Expected Path Degradation when Searching over a Sparse Grid Hierarchy

The traditional focus of combinatorial search research is to speed up the search algorithm. An alternative, however, is to create a sparser representation of the search space. This relates to the idea of spanners from graph theory. These are subgraphs which retain paths between any two vertices of the original graph while guaranteeing a maximum stretch in path length. In practice, the path degradation of graph spanners is significantly smaller than the theoretical bound. Even so, expected path degradation of graph spanners is typically not studied. This work focuses on grid path-finding to propose an algorithm that constructs a grid spanner, where analysis for the obstacle-free case shows that significant performance gains can be achieved with a small decrease in expected path quality. This is an important first step towards studying the expected performance of spanners. Experiments on game maps show that expected path quality with obstacles is only sometimes marginally lower than that in the obstacle-free case and that a significant reduction in the size of the search space can be achieved.

[1]  Kostas E. Bekris,et al.  Asymptotically Near-Optimal Planning With Probabilistic Roadmap Spanners , 2013, IEEE Transactions on Robotics.

[2]  Kurt Mehlhorn,et al.  Additive spanners and (α, β)-spanners , 2010, TALG.

[3]  Sabine Storandt Contraction Hierarchies on Grid Graphs , 2013, KI.

[4]  Kostas E. Bekris,et al.  Sparse roadmap spanners for asymptotically near-optimal motion planning , 2014, Int. J. Robotics Res..

[5]  Sebastian Thrun,et al.  ARA*: Anytime A* with Provable Bounds on Sub-Optimality , 2003, NIPS.

[6]  Adi Botea,et al.  Near Optimal Hierarchical Path-Finding , 2004, J. Game Dev..

[7]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[8]  Arthur L. Liestman,et al.  Grid spanners , 1993, Networks.

[9]  Nathan R. Sturtevant,et al.  Partial Pathfinding Using Map Abstraction and Refinement , 2005, AAAI.

[10]  David Furcy,et al.  Lifelong Planning A , 2004, Artif. Intell..

[11]  Kostas E. Bekris,et al.  Sparse Roadmap Spanners , 2012, WAFR.

[12]  Jeffrey S. Rosenschein,et al.  Search Space Reduction Using Swamp Hierarchies , 2010, SOCS.

[13]  Stefan Janssen Improving Heuristics for Pathfinding in Games , 2011 .

[14]  Shang-Hua Teng,et al.  Lower-stretch spanning trees , 2004, STOC '05.

[15]  Nathan R. Sturtevant,et al.  Benchmarks for Grid-Based Pathfinding , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[16]  Kostas E. Bekris,et al.  Asymptotically Near-Optimal Is Good Enough for Motion Planning , 2011, ISRR.

[17]  Pankaj K. Agarwal,et al.  Sparsification of motion-planning roadmaps by edge contraction , 2013, 2013 IEEE International Conference on Robotics and Automation.

[18]  Devin J. Balkcom,et al.  A fast streaming spanner algorithm for incrementally constructing sparse roadmaps , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Jose Augusto Ramos Soares,et al.  Graph Spanners: a Survey , 1992 .

[20]  Sven Koenig,et al.  Identifying Hierarchies for Fast Optimal Search , 2014, SOCS.