The paper presents a path planning algorithm based on RRT* that addresses the risk of grounding during evasive manoeuvres to avoid collision. The planner achieves this objective by integrating a collective navigation experience with the systematic use of water depth information from the electronic navigational chart. Multivariate kernel density estimation is applied to historical AIS data to generate a probabilistic model describing seafarer's best practices while sailing in confined waters. This knowledge is then encoded into the RRT* cost function to penalize path deviations that would lead own ship to sail in shallow waters. Depth contours satisfying the own ship draught define the actual navigable area, and triangulation of this non-convex region is adopted to enable uniform sampling. This ensures the optimal path deviation.
翻译:本文介绍了一种基于RRT* 的路径规划算法,该算法处理在为避免碰撞而进行规避演习期间潜伏的风险。规划员通过将集体航行经验与系统使用电子导航图提供的水深信息结合起来来实现这一目标。多变量内核密度估计适用于历史的AIS数据,以产生一个概率模型,描述海员在封闭水域航行时的最佳做法。然后,这一知识被编码为RRT* 成本函数,以惩罚导致自己的船舶在浅水中航行的路径偏差。满足自己船舶潮流的深度轮廓确定了实际可航行区域,并采用这一非凝固区域三引法,以便能够进行统一取样。这确保了最佳路径偏差。