Heading towards navigational autonomy in unmanned surface vehicles (USVs) in the maritime sector can fundamentally lead towards safer waters as well as reduced operating costs, while also providing a range of exciting new capabilities for oceanic research, exploration and monitoring. However, achieving such a goal is challenging. USV control systems must, safely and reliably, be able to adhere to the international regulations for preventing collisions at sea (COLREGs) in encounters with other vessels as they navigate to a given waypoint while being affected by realistic weather conditions, either during the day or at night. To deal with the multitude of possible scenarios, it is critical to have a virtual environment that is able to replicate the realistic operating conditions USVs will encounter, before they can be implemented in the real world. Such "digital twins" form the foundations upon which Deep Reinforcement Learning (DRL) and Computer Vision (CV) algorithms can be used to develop and guide USV control systems. In this paper we describe the novel development of a COLREG-compliant DRL-based collision avoidant navigational system with CV-based awareness in a realistic ocean simulation environment. The performance of the trained autonomous Agents resulting from this approach is evaluated in several successful navigations to set waypoints in both open sea and coastal encounters with other vessels. A binary executable version of the simulator with trained agents is available at https://github.com/aavek/Aeolus-Ocean

翻译：在海洋领域实现无人水面艇（USV）的导航自主性，可从根本上有助于提升水域安全性、降低运营成本，同时为海洋研究、勘探与监测提供一系列令人振奋的新能力。然而实现这一目标颇具挑战。USV控制系统必须安全可靠地遵守国际海上避碰规则（COLREGs），在驶向指定航点过程中应对与其他船舶的相遇情形，同时受真实天气条件影响，无论昼夜。为应对众多可能场景，在将USV部署至真实环境前，拥有能复现其遭遇的真实运行条件的虚拟环境至关重要。此类“数字孪生”为利用深度强化学习（DRL）和计算机视觉（CV）算法开发并引导USV控制系统奠定了基础。本文描述了在真实海洋仿真环境中，创新型地开发基于COLREG合规DRL的碰撞规避导航系统及基于CV的态势感知系统。该方法训练出的自主智能体在执行多个导航任务中的性能得到评估，这些任务涉及在公海及近海区域与其他船舶相遇时成功驶向指定航点。包含训练后智能体的仿真器二进制可执行版本可在 https://github.com/aavek/Aeolus-Ocean 获取。