论文标题
高级位*（abit*）：带有高级图形搜索技术的基于抽样的计划
Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
论文作者
论文摘要
路径规划是许多在机器人技术中使用的重要领域。流行技术包括基于图的搜索和基于抽样的计划者。这些方法是有力的，但有局限性。本文继续努力使用统一的计划范式结合自己的优势并减轻局限性。它通过将路径计划问题视为搜索和近似的两个子问题，并在基于采样的近似值上使用高级图形搜索技术来做到这一点。该视角导致了高级位*。 Abit*将截断的任何基于图形的搜索（例如ATD*）与任何时间渐近基于最佳采样的计划者（例如RRT*）结合在一起。这使其可以快速找到初始解决方案，然后以任何时间方式收敛到最佳。在$ \ mathbb {r}^{4} $和$ \ mathbb {r}^{8} $中，Abit*优于现有的单质量计划，基于采样的计划者，在$ \ Mathbb {r}^{4} $中的测试问题，并在NASA/JPL-Caltech的现实世界中进行了证明。
Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approaches are powerful but have limitations. This paper continues work to combine their strengths and mitigate their limitations using a unified planning paradigm. It does this by viewing the path planning problem as the two subproblems of search and approximation and using advanced graph-search techniques on a sampling-based approximation. This perspective leads to Advanced BIT*. ABIT* combines truncated anytime graph-based searches, such as ATD*, with anytime almost-surely asymptotically optimal sampling-based planners, such as RRT*. This allows it to quickly find initial solutions and then converge towards the optimum in an anytime manner. ABIT* outperforms existing single-query, sampling-based planners on the tested problems in $\mathbb{R}^{4}$ and $\mathbb{R}^{8}$, and was demonstrated on real-world problems with NASA/JPL-Caltech.
