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Mathematical Problems in Engineering
Volume 2017, Article ID 1850678, 14 pages
Research Article

Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees

College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China

Correspondence should be addressed to Quanjun Yin; moc.361@nujnauq_niy

Received 31 July 2016; Revised 9 February 2017; Accepted 28 February 2017; Published 19 March 2017

Academic Editor: Chunlin Chen

Copyright © 2017 Yue Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the context of robot navigation, game AI, and so on, real-time search is extensively used to undertake motion planning. Though it satisfies the requirement of quick response to users’ commands and environmental changes, learning real-time search (LRTS) suffers from the heuristic depressions where agents behave irrationally. There have introduced several effective solutions, such as state abstractions. This paper combines LRTS and encoded quad-tree abstraction which represent the search space in multiresolutions. When exploring the environments, agents are enabled to locally repair the quad-tree models and incrementally refine the spatial cognition. By virtue of the idea of state aggregation and heuristic generalization, our EQ LRTS (encoded quad-tree based LRTS) possesses the ability of quickly escaping from heuristic depressions with less state revisitations. Experiments and analysis show that (a) our encoding principle for quad-trees is a much more memory-efficient method than other data structures expressing quad-trees, (b) EQ LRTS differs a lot in several characteristics from classical PR LRTS which represent the space and refine the paths hierarchically, and (c) EQ LRTS substantially reduces the planning amount and curtails heuristic updates compared with LRTS on uniform cells.