通过主动估计障碍物意图，提升高密度水域中无人船避障安全性。
Active Learning-augmented Intention-aware Obstacle Avoidance of Autonomous Surface Vehicles in High-traffic Waters
- 基于拓扑结构建模障碍物通行意图，融合国际海上避碰规则（COLREGs）
- 采用LSTM分类意图，结合信息增益与安全性的多目标优化决策
- 在2400次模拟及真实海况实验中均实现实时避障，适用于复杂行为场景
本文通过主动估计障碍物的通行意图并降低其不确定性，增强高密度水域中自主水面航行器（ASVs）的避障能力。提出一种基于拓扑结构的障碍物通行意图建模方法，可适应不同会遇情形，其设计融入了国际海上避碰规则（COLREGs）的拓扑概念。采用长短期记忆（LSTM）神经网络对障碍物意图进行分类，并构建包含意图信息增益与安全性的多目标优化框架以确定ASV机动策略。通过2400次蒙特卡洛仿真验证，涵盖不同数量障碍物、动态特性、会遇情境及障碍物行为模式（合作/非合作）。同时，结合真实海事事故案例分析与真实ASV在环境扰动下的实验，结果表明该策略可在实时条件下成功规避碰撞。
原文摘要 · Abstract (English)
This paper enhances the obstacle avoidance of Autonomous Surface Vehicles (ASVs) for safe navigation in high-traffic waters with an active state estimation of obstacle's passing intention and reducing its uncertainty. We introduce a topological modeling of passing intention of obstacles, which can be applied to varying encounter situations based on the inherent embedding of topological concepts in COLREGs. With a Long Short-Term Memory (LSTM) neural network, we classify the passing intention of obstacles. Then, for determining the ASV maneuver, we propose a multi-objective optimization framework including information gain about the passing obstacle intention and safety. We validate the proposed approach under extensive Monte Carlo simulations (2,400 runs) with a varying number of obstacles, dynamic properties, encounter situations, and different behavioral patterns of obstacles (cooperative, non-cooperative). We also present the results from a real marine accident case study as well as real-world experiments of a real ASV with environmental disturbances, showing successful collision avoidance with our strategy in real-time.
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