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Hierarchical path computation approach for large graphs

机译:大图的分层路径计算方法

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Time is a critical factor in several path planning problems such as flood emergency rescue operations, escape planning from fires and chemical warfare agents dispersed in large buildings, evacuation from urban areas during natural disasters such as earthquakes, and military personnel movement. We propose a hierarchical path planning algorithm (HIPLA) for real time path planning problems where the computational time is of critical significance. The main idea of HIPLA is to significantly reduce the search space for path computation by searching in a high-level abstraction graph, whose nodes are associated with precomputed risk estimates. The cumulative risk associated with all nodes along a path determines the quality of a path. We present a detailed experimental analysis of HIPLA by comparing it with two well-known approaches viz., shortest path algorithm (SPAH) [1] and Dijkstra''s algorithm with pruning [2] for large node-weighted graphs.
机译:时间是一些路径规划问题中的关键因素,例如洪水紧急救援行动,火灾和散布在大型建筑物中的化学战剂的逃生规划,地震等自然灾害期间从市区撤离以及军事人员流动。对于实时路径规划问题,我们提出了一种层次化路径规划算法(HIPLA),其中计算时间至关重要。 HIPLA的主要思想是通过在高级抽象图中进行搜索来显着减少用于路径计算的搜索空间,该抽象图的节点与预先计算的风险估计值相关联。与沿路径的所有节点关联的累积风险决定了路径的质量。通过将HIPLA与两种众所周知的方法进行比较,我们对HIPLA进行了详细的实验分析,即最短路径算法(SPAH)[1]和带修剪的Dijkstra算法[2],用于大型节点加权图。

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