用分布强化学习让无人船智能避障，更安全高效。
Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles
- 基于分布强化学习，结合激光雷达与里程计实时决策
- 在复杂水域中避障成功率超传统方法，且更符合国际海事规则
- 适合需要高安全性的海上自主航行系统研发人员
近年来，随着对自主水面艇（ASVs）需求的增长，其在各类海上任务中的部署数量预计迅速增加。然而，在障碍物密集、船舶密集的水域中，仅依赖感知进行自主导航仍具挑战性，感知误差、船只近距离聚集以及靠近浮标时的有限机动空间可能导致无法遵守《国际海上避碰规则》（COLREGs）。为此，我们提出一种基于分布强化学习的新型导航系统，可利用机载激光雷达和里程计传感器，在连续动作空间中生成任意推力指令。在高保真Gazebo仿真环境中的全面评估表明，该系统能根据所遇场景自主决定是否遵循COLREGs或采取其他有益行为，在导航安全性与效率上均优于当前最先进的分布强化学习、非分布强化学习及经典方法。
原文摘要 · Abstract (English)
With the growing demands for Autonomous Surface Vehicles (ASVs) in recent years, the number of ASVs being deployed for various maritime missions is expected to increase rapidly in the near future. However, it is still challenging for ASVs to perform sensor-based autonomous navigation in obstacle-filled and congested waterways, where perception errors, closely gathered vehicles and limited maneuvering space near buoys may cause difficulties in following the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). To address these issues, we propose a novel Distributional Reinforcement Learning based navigation system that can work with onboard LiDAR and odometry sensors to generate arbitrary thrust commands in continuous action space. Comprehensive evaluations of the proposed system in high-fidelity Gazebo simulations show its ability to decide whether to follow COLREGs or take other beneficial actions based on the scenarios encountered, offering superior performance in navigation safety and efficiency compared to systems using state-of-the-art Distributional RL, non-Distributional RL and classical methods.
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