International Journal of Fuzzy Logic and Intelligent Systems

  • 제5권3호
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  • Pages.222-229
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  • 2005
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Flexible and Scalable Formation for Swarm Systems

  • Kim Dong-Hun (Division of Electronic and Electrical Engineering, Kyungnam University)
  • 발행 : 2005.09.01
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초록

This paper presents a self-organizing scheme for multi-agent swarm systems based on coupled nonlinear oscillators (CNOs). In this scheme, unicycle robots self-organize to flock and arrange group formation through attractive and repulsive forces among themselves. The main result is the maintenance of flexible and scalable formation. It is also shown how localized distributed controls are utilized throughout group behaviors such as formation and migration. In the paper, the proposed formation ensures safe separation and good cohesion performance among the robots. Several examples show that the proposed method for group formation performs the group behaviors such as reference path following, obstacle avoidance and flocking, and the formation characteristics such as flexibility and scalability, effectively.

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참고문헌

  1. G. Beni, 'The concept of cellular robotic system,' IEEE Int. Symp. on Intelligent Control, pp. 57-62, 1998 https://doi.org/10.1109/ISIC.1988.65405
  2. R. Olfati-Saber and R. M. Murray, 'Distributed cooperative control of multiple vehicle formations using structural potential functions,' In IFAC World Congress, Barcelona, Spain, to appear, 2002
  3. M. J. Mataric, 'Designing emergent behaviors: from local interactions to collective intelligence,' Proc. Int. Conf. Simulation of Adaptive Behavior: From Animals to Animats, pp. 432-441, 1992
  4. M. J. Mataric, 'Minimizing complexity in controlling a mobile robot population,' Proc. IEEE Int. Conf. Robot. Automat., pp. 830-835, 1992 https://doi.org/10.1109/ROBOT.1992.220192
  5. T. Balch and R. C. Arkin, 'Behavior-based formation control for multirobot teams,' IEEE Trans. on Robotics and Automation, pp. 926-939, 1998
  6. L. E. Parker, 'Designing control laws for cooperative agent teams,' Proc. IEEE Int. Conf. Robot. Automat., pp. 582-587, 1993 https://doi.org/10.1109/ROBOT.1993.291842
  7. K. Jin, P. Liang and G. Beni, 'Stability of synchronized distributed control of discrete swarm structures,' IEEE Int. Conf. on Robotics and Automation, pp. 1033-1038, 1994 https://doi.org/10.1109/ROBOT.1994.351221
  8. P. Liang and G. Beni, 'Robotic morphogenesis,' IEEE Int. Conf. on Robotics and Automation, pp. 2175-2180, 1995 https://doi.org/10.1109/ROBOT.1995.525582
  9. C. R. Carignan and D. L. Akin, 'Autonomous formation flight,' IEEE Control System Magazine, vol. 20, no. 6, pp. 34-44, 2000 https://doi.org/10.1109/37.887447
  10. D. J. Stilwell and B. E. Bishop, 'Platoons of underwater vehicles,' IEEE Control System Magazine, vol. 20, no.6, pp. 45-52, 2000 https://doi.org/10.1109/37.887448
  11. G. T. Anderson and M. R. Clark, 'Navigation of autonomous robots with an intelligent Oscillator Controller,' Proc. of the IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, pp. 1003-1007, 1999 https://doi.org/10.1109/AIM.1999.803309
  12. A. Mogilner and L. Edelstin-Keshet, 'Spatio-angular order in population of self-aligning objects: formation of oriented patches,' Physica D, vol. 89, pp. 346-367, 1996 https://doi.org/10.1016/0167-2789(95)00207-3
  13. H. Levine, W. Rappel and I. Cohen, 'Self-organized in systems of self-propelled particles,' Physical Review E, vol. 63, pp. 017101(4), 2001
