论文标题
知情的基于抽样的碰撞避免，与名义路径的偏差最小
Informed Sampling-based Collision Avoidance with Least Deviation from the Nominal Path
论文作者
论文摘要
本文通过引入知情的采样方案和成本函数来解决$ n $维系统的本地路径重新计划，以实现避免碰撞的情况，而偏离（最佳）名义路径。所提出的知情子集由沿指定的名义路径的椭圆形联合组成，因此该子集有效地沿标称路径封装了所有点。成本函数惩罚了与名义路径的巨大偏差，从而确保面对潜在碰撞的当前安全性，同时保留了标称路径的大部分总体效率。提出的方法在与自主海工航行的导航有关的方案中得到了证明。
This paper addresses local path re-planning for $n$-dimensional systems by introducing an informed sampling scheme and cost function to achieve collision avoidance with minimum deviation from an (optimal) nominal path. The proposed informed subset consists of the union of ellipsoids along the specified nominal path, such that the subset efficiently encapsulates all points along the nominal path. The cost function penalizes large deviations from the nominal path, thereby ensuring current safety in the face of potential collisions while retaining most of the overall efficiency of the nominal path. The proposed method is demonstrated on scenarios related to the navigation of autonomous marine crafts.
