自动驾驶船在破碎冰区自主导航，减少碰撞能量损失。
AUTO-IceNav: A Local Navigation Strategy for Autonomous Surface Ships in Broken Ice Fields
- 基于滚动时域规划，实时更新冰情数据重规划路径。
- 新代价函数最小化船体与冰碰撞的动能损耗。
- 适合极地航行船舶，提升冰区自主导航安全性。
冰况常迫使船舶减速并偏离主航线以避免损伤。如今北极地区破碎冰场已成为主要冰况，船只与冰的碰撞影响高度依赖接触位置及冰块特征。本文提出AUTO-IceNav框架，实现船舶在冰筏区域的自主导航。采用滚动时域方式计算轨迹，根据更新的冰场数据频繁重规划。规划阶段假设安全名义速度，生成参考路径；设计新颖代价函数，最小化船-冰碰撞导致的动能损失，并融入基于格栅的路径规划器。格栅规划结果作为优化改进阶段的初始猜测，生成局部最优路径。通过仿真与物理试验平台的大量实验验证了方法有效性。
原文摘要 · Abstract (English)
Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particular features of the ice floes. In this paper, we present AUTO-IceNav, a framework for the autonomous navigation of ships operating in ice floe fields. Trajectories are computed in a receding-horizon manner, where we frequently replan given updated ice field data. During a planning step, we assume a nominal speed that is safe with respect to the current ice conditions, and compute a reference path. We formulate a novel cost function that minimizes the kinetic energy loss of the ship from ship-ice collisions and incorporate this cost as part of our lattice-based path planner. The solution computed by the lattice planning stage is then used as an initial guess in our proposed optimization-based improvement step, producing a locally optimal path. Extensive experiments were conducted both in simulation and in a physical testbed to validate our approach.
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