用深度学习预测船舶破冰时的冰块移动，减少碰撞。
Autonomous Navigation in Ice-Covered Waters with Learned Predictions on Ship-Ice Interactions
- 通过占用估计预测船舶动作引发的冰块粗略动态
- 在仿真和物理测试中碰撞率显著低于现有方法
- 支持实时运行，适合极地自主航行系统
在冰封水域的自主导航面临严峻挑战，因频繁缺乏无碰撞轨迹。当完全避障不可行时，导航策略必须最小化碰撞。此外，冰体随船舶操作动态移动，使路径规划复杂化。为此，我们提出一种新型深度学习模型，通过占用估计估算船舶动作引发的冰体粗略运动。为确保实时性，我们设计了一种缓存中间预测结果的新方法，并将预测模型无缝集成到图搜索规划器中。我们在仿真和物理测试平台中评估了该规划器，结果表明其在与现有方法对比中显著降低了与冰体的碰撞。相关代码与演示已公开于 https://github.com/IvanIZ/predictive-asv-planner。
原文摘要 · Abstract (English)
Autonomous navigation in ice-covered waters poses significant challenges due to the frequent lack of viable collision-free trajectories. When complete obstacle avoidance is infeasible, it becomes imperative for the navigation strategy to minimize collisions. Additionally, the dynamic nature of ice, which moves in response to ship maneuvers, complicates the path planning process. To address these challenges, we propose a novel deep learning model to estimate the coarse dynamics of ice movements triggered by ship actions through occupancy estimation. To ensure real-time applicability, we propose a novel approach that caches intermediate prediction results and seamlessly integrates the predictive model into a graph search planner. We evaluate the proposed planner both in simulation and in a physical testbed against existing approaches and show that our planner significantly reduces collisions with ice when compared to the state-of-the-art. Codes and demos of this work are available at https://github.com/IvanIZ/predictive-asv-planner.
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