Advertisement

Computational Visual Media

, Volume 1, Issue 2, pp 105–118 | Cite as

Subregion graph: A path planning acceleration structure for characters with various motion types in very large environments

  • Nicholas Mario Wardhana
  • Henry Johan
  • Hock Soon Seah
Open Access
Research Article
  • 317 Downloads

Abstract

Modern computer graphics applications commonly feature very large virtual environments and diverse characters which perform different kinds of motions. To accelerate path planning in such a scenario, we propose the subregion graph data structure. It consists of subregions, which are clusters of locally connected waypoints inside a region, as well as subregion connectivities. We also present a fast algorithm to automatically generate a subregion graph from an enhanced waypoint graph map representation, which also supports various motion types and can be created from large virtual environments. Nevertheless, a subregion graph can be generated from any graphbased map representation. Our experiments show that a subregion graph is very compact relative to the input waypoint graph. By firstly planning a subregion path, and then limiting waypoint-level planning to this subregion path, over 8 times average speedup can be achieved, while average length ratios remain as low as 102.5%.

Keywords

path planning acceleration very large environments motion types abstraction 

Supplementary material

41095_2015_18_MOESM1_ESM.pdf (2.3 mb)
Supplementary material, approximately 2.35 MB.
41095_2015_18_MOESM2_ESM.mp4 (49.5 mb)
Supplementary material, approximately 49.4 MB.

