论文标题
自适应混合局部全球抽样，用于基于快速采样的最佳路径计划
Adaptive Hybrid Local-Global Sampling for Fast Informed Sampling-Based Optimal Path Planning
论文作者
论文摘要
本文通过结合可允许的知情采样和本地抽样（即，对当前解决方案的邻域进行采样）来提高RRT $^*$的性能。一种自适应策略根据以前样本的在线奖励，调节探索（可接受的知情抽样）和剥削（本地抽样）之间的权衡。该论文表明，在几个模拟和现实世界中，该算法在渐近上是最佳的，并且比最先进的路径计划者（例如，知情RRT*）具有更好的收敛速率。该算法的开源，与ROS兼容的实现可公开使用。
This paper improves the performance of RRT$^*$-like sampling-based path planners by combining admissible informed sampling and local sampling (i.e., sampling the neighborhood of the current solution). An adaptive strategy regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling) based on online rewards from previous samples. The paper demonstrates that the algorithm is asymptotically optimal and has a better convergence rate than state-of-the-art path planners (e.g., Informed-RRT*) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.
