While techniques exist for simulating group behaviors, these methods usually only provide simplistic navigation and planning capabilities. In this work, we explore the benefits of integrating roadmap-based path planning methods with flocking techniques. 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 three distinct group behaviors: homing, exploring (covering and goal searching) and passing through narrow areas. Animations of these behaviors can be viewed at http://parasol.tamu.edu/dsmft.
- Edge Weight
- Group Behavior
- Narrow Passage
- Homing Behavior
- Behavior Rule
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T. Balch and M. Hybinette. Social potentials for scalable multirobot formations. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 73–80, 2000.
O. B. Bayazit, J.-M. Lien, and Nancy M. Amato. Better group behaviors in complex environments using global roadmaps. In Artif. Life, Dec 2002. To appear.
D. C. Brogan and J. K. Hodgins. Group behaviors for systems with significant dynamics. In Autonomous Robots, pages 137–153, 1997.
T. Fukuda, H. Mizoguchi, K. Sekiyama, and F. Arai. Group behavior control for MARS (micro autonomous robotic system). In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 1550–1555, 1999.
J. Funge, X. Tu, and D. Terzopoulos. Cognitive modeling: Knowledge, reasoning and planning for intelligent characters. In Computer Graphics, pages 29–38, 1999.
O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot Res., 5 (l): 90–98, 1986.
J. C. Latombe. Robot Motion Planning. Kluwer Academic Publishers, Boston, MA, 1991.
T. Y. Li, Y. J. Jeng, and S. I. Chang. Simulating virtual human crowds with a leader-follower model. In Proceedings of 2001 Computer Animation Conference, 2001.
Dorigo M., G. Di Caro, and L. M. Gambardella. Ant algorithms for discrete optimization. In Artificial Life, pages 137–172, 1999.
M. J. Mataric. Interaction and Intelligent Behavior. PhD thesis, MIT EECS, 1994.
S.I. Nishimura and T. Ikegami. Emergence of collective strategies in prey-predator game model. Artif. Life, 3: 243–260, 1997.
L. E. Parker. Designing control laws for cooperative agent teams. In IEEE International Conference on Robotics and Automation, pages 582–587, 1993.
C. W. Reynolds. Flocks, herds, and schools: A distributed behaviroal model. In Computer Graphics, pages 25–34, 1987.
C. W. Reynolds. Steering behaviors for autonomous characters. In Game Developers Conference, 1999.
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 1th edition, 1994.
N. Saiwaki, T. Komatsu, T. Yoshida, and S. Nishida. Automatic generation of moving crowd using chaos model. In IEEE Int. Conference on System, Man and Cybernetics, pages 3715–3721, 1997.
S.-J. Sun and D.-W Lee K.-B. Sim. Artificial immune-based swarm behaviors of distributed autonomous robotic systems. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 3993–3998, 2001.
X. Tu and D. Terzopoulos. Artificial fishes: Physics, locomotion, perception, behavior. In Computer Graphics, pages 24–29, 1994.
R. T. Vaughan, N. Sumpter, J. Henderson, A. Frost, and S. Cameron. Experiments in automatic flock control. J. Robot, and Autonom. Sys., 31: 109–117, 2000.
C.R. Ward, F. Gobet, and G. Kendall. Evolving collective behavior in an artificial ecology. Artif. Life, 7: 191–209, 2001.
S. A. Wilmarth, N. M. Amato, and P. F. Stiller. MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 1024–1031, 1999.
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© 2004 Springer-Verlag Berlin Heidelberg
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Bayazit, O.B., Lien, JM., Amato, N.M. (2004). Better Group Behaviors Using Rule-Based Roadmaps. In: Boissonnat, JD., Burdick, J., Goldberg, K., Hutchinson, S. (eds) Algorithmic Foundations of Robotics V. Springer Tracts in Advanced Robotics, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45058-0_7
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