Towards a Generalised Hybrid Path-Planning and Motion Control System with Auto-calibration for Animated Characters in 3D Environments

  • Antony P. Gerdelan
  • Napoleon H. Reyes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)


Intelligent navigation and path-finding for computer- animated characters in graphical 3D environments is a major design challenge facing programmers of simulations, games, and cinematic productions. Designing agents for computer-animated characters that are required to both move intelligently around obstacles in the environment, and do so in a psycho-visually realistic way with smooth motion is often a too-difficult challenge - designers generally sacrifice intelligent navigation for realistic movement or vice-versa. We present here a specially adapted hybrid fuzzy A* algorithm as a viable solution to meet both of these challenges simultaneously. We discuss the application of this algorithm to animated characters and outline our proposed architecture for automatic tuning of this system.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gerdelan, A.P., Reyes, N.H.: A novel hybrid fuzzy a* robot navigation system for target pursuit and obstacle avoidance. In: Proceedings of the First Korean-New Zealand Joint Workshop on Advance of Computational Intelligence Methods and Applications, Auckland, New Zealand, vol. 1, pp. 75–79 (2006)Google Scholar
  2. 2.
    Gerdelan, A.P.: Artificial Intelligence in Robot Soccer. Bachelor of engineering honours year project report, Institute of Information and Mathematical Sciences, Massey University, Albany, New Zealand (October 2006)Google Scholar
  3. 3.
    Gerdelan, A.P., Iskandar, D., Djohar, A.F., Reyes, N.H.: Utilising the hybrid fuzzy a* algorithm in a cooperative multi-agent system. In: Conference Program and Abstracts of the 4th Conference on Neuro-Computing and Evolving Intelligence (NCEI 2006) and 6th International Conference on Hybrid Intelligent Systems, HIS 2006 (2006)Google Scholar
  4. 4.
    Gerdelan, A.P., Reyes, N.H.: Synthesizing Adaptive Navigational Robot Behaviours using a Hybrid Fuzzy A* Approach. In: Advances in Soft Computing: Computational Intelligence: Theory and Applications, pp. 699–710. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Junker, G.: Pro OGRE 3D Programming. APress (2006); ISBN 1590597109Google Scholar
  6. 6.
    Lawes, G., Barlow, M.: Visual realism and decision making: A novel approach to real-time maritime battlespace visualisation. In: SimTecT 2007 Conference Proceedings. Virtual Environment and Simulation Laboratory (VESL), University of New South Wales at the Australian Defence Force Academy, Simulation Industry Association of Australia (2007); ISBN:0 9775257 2 4Google Scholar
  7. 7.
    Barron, T.: Strategy Game Programming With DirectX 9.0. Wordware (2003); ISBN 1-55622-922-4Google Scholar
  8. 8.
    Funge, J.D.: Artifcial Intelligence for Computer Games. A K Peters, Ltd., Wellesley (2004)Google Scholar
  9. 9.
    Choset, H.M., Hutchinson, S., Lynch, K.M., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementation. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  10. 10.
    Messom, C.H.: Genetic algorithms for autotuning mobile robot motion control. Res. Lett. Inf. Math. Sci. (3), 129–134 (2002); ISSN 1175-2777Google Scholar
  11. 11.
    Stanley, K.O., Bryant, B.D., Miikkulainen, R.: Real-time neuroevolution in the nero video game. IEEE Transactions on Evolutionary Computation 9(6), 653–668 (2005)CrossRefGoogle Scholar
  12. 12.
    Miikkulainen, R.: Creating intelligent agents in games. The Bridge 36(4), 5–13 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antony P. Gerdelan
    • 1
  • Napoleon H. Reyes
    • 1
  1. 1.Institute of Information and Mathematical SciencesMassey University, North ShoreAucklandNew Zealand

Personalised recommendations