Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles and strong currents, due to the complex interactions. To address this problem, we propose a decentralized multi-ASV collision avoidance policy based on Distributional Reinforcement Learning, which considers the interactions among ASVs as well as with static obstacles and current flows. We evaluate the performance of the proposed Distributional RL based policy against a traditional RL-based policy and two classical methods, Artificial Potential Fields (APF) and Reciprocal Velocity Obstacles (RVO), in simulation experiments, which show that the proposed policy achieves superior performance in navigation safety, while requiring minimal travel time and energy. A variant of our framework that automatically adapts its risk sensitivity is also demonstrated to improve ASV safety in highly congested environments.

翻译：摘要：近年来，遵循《国际海上避碰规则公约》（COLREGs）的自主水面艇（ASV）避碰算法已被提出。然而，在繁忙水域中，多艘ASV需要在静态障碍物和强水流环境下航行，由于复杂的交互作用，严格遵循COLREGs可能困难且不安全。针对此问题，我们提出一种基于分布强化学习的去中心化多ASV避碰策略，该策略同时考虑了ASV之间以及ASV与静态障碍物和水流之间的交互作用。通过仿真实验，我们将所提出的分布强化学习策略与传统强化学习策略、人工势场法（APF）和 reciprocal速度障碍法（RVO）两种经典方法进行性能对比。结果表明，所提策略在导航安全性方面表现优越，同时所需航行时间和能耗最小。此外，我们还展示了一种能自动调整风险敏感度的框架变体，可显著提升高拥挤环境下ASV的安全性。