  14. R. A. Alejandro and P. V. Vicente, 'Adaptive control with impedance of cooperative multi-robot system,' Proc. of the IEEE Int. Conf. on Robotics & Automation, pp. 1522-1527, 1998 https://doi.org/10.1109/ROBOT.1998.677335
  15. M. R. Clark and G. T. Anderson, 'Coupled Oscillator control of autonomous mobile robots,' Autonomous Robots, no. 9, pp. 189-198, 2000
  16. A. T. Hayes and P. D. Tabatabaei, 'Self-organized flocking with agent failure: off-line optimization and demonstration with real robots,' Proc. of the IEEE int. Conf. on Robotics & Automation, pp. 3900-3905, 2002 https://doi.org/10.1109/ROBOT.2002.1014331
  17. R. Vaughan, N. Sumpter, J. Henderson, A. Frost and S. Cameron, 'Robot control of animal flocks,' Proc. of the IEEE ISIC/CIRA/ISAS Joint Conference, pp. 277-282, 1998 https://doi.org/10.1109/ISIC.1998.713674
  18. L. E. Parker, 'Cooperative robotics for multi-target observation,' Intelligent Automation and Soft Computing, special issue on Robotics Research at Oak Ridge National Laboratory, vol. 5, no. 1, pp. 5-19, 1999
  19. B. B. Werger and M. J. Mataric, 'Broadcast of local eligibility for multi-target observation,' Proc. on Distributed Autonomous Robotic Systems, pp. 347-356, 2000
  20. Y. Zhong, J. Liang, G. Guochang, R. Zhang and H. Yang, 'An implementation of evolutionary computation for path planning of cooperative mobile robots,' Proc. on Intelligent Control and Automation, 2002 https://doi.org/10.1109/WCICA.2002.1021392
  21. K. Macek, I. Petrovic and N. Peric, 'A reinforcement learning approach to obstacle avoidance of mobile robots,' Int. Workshop on Advanced Motion Control, pp. 462 -466, 2002 https://doi.org/10.1109/AMC.2002.1026964
  22. S. Nolfi and D. Floreano, Evolutionary Robotics, The MIT Press, 2000
  23. T. Balch and M. Hybinette, 'Behavior-based coordination of large-scale robot formations,' Proc. Fourth Int. Conf. on Multi Agent Systems, pp. 363-364, 2000 https://doi.org/10.1109/ICMAS.2000.858476
  24. M. Egerstedt and X. Hu, 'A hybrid control approach to action coordination for mobile robots,' Automatica, vol. 38, pp. 125-130, 2002 https://doi.org/10.1016/S0005-1098(01)00185-6
  25. M. Egerstedt and X. Hu, 'Formation Constrained multiagent control,' IEEE Trans. on Robotics and Automation, vol. 17, no. 6, pp. 947-951, 2001 https://doi.org/10.1109/70.976029
  26. D. H. Kim and S. Shin, New Repulsive Potential Function with an Angle Distribution for Local Path Planning, Advanced Robotics, vol. 19, no. 9, Nov. 2005, to appear
  27. D. H. Kim and S. Shin, Local Path Planning Using a New Artificial Potential Function Configuration and Its Analytical Design Guidelines, Advanced Robotics, vol. 20, no. 1, Jan. 2006, to appear
  28. P. Vadakkepat, K. C. Tan, and M. Wang, Evolutionary artificial potential fields and their application in real time robot path planning, Congress of Evolutionary Computation (CEC2000), pp. 256-263, 2000 https://doi.org/10.1109/CEC.2000.870304
  29. W. Spears, D. Spears, J. Hamann, and R. Heil, Distributed, physics-based control of swarms of vehicles, Autonomous Robots, vol. 17, no. 3, 2004
  30. S. X. Yang and M. Q. Meng, Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach, IEEE Trans. on Neural Networks, vol. 14, no. 6, pp. 1541-1552, 2003 https://doi.org/10.1109/TNN.2003.820618
  31. C. Unsal and J. S. Bay, Spatial self-organization in large populations of mobile robots, IEEE International Symposium on Intelligent Control, Columbus, Ohio, August, pp. 249-254, 1994. http://www.cs.cmu.edu/unsal/publications/spatial.html https://doi.org/10.1109/ISIC.1994.367809
  32. D. Dudenhoeffer and M. Jones, A formation behavior for large-scale micro-robot force deployment, Proc. of the 2000 Winter Simulation Conf., pp. 972-982, 2000 https://doi.org/10.1109/WSC.2000.899900

피인용 문헌

  1. Potential-function-based shape formation in swarm simulation vol.12, pp.2, 2014, https://doi.org/10.1007/s12555-013-0133-6