多分辨率A*

摘要：

基于启发式搜索的规划技术通常用于离散空间上的运动规划。这些算法的性能受到搜索空间离散化的分辨率的严重影响。通常，为给定域选择固定分辨率。虽然更精细的分辨率允许更好的机动性，但它显著增加了状态空间的大小，因此需要更多的搜索工作。相反，较粗糙的分辨率提供了快速的探索行为，但在搜索的可操作性和完整性上有所妥协。为了有效地利用高分辨率和低分辨率离散化的优势，我们提出了多分辨率A*（MRA*）算法，该算法同时运行多个具有不同分辨率级别的加权A *（WA*）搜索，并结合了所有这些搜索的优势。除了这些搜索之外，MRA* 还使用一个锚点搜索来控制这些搜索的扩展。我们表明，MRA* 是有界次优的锚决议搜索空间和决议完成。我们在几个运动规划域上进行了实验，包括2D、3D网格规划和7自由度操作规划，并将我们的方法与几种基于搜索和基于采样的基线进行了比较。

英文摘要：

Heuristic search-based planning techniques are commonly used for motion planning on discretized spaces. The performance of these algorithms is heavily affected by the resolution at which the search space is discretized. Typically a fixed resolution is chosen for a given domain. While a finer resolution allows for better maneuverability, it significantly increases the size of the state space, and hence demands more search efforts. On the contrary, a coarser resolution gives a fast exploratory behavior but compromises on maneuverability and the completeness of the search. To effectively leverage the advantages of both high and low resolution discretizations, we propose Multi-Resolution A* (MRA*) algorithm, that runs multiple weighted-A*(WA*) searches having different resolution levels simultaneously and combines the strengths of all of them. In addition to these searches, MRA* uses one anchor search to control expansions from these searches. We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete. We performed experiments on several motion planning domains including 2D, 3D grid planning and 7 DOF manipulation planning and compared our approach with several search-based and sampling-based baselines.

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