论文标题
一项针对船舶碰撞避免和任务计划的机器学习解决方案的调查
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
论文作者
论文摘要
在过去的几年中，机器学习（ML）技术已获得了大量的吸引力，以提高海洋车辆的自主权。本文调查了最近用于避免船舶碰撞的ML方法（COLAV）和任务计划。在概述了对海上车辆不断扩展的ML剥削的介绍之后，概述了船舶任务计划中的关键主题。在技术上进行了审查和比较，并比较了对COLAV主题的直接和间接应用的著名论文。还确定了批评，挑战和未来的方向。结果清楚地表明了该领域的繁荣研究，即使在所有操作条件下能够自主性能执行的机器智能的商业船只仍然很长一段路。
Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.
