Skip to main content

Part of the book series: Advances in Soft Computing ((AINSC,volume 29))

Abstract

In this paper, we discuss our challenge on how to give the creatures and ability to follow spatial restriction while keeping the complexity low enough to still allow for real-time simulation of the herd. Our methodologies extend the pioneering work by Reynolds’ flocking algorithm. We extend how the herd can move in natural-looking paths. Also, we show like the creatures to travel smoothly in 3D space with speed regulation in curve.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Reynolds, C. W. (1987) “Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics,” 21(4) (SIGGRAPH’ 87 Conference Proceedings), pp.25/34.

    Article  MathSciNet  Google Scholar 

  2. Reynolds, C. W. (1999) “Steering Behaviors For Autonomous Characters,” in the proceedings of Game Developers Conference 1999 held in San Jose, California. Miller Freeman Game Group, San Francisco, California. pp.763/782.

    Google Scholar 

  3. Hertz J., Krogh A., Palmer, R. G. (1991) “Introduction to the Theory of Neural Computation,” Addison-Wesley.

    Google Scholar 

  4. S. L. Veherencamp (1987) “Individual, kin, and group selection,” in Handbook of Behavioral Neurobiology, Volume 3: Social Behavior and Communication, P. Marler and J. G. Vandenbergh, Eds. New York: Plenum, pp. 354/382.

    Google Scholar 

  5. J. M. Cullen, E. Shaw, and H. A. Baldwin (1965) “Methods for measuring the three-dimensional structure of fish schools,” Animal Beh., vol.13, pp.534/543

    Google Scholar 

  6. X. Tu and D. Terzopoulos (July 1994) “Artificial fishes: Physics, locomotion, perception, behavior,” in Proc. SIGGRAPH 94 Conf., Orlando, FL, pp.43/50

    Google Scholar 

  7. D. C. Brogan and J. K. Hodgins (Mar. 1997) “Group behaviors for systems with significant dynamics,” Auton. Robots, vol. 4, no. 1, pp.137/153

    Google Scholar 

  8. P. K. C. Wang (1991) “Navigation strategies for multiple autonomous robots moving in formation,” J. Robot. Syst., vol. 8, no. 2, pp.177/195

    Article  Google Scholar 

  9. Q. Chen and J. Y. S. Luh (1994) “Coordination and control of a group of small mobile robots,” in Proc. IEEE Int. Conf. Robot. Automat., San Diego, CA, 1994, pp. 2315/2320

    Google Scholar 

  10. M. Mataric (1992) “Designing emergent behaviors: From local interactions to collective intelligence,” in Proc. Int. Conf. Simulation of Adaptive Behavior: From Animals to Animats 2, pp.432/441

    Google Scholar 

  11. (May 1992) “Minimizing complexity in controlling a mobile robot population,” in Proc. 1992 IEEE Int. Conf. Robot. Automat., Nice, France, pp. 830/835

    Google Scholar 

  12. L. E. Parker (1994) “Heterogeneous Multi-Robot Cooperation,” Ph.D. dissertation, Dept. Electr. Eng. Comput. Sci., Mass. Inst. of Technol., Cambridge, MA

    Google Scholar 

  13. E. Yoshida, T. Arai, J. Ota, and T. Miki (1994) “Effect of grouping in local communication system of multiple mobile robots,” in Proc. 1994 IEEE Int. Conf. Intell. Robots Syst., Munich, Germany, pp. 808/815

    Google Scholar 

  14. H. Yamaguchi (Apr. 1997) “Adaptive formation control for distributed autonomous mobile robot groups,” in Proc. 1997 IEEE Conf. Robot. Automat., Albuquerque, NM

    Google Scholar 

  15. D. W. Gage (1992) “Command control for many-robot systems,” Unmanned Syst. Mag., vol. 10, no. 4, pp. 28/34

    Google Scholar 

  16. L. Parker (1993) “Designing control laws for cooperative agent teams,” in Proc. 1993 IEEE Int. Conf. Robot. Automat., pp. 582/587

    Google Scholar 

  17. R. Brooks (Feb. 1986) “A robust layered control system for a mobile robot,” IEEE J. Robot. Automat., vol. RA-2, p. 14

    MathSciNet  Google Scholar 

  18. Tucker Balch and Ronald C. Arkin (Dec 1998) “Behavior-Based Formation Control for Multirobot Teams,” IEEE Transactions on Robotics and Automation, vol.14, no.6

    Google Scholar 

  19. Wolpert and Tumer (2004) “Collectives and the Design of Complex Systems,” Springer.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Aoyagi, M., Namatame, A. (2005). Massive Multi-Agent Simulation in 3D. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_37

Download citation

  • DOI: https://doi.org/10.1007/3-540-32391-0_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25055-5

  • Online ISBN: 978-3-540-32391-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics