具有很大的可行性。
As the number of ships for marine transportation increases with the globalisation of the world economy, waterways are becoming more congested than before. This situation will raise the risk of collision of the ships; hence, an automatic collision avoidance system needs to be developed. In this paper, a novel approach based on deep reinforcement learning (DRL) is proposed for automatic collision avoidance of multiple ships particularly in restricted waters. A training method and algorithms for collision avoidance of ships, incorporating ship manoeuvrability, human experience and navigation rules, are presented in detail. The proposed approach is investigated not only by numerical simulations but also by model experiments using three self-propelled ships. Through the systematic numerical and experimental validation, it is demonstrated the developed approach based on the DRL has great possibility for realising automatic collision avoidance of ships in highly complicated navigational situations.