Tree Adaptive A*

  • Authors:

  • Carlos Hernández;Xiaoxun Sun;Sven Koenig;Pedro Meseguer

  • Affiliations:

  • Universidad Católica de la Ssma. Concepción Caupolican, Concepción, Chile;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;Institut d'Investigació en, Intel.ligéncia Artificial IIIA-CSIC Campus UAB, Bellaterra, Spain

  • Venue:
  • The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
  • Year:
  • 2011

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Abstract

Incremental heuristic search algorithms can solve sequences of similar search problems potentially faster than heuristic search algorithms that solve each search problem from scratch. So far, there existed incremental heuristic search algorithms (such as Adaptive A*) that make the h-values of the current A* search more informed, which can speed up future A* searches, and incremental heuristic search algorithms (such as D* Lite) that change the search tree of the current A* search to the search tree of the next A* search, which can be faster than constructing it from scratch. In this paper, we present Tree Adaptive A*, which applies to goal-directed navigation in unknown terrain and builds on Adaptive A* but combines both classes of incremental heuristic search algorithms in a novel way. We demonstrate experimentally that it can run faster than Adaptive A*, Path Adaptive A* and D* Lite, the top incremental heuristic search algorithms in the context of goal-directed navigation in unknown grids.