References

  1. [1]
    Grand Theft Auto III (DVD). Rockstar Games, 2001.Google Scholar
  2. [2]
    Just Cause II(Steam). Eidos Interactive, 2010. Available at http://store.steampowered.com/app/81901.Google Scholar
  3. [3]
    The Elder Scrolls V: Skyrim (Steam). Bethesda Softworks, 2011. Available at http://store.steampowered.com/app/7282501.Google Scholar
  4. [4]
    Plaku, E.; Kavraki, L. E. Distributed sampling-based roadmap of trees for large-scale motion planning. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 3868–3873, 2005.CrossRefGoogle Scholar
  5. [5]
    Samperi, K.; Hawes, N.; Beale, R. Improving map generation in large-scale environments for intelligent virtual agents. In: The AAMAS-2013 Workshop on Cognitive Agents for Virtual Environments, 2013. Available at http://www.cs.bham.ac.uk/~nah/bibtex/papers/samperietal2013cave.pdf.Google Scholar
  6. [6]
    Wardhana, N. M.; Johan, H.; Seah, H. S. Enhanced waypoint graph for surface and volumetric path planning in virtual worlds. The Visual Computer Vol. 29, No. 10, 1051–1062, 2013.CrossRefGoogle Scholar
  7. [7]
    Hart, P. E.; Nilsson, N. J.; Raphael, B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics Vol. 4, No. 2, 100–107, 1968.CrossRefGoogle Scholar
  8. [8]
    Holtë, R. C.; Mkadmi, T.; Zimmer, R. M.; MacDonald, A. J. Speeding up problem solving by abstraction: A graph oriented approach. Artificial Intelligence Vol. 85, Nos. 1–2, 321–361, 1996.CrossRefGoogle Scholar
  9. [9]
    Sturtevant, N.; Buro, M. Partial pathfinding using map abstraction and refinement. In: Proceedings of the 20th National Conference on Artificial Intelligence, Vol. 3, 1392–1397, 2005.Google Scholar
  10. [10]
    Bulitko, V.; Sturtevant, N.; Lu, J.; Yau, T. Graph abstraction in real-time heuristic search. Journal of Artificial Intelligence Research Vol. 30, No. 1, 51–100, 2007.zbMATHGoogle Scholar
  11. [11]
    Frederickson, G. N. Fast algorithms for shortest paths in planar graphs, with applications. SIAM Journal on Computing Vol. 6, No. 6, 1004–1022, 1987.MathSciNetCrossRefGoogle Scholar
  12. [12]
    Köhler, E.; Möhring, R. H.; Schilling, H. Acceleration of shortest path and constrained shortest path computation. Lecture Notes in Computer Science Vol. 3503, 126–138, 2005.CrossRefGoogle Scholar
  13. [13]
    Wagner, D.; Willhalm, T. Geometric speedup techniques for finding shortest paths in large sparse graphs. Lecture Notes in Computer Science Vol. 2832, 776–787, 2003.CrossRefGoogle Scholar
  14. [14]
    Hilger, M.; Köhler, E.; Möhring, R. H.; Schilling, H. Fast point-to-point shortest path computations with arc-flags. In: The Shortest Path Problem: Ninth DIMACS Implementation Challenge. Demetrescu, C.; Goldberg, A. V.; Johnson, D. S. Eds. American Mathematical Society, 41–72, 2009.Google Scholar
  15. [15]
    Lauther, U. An extremely fast, exact algorithm for finding shortest paths in static networks with geographical background. In: Geoinformation und Mobilität–von der Forschung zur praktischen Anwendung, Vol. 22, 219–230, 2004.Google Scholar
  16. [16]
    Möhring, R. H.; Schilling, H.; Schütz, B.; Wagner, D.; Willhalm, T. Partitioning graphs to speed up Dijkstra’s algorithm. Lecture Notes in Computer Science Vol. 3503, 189–202, 2005.CrossRefGoogle Scholar
  17. [17]
    Harabor, D.; Botea, A. Hierarchical path planning for multi-size agents in heterogeneous environments. In: IEEE Symposium on Computational Intelligence and Games, 258–265, 2008.Google Scholar
  18. [18]
    Mould, D.; Horsch, M. C. A hierarchical terrain representation for approximately shortest paths. Lecture Notes in Computer Science Vol. 3157, 104–113, 2004.CrossRefGoogle Scholar
  19. [19]
    Gutman, R. J. Reach-based routing: A new approach to shortest path algorithms optimized for road networks. In: Proceedings of the 6th Workshop on Algorithm Engineering and Experiments and the First Workshop on Analytic Algorithmics and Combinatorics, 100–111, 2004.Google Scholar
  20. [20]
    Goldberg, A. V.; Kaplan, H.; Werneck, R. F. Reach for A*: Efficient point-to-point shortest path algorithms. In: Proceedings of the Eighth Workshop on Algorithm Engineering and Experiments, 129–143, 2006.Google Scholar
  21. [21]
    Sanders, P.; Schultes, D. Highway hierarchies hasten exact shortest path queries. Lecture Notes in Computer Science Vol. 3669, 568–579, 2005.MathSciNetCrossRefGoogle Scholar
  22. [22]
    Floyd, R. W. Algorithm 97: Shortest path. Communications of the ACM Vol. 5, No. 6, 345, 1962.CrossRefGoogle Scholar
  23. [23]
    Warshall, S. A theorem on boolean matrices. Journal of the ACM Vol. 9, No. 1, 11–12, 1962.zbMATHMathSciNetCrossRefGoogle Scholar
  24. [24]
    Goldberg, A. V.; Harrelson, C. Computing the shortest path: A* search meets graph theory. In: Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 156–165, 2005.Google Scholar
  25. [25]
    Felner, A.; Sturtevant, N.; Schaeffer, J. Abstractionbased heuristics with true distance computations. In: Proceedings of the Eighth Symposium on Abstraction, Reformulation, and Approximation, 74–81, 2009.Google Scholar
  26. [26]
    Oliva, R.; Pelechano, N. NEOGEN: Near optimal generator of navigation meshes for 3D multi-layered environments. Computers & Graphics Vol. 37, No. 5, 403–412, 2013.CrossRefGoogle Scholar
  27. [27]
    Van Toll, W. G.; Cook IV, A. F.; Geraerts, R. Navigation meshes for realistic multi-layered environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 3526–3532, 2011.Google Scholar
  28. [28]
    Dijkstra, E. W. A note on two problems in connexion with graphs. Numerische Mathematik Vol. 1, No. 1, 269–271, 1959.zbMATHMathSciNetCrossRefGoogle Scholar
  29. [29]
    Pinter, M. Toward more realistic pathfinding. 2001. Available at http://www.gamasutra.com/features/20010314/pinter_01.htm.Google Scholar
  30. [30]
    Siek, J.; Lee, L.-Q.; Lumsdaine, A. The Boost Graph Library (BGL) (version 1.57). 2014. Available at http://www.boost.org/libs/graph/.Google Scholar
  31. [31]
    The OGRE Team. OGRE—Object-oriented Graphics Rendering Engine (version 1.7.3). 2011. Available at http://www.ogre3d.org/.Google Scholar
  32. [32]
    Wagner, D.; Willhalm, T. Speed-up techniques for shortest-path computations. Lecture Notes in Computer Science Vol. 4393, 23–36, 2007.MathSciNetCrossRefGoogle Scholar
  33. [33]
    Garcia, F. M.; Kapadia, M.; Badler, N. I. GPU-based dynamic search on adaptive resolution grids. In: 2014 IEEE International Conference on Robotics and Automation, 1631–1638, 2014.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Nicholas Mario Wardhana
    • 1
    • 2
  • Henry Johan
    • 3
  • Hock Soon Seah
    • 1
    • 2
  1. 1.Multi-plAtform Game Innovation Centre (MAGIC)Nanyang Technological University, XFrontiers BlockSingaporeSingapore
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Fraunhofer IDM@NTUNanyang Technological UniversitySingaporeSingapore

Personalised recommendations