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Swarming Behavior Using Probabilistic Roadmap Techniques

  • O. Burçhan Bayazıt
  • Jyh-Ming Lien
  • Nancy M. Amato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3342)

Abstract

While techniques exist for simulating swarming behaviors, these methods usually provide only simplistic navigation and planning capabilities. In this review, we explore the benefits of integrating roadmap-based path planning methods with flocking techniques to achieve different behaviors. We show how group behaviors such as exploring can be facilitated by using dynamic roadmaps (e.g., modifying edge weights) as an implicit means of communication between flock members. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These behavior rules enable the flock members to modify their actions based on their current location and state. We propose new techniques for several distinct group behaviors: homing, exploring (covering and goal searching), passing through narrow areas and shepherding. We present results that show that our methods provide significant improvement over methods that utilize purely local knowledge and moreover, that we achieve performance approaching that which could be obtained by an ideal method that has complete global knowledge. Animations of these behaviors can be viewed on our webpages.

Keywords

Path Planning Edge Weight Group Behavior Feasible Path Narrow Passage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Reynolds, C.W.: Flocks, herds, and schools: A distributed behavioral model. Computer Graphics, 25–34 (1987)Google Scholar
  2. 2.
    Reynolds, C.W.: Steering behaviors for autonomous characters. In: Game Developers Conference (1999)Google Scholar
  3. 3.
    Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Boston (1991)Google Scholar
  4. 4.
    Bayazit, O.B., Lien, J.M., Amato, N.M.: Better flocking behaviors using rulebased roadmaps. In: Proc. Int. Workshop on Algorithmic Foundations of Robotics, WAFR (2002)Google Scholar
  5. 5.
    Bayazit, O.B., Lien, J.M., Amato, N.M.: Better group behaviors in complex environments using global roadmaps. Artif. Life (2002)Google Scholar
  6. 6.
    Bayazit, O.B., Lien, J.M., Amato, N.M.: Roadmap-based flocking for complex environments. In: Proc. Pacific Graphics, pp. 104–113 (2002)Google Scholar
  7. 7.
    Lien, J.M., Bayazit, O.B., Sowell, R.T.S., Rodrigues, L., Amato, N.M.: Shepherding behaviors. In: Proc. IEEE Int. Conf. Robot. Autom (ICRA), pp. 4159–4164 (2004)Google Scholar
  8. 8.
    Kavraki, L., Svestka, P., Latombe, J.C., Overmars, M.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Automat. 12, 566–580 (1996)CrossRefGoogle Scholar
  9. 9.
    Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C.V., Vallejo, D.: OBPRM: Anobstacle-based PRM for 3D workspaces. In: Robotics: The Algorithmic Perspective, Natick, MA, A.K. Peters Proceedings of the Third Workshop on the Algorithmic Foundations of Robotics (WAFR), Houston, TX, pp. 155–168 (1998)Google Scholar
  10. 10.
    Hsu, D., Kindel, R., Latombe, J.C., Rock, S.: Randomized Kinodynamic Motion Planning with Moving Obstacles. In: Proc. Int. Workshop on Algorithmic Foundations of Robotics (WAFR), pp. SA1–SA18 (2000)Google Scholar
  11. 11.
    Bohlin, R., Kavraki, L.E.: Path planning using Lazy PRM. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 521–528 (2000)Google Scholar
  12. 12.
    Nielsen, C.L., Kavraki, L.E.: A two level fuzzy prm for manipulation planning. In: IEEE/RSJ International Conference on Intelligent Robotics and Systems (2000)Google Scholar
  13. 13.
    Song, G., Miller, S.L., Amato, N.M.: Customizing PRM roadmaps at query time. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1500–1505 (2001)Google Scholar
  14. 14.
    Bayazit, O.B., Song, G., Amato, N.M.: Enhancing randomized motion planners: Exploring with haptic hints. Autonomous Robots, Special Issue on Personal Robotics 10, 163–174 Preliminary version appeared in ICRA, pp. 529–536 (2001)Google Scholar
  15. 15.
    Witkin, A., Baraff, D.: Physically Based Modeling: Principles and Practice. SIGGRAPH 1997 Course Notes #19, SIGGRAPH-ACM publication (1997)Google Scholar
  16. 16.
    Khatib, O.: Real time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5, 90–98 (1986)CrossRefGoogle Scholar
  17. 17.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 1st edn. Prentice Hall, Englewood Cliffs (1994)Google Scholar
  18. 18.
    Vaughan, R.T., Sumpter, N., Henderson, J., Frost, A., Cameron, S.: Experiments in automatic flock control. J. Robot. and Autonom. Sys. 31, 109–117 (2000)CrossRefGoogle Scholar
  19. 19.
    Tu, X., Terzopoulos, D.: Artificial fishes: Physics, locomotion, perception, behavior. In: Computer Graphics, pp. 24–29 (1994)Google Scholar
  20. 20.
    Nishimura, S., Ikegami, T.: Emergence of collective strategies in prey-predator game model. Artif. Life 3, 243–260 (1997)CrossRefGoogle Scholar
  21. 21.
    Ward, C., Gobet, F., Kendall, G.: Evolving collective behavior in an artificial ecology. Artif. Life 7, 191–209 (2001)CrossRefGoogle Scholar
  22. 22.
    Brogan, D.C., Hodgins, J.K.: Group behaviors for systems with significant dynamics. Autonomous Robots, 137–153 (1997)Google Scholar
  23. 23.
    Sun, S.J., Sim, D.W.L.K.B.: Artificial immune-based swarm behaviors of distributed autonomous robotic systems. In: Proc. IEEE Int. Conf. Robot. Autom (ICRA), pp. 3993–3998 (2001)Google Scholar
  24. 24.
    Balch, T., Hybinette, M.: Social potentials for scalable multirobot formations. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 73–80 (2000)Google Scholar
  25. 25.
    Fukuda, T., Mizoguchi, H., Sekiyama, K., Arai, F.: Group behavior control for MARS (micro autonomous robotic system). In: Proc. IEEE Int. Conf. Robot. Autom (ICRA), pp. 1550–1555 (1999)Google Scholar
  26. 26.
    Mataric, M.J.: Interaction and Intelligent Behavior. PhD thesis, MIT EECS (1994)Google Scholar
  27. 27.
    Saiwaki, N., Komatsu, T., Yoshida, T., Nishida, S.: Automatic generation of moving crowd using chaos model. In: IEEE Int. Conference on System, Man and Cybernetics, pp. 3715–3721 (1997)Google Scholar
  28. 28.
    Li, T.Y., Jeng, Y.J., Chang, S.I.: Simulating virtual human crowds with a leaderfollower model. In: Proceedings of 2001 Computer Animation Conference (2001)Google Scholar
  29. 29.
    Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5, 137–172 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • O. Burçhan Bayazıt
    • 1
  • Jyh-Ming Lien
    • 2
  • Nancy M. Amato
    • 2
  1. 1.Washington UniversitySt. LouisUSA
  2. 2.Texas A&M UniversityCollege StationUSA

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