Lifelong planning A
Lifelong planning A
复制标题
DOI：
10.1016/j.artint.2003.12.001
复制
发表时间：
2004-05-01
影响因子：
14.4
通讯作者：
Furcy, D
中科院分区：
文献类型：
--
作者：
Koenig, S;Likhachev, M;Furcy, D
启发式搜索方法有望找到比未知的搜索方法更快地找到路径规划问题的路径。另一方面，增量搜索方法有望找到一系列类似路径规划问题的最短路径，而不是从头开始解决每个路径规划问题。在本文中，我们开发了终生规划A*（LPA*）A*的增量版本，该版本结合了人工智能和算法文献的想法。它反复找到从给定的启动顶点到给定目标顶点的最短路径，而添加或删除了图形更改或顶点的边缘成本。它的第一次搜索与A*的版本相同，该版本破坏了纽带，而偏爱具有较小G值的顶点，但是随后的许多搜索可能会更快，因为它可以重用上一个搜索树的那些部分，这些部分与与该搜索相同的部分相同新的。我们提出了分析结果，该结果证明了它与**和实验结果的相似性，这些结果证明了如果路径规划问题仅略有变化并且变化接近目标，则证明了其在两个不同领域的潜在优势。 （c）2004年由Elsevier B.V.出版
Heuristic search methods promise to find shortest paths for path-planning problems faster than uninformed search methods. Incremental search methods, on the other hand, promise to find shortest paths for series of similar path-planning problems faster than is possible by solving each path-planning problem from scratch. In this article, we develop Lifelong Planning A* (LPA*) an incremental version of A* that combines ideas from the artificial intelligence and the algorithms literature. It repeatedly finds shortest paths from a given start vertex to a given goal vertex while the edge costs of a graph change or vertices are added or deleted. Its first search is the same as that of a version of A* that breaks ties in favor of vertices with smaller g-values but many of the subsequent searches are potentially faster because it reuses those parts of the previous search tree that are identical to the new one. We present analytical results that demonstrate its similarity to A* and experimental results that demonstrate its potential advantage in two different domains if the path-planning problems change only slightly and the changes are close to the goal. (C) 2004 Published by Elsevier B.V.