DOI QR코드

DOI QR Code

Systematic Singular Association for Group Behaviors of a Swarm System

스웜 시스템의 그룹 행동을 위한 조직화된 단일 연합법

  • Received : 2009.01.29
  • Accepted : 2009.05.14
  • Published : 2009.06.25

Abstract

In this paper, we present a framework for managing group behaviors in multi-agent swarm systems. The framework explores the benefits by dynamic associations with the proposed artificial potential functions to realize complex swarming behaviors. A key development is the introduction of a set of flocking by dynamic association (DA) algorithms that effectively deal with a host of swarming issues such as cooperation for fast migration to a target, flexible and agile formation, and inter-agent collision avoidance. In particular, the DA algorithms employ a so-called systematic singular association (SSA) rule for fast migration to a target and compact formation through inter-agent interaction. The resulting algorithms enjoy two important interrelated benefits. First, the SSA rule greatly reduces time-consuming for migration and satisfies low possibility that agents may be lost. Secondly, the SSA is advantageous for practical implementations, since it considers for agents even the case that a target is blocked by obstacles. Extensive simulation presents to illustrate the viability and effectiveness of the proposed framework.

본 논문은 다수의 에이전트가 있는 스웜 시스템에서 효과적인 그룹행동을 다루는 연구를 한 내용이다. 많은 에이전트들이 그룹 행동을 할 때 효율적인 연합 행동을 할 수 있도록 인공 포텐셜 함수(Artificial Potential Function, 이하 APF)를 사용하였다. 제안된 연구에서는 균일한 에이전트간의 포메이션 형성, 신속한 목표물 이동, 그리고 에이전트간의 충돌 회피를 만족시키는 동적 연합(Dynamic Association, 이하 DA)알고리즘을 소개 한다. 동적 연합을 바탕으로 조직화된 단일 연합법(Systematic Singular Association, 이하 SSA)을 제안하였다. 제안된 계획에서는 장애물과 목표물 사이에도 직선시야(Line Of Signt, 이하 LOS)를 고려했다. 제안된 SSA 규칙과의 비교를 위해, 에이전트 간의 LOS만 고려하는 근거리 에이전트 선택 단일연합(Singular Association, 이하 SA)과 다(多) 연결 에이전트 선택 SA 알고리즘을 사용하였다. 비교의 결과로 제안된 방법에서 두개의 중요한 장점을 확인했다. 첫째, SSA규칙은 동료 에이전트를 잃을 가능성이 상당히 낮고 빠른 에이전트들의 빠른 이동을 만족시킨다. 둘째, 장애물과 목표물 사이의 LOS고려로 인해서 SSA규칙의 간소화는 특히 그룹 이동시 유리하다. 제안된 알고리즘의 효율성을 자세히 보여주기 위하여 다른 알고리즘과의 비교 시뮬레이션을 제공한다.

Keywords

References

  1. P. Ogren, Formations and Obstacle Avoidance in Mobile Robot Control, Ph.D. thesis, Royal Institute of Technology, 2003
  2. S. Waydo and R. M. Murray, 'Vehicle motion planning using stream functions,' In Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 2484-2491, 2003
  3. J. Sullivan, S. Waydo, and M. Campbell, 'Using stream functions for complex behavior and path generation,' In Proceedings of the AIAA Guidance, Navigation and Control Conference, Austin, Texas, 2003
  4. M. Campbell, R. D’Andrea, D. Schneider, A. Chaudhry, S. Waydo, J. Sullivan, J. Veverka and A. klochko, 'RoboFlag games using system based, hierarchical control,' In Proceedings of the American Control Conference, Denver, Colorado, pp. 661-666, 2003
  5. D. H. Kim, H. O. Wang, and S. Shin, 'Decentralized control of autonomous swarm systems using artificial potential functions : Analytical Design Guidelines,' Int. Journal of Intelligent and Robotic Systems, vol. 45, no. 4, pp. 369-394, 2006 https://doi.org/10.1007/s10846-006-9050-8
  6. G. Ye, H. O. Wang and K. Tanaka, 'Coordinated motion control of swarms with dynamic connectivity in potential flows,' In Proceedings of the 16th International Federation of Automatic Control World Congress, Prague, Czech Republic, 2005
  7. G. Ye, H.O. Wang, K. Tanaka, Z. Guan, 'Managing group behaviors in swarm systems by associations,' In Proceedings of the 25th American Control Conference, Minneapolis, Minnesota, pp. 3537-3544, 2006
  8. Y. Koren, and J. Borenstein, 'Potential field methods and their inherent limitations for mobile robot navigation,' Proc. of the IEEE int. Conf. on Robotics & Automation, pp. 1398-1404, 1991
  9. V. Gazi, and K. M. Passino, 'Stability analysis of social foraging swarms,' IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 34, no. 1, pp. 539-557, 2004 https://doi.org/10.1109/TSMCB.2003.817077
  10. 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
  11. J. S. Baras, X. Tan, and P. Hovareshti, 'Decentralized control of autonomous vehicles,' 42nd IEEE Conference on Decision and Control, Maui, Hawaii, pp. 1532-1537, 2003
  12. S. S. Ge, and Y. J. Cui, 'Dynamic motion planning for mobile robots using potential field method,' Autonomous Robots, vol. 13, no. 3, pp. 207-222, 2002 https://doi.org/10.1023/A:1020564024509
  13. W. Spears, D. Spears, J. Hamann, and R. Heil, 'Distributed, physics-based control of swarms of vehicles,' Autonomous Robots, vol. 17, no. 2-3, pp. 137-162, 2004 https://doi.org/10.1023/B:AURO.0000033970.96785.f2
  14. L. E. Kavraki and J. C. Latombe, 'Probabilistic roadmaps for robot path planning, practical motion planning in robotics: current approaches and future directions,' Gupta K. and Pobil A. del (eds), John Wiley, pp. 33-53, 1998
  15. S. Guang, S. Thomas and N. M. Amato, 'A general framework for PRM motion Planning,' In Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 21-26, 2003
  16. S. J. Russell and P. Norvig, Artificial Intelligence A Modern Approach, Prentice Hall, edition, 1995.