Path planning plays a crucial role in various autonomy applications, and RRT* is one of the leading solutions in this field. In this paper, we propose the utilization of vertex-based networks to enhance the sampling process of RRT*, leading to more efficient path planning. Our approach focuses on critical vertices along the optimal paths, which provide essential yet sparser abstractions of the paths. We employ focal loss to address the associated data imbalance issue, and explore different masking configurations to determine practical tradeoffs in system performance. Through experiments conducted on randomly generated floor maps, our solutions demonstrate significant speed improvements, achieving over a 400% enhancement compared to the baseline model.

翻译：路径规划在各种自主应用中起着关键作用，而RRT*是该领域的主要解决方案之一。本文提出利用基于顶点的网络来增强RRT*的采样过程，从而实现更高效的路径规划。我们的方法聚焦于最优路径上的关键顶点，这些顶点提供了路径的必要但更稀疏的抽象表示。我们采用焦点损失函数来解决相关的数据不平衡问题，并探索不同的掩码配置以评估系统性能的实际权衡。通过在随机生成的地图上进行的实验，我们的方案实现了显著的加速效果，相比基线模型性能提升超过400%。