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Path-Planning for RTS Games Based on Potential Fields

  • Renato Silveira
  • Leonardo Fischer
  • José Antônio Salini Ferreira
  • Edson Prestes
  • Luciana Nedel
Conference paper
  • 1.1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6459)

Abstract

Many games, in particular RTS games, are populated by synthetic humanoid actors that act as autonomous agents. The navigation of these agents is yet a challenge if the problem involves finding a precise route in a virtual world (path-planning), and moving realistically according to its own personality, intentions and mood (motion planning). In this paper we present several complementary approaches recently developed by our group to produce quality paths, and to guide and interact with the navigation of autonomous agents. Our approach is based on a BVP Path Planner that generates potential fields through a differential equation whose gradient descent represents navigation routes. Resulting paths can deal with moving obstacles, are smooth, and free from local minima. In order to evaluate the algorithms, we implemented our path planner in a RTS game engine.

Keywords

Path-planning navigation autonomous agent 

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References

  1. 1.
    van den Berg, J., Patil, S., Sewall, J., Manocha, D., Lin, M.: Interactive navigation of multiple agents in crowded environments. In: Proc. of the Symposium on Interactive 3D Graphics and Games, pp. 139–147. ACM Press, New York (2008)Google Scholar
  2. 2.
    Burgess, R.G., Darken, C.J.: Realistic human path planning using fluid simulation. In: Proc. of Behavior Representation in Modeling and Simulation, BRIMS (2004)Google Scholar
  3. 3.
    Choi, M.G., Lee, J., Shin, S.Y.: Planning biped locomotion using motion capture data and probabilistic roadmaps. ACM Trans. Graph. 22(2), 182–203 (2003)CrossRefGoogle Scholar
  4. 4.
    Dapper, F., Prestes, E., Nedel, L.P.: Generating steering behaviors for virtual humanoids using BVP control. In: Proc. of CGI, vol. 1, pp. 105–114 (2007)Google Scholar
  5. 5.
    Dietrich, C.A., Nedel, L.P., Comba, J.L.D.: A sketch-based interface to real-time strategy games based on a cellular automation. In: Game Programming Gems, vol. 7, pp. 59–67. Charles River Media, Boston (2008)Google Scholar
  6. 6.
    Fischer, L.G., Silveira, R., Nedel, L.: Gpu accelerated path-planning for multi-agents in virtual environments. SB Games, 101–110 (2009)Google Scholar
  7. 7.
    Funge, J.D.: Artificial Intelligence For Computer Games: An Introduction. A. K. Peters, Ltd., Natick (2004)Google Scholar
  8. 8.
    James, J., Kuffner, J.: Goal-directed navigation for animated characters using real-time path planning and control. In: Magnenat-Thalmann, N., Thalmann, D. (eds.) CAPTECH 1998. LNCS (LNAI), vol. 1537, pp. 171–186. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Kallmann, M.: Shortest Paths with Arbitrary Clearance from Navigation Meshes. In: Symposium on Computer Animation, SCA (2010)Google Scholar
  10. 10.
    Kavraki, L., Svestka, P., Latombe, J.C., Overmars, M.: Probabilistic roadmaps for path planning in high-dimensional configuration space. IEEE Trans. on Robotics and Automation 12(4), 566–580 (1996)CrossRefGoogle Scholar
  11. 11.
    Metoyer, R.A., Hodgins, J.K.: Reactive pedestrian path following from examples. Visual Comput. 20(10), 635–649 (2004)CrossRefGoogle Scholar
  12. 12.
    Nieuwenhuisen, D., Kamphuis, A., Overmars, M.H.: High quality navigation in computer games. Sci. Comput. Program. 67(1), 91–104 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Pettre, J., Simeon, T., Laumond, J.: Planning human walk in virtual environments. In: Int. Conf. on Intelligent Robots and System, vol. 3, pp. 3048–3053 (2002)Google Scholar
  14. 14.
    Silveira, R., Dapper, F., Prestes, E., Nedel, L.: Natural steering behaviors for virtual pedestrians. Visual Comput. (2009)Google Scholar
  15. 15.
    Silveira, R., Prestes, E., Nedel, L.P.: Managing coherent groups. Comput. Animat. Virtual Worlds 19(3-4), 295–305 (2008)CrossRefGoogle Scholar
  16. 16.
    Tecchia, F., Loscos, C., Conroy, R., Chrysanthou, Y.: Agent behaviour simulator (abs): A platform for urban behaviour development. In: Proc. Game Technology, 2001 (2001)Google Scholar
  17. 17.
    Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. In: ACM SIGGRAPH, pp. 1160–1168. ACM Press, New York (2006)Google Scholar
  18. 18.
    Trevisan, M., Idiart, M.A.P., Prestes, E., Engel, P.M.: Exploratory navigation based on dynamic boundary value problems. J. Intell. Robot. Syst. 45(2), 101–114 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Renato Silveira
    • 1
  • Leonardo Fischer
    • 1
  • José Antônio Salini Ferreira
    • 1
  • Edson Prestes
    • 1
  • Luciana Nedel
    • 1
  1. 1.Universidade Federal do Rio Grande do SulBrazil

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