Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
复制标题
高级 BIT* (ABIT*)：采用高级图形搜索技术的基于采样的规划
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Gammell
中科院分区：
文献类型：
--
作者：
Marlin P. Strub;J. Gammell
路径规划是一个活跃的研究领域，对机器人的许多应用至关重要。流行的技术包括基于图形的搜索和基于采样的规划器。这些方法都是强大的，但有局限性。本文继续工作，联合收割机结合他们的优势，并减轻他们的局限性，使用一个统一的规划范式。它通过将路径规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上使用高级图搜索技术来实现这一点。ABIT * 结合了截断的随时基于图的搜索，如ATD *，与随时几乎肯定渐近最优的基于采样的规划器，如RRT *。这使得它能够快速找到初始解，然后以随时随地的方式收敛到最优解。ABIT * 在2004年和2008年的测试问题上优于现有的单查询、基于采样的规划器，并在NASA/JPL-Caltech的实际问题上得到了验证。
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 ℝ4 and ℝ8, and was demonstrated on real-world problems with NASA/JPL-Caltech.