Scalable Precomputed Search Trees
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The traditional A*-search method builds a search tree of potential solution paths during runtime. An alternative approach is to compute this search tree in advance, and then use it during runtime to efficiently find a solution. Recent work has shown the potential for this idea of precomputation. However, these previous methods do not scale to the memory and time needed for precomputing trees of a reasonable size. The focus of this paper is to take a given set of actions from a navigation scenario, and precompute a search tree that can scale to large planning problems. We show that this precomputation approach can be used to efficiently generate the motions for virtual human-like characters navigating in large environments such as those in games and films. We precompute a search tree incrementally and use a density metric to scatter the paths of the tree evenly among the region we want to build the tree in. We experimentally compare our algorithm with some recent methods for building trees with diversified paths. We also compare our method with traditional A*-search approaches. Our main advantage is a significantly faster runtime, and we show and describe the tradeoffs that we make to achieve this runtime speedup.
KeywordsPath Planning Search Tree Child Node Parent Node Depth Level
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