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Energy Efficient Cooperative Foraging Swarm Robots Using Adaptive Behavioral Model

역할 모델의 적응적 전환을 통한 협업 채집 무리 로봇의 에너지 효율 향상

  • 이종현 (성균관대학교 정보통신공학부) ;
  • 안진웅 (대구경북과학기술원 로봇시스템연구부) ;
  • 안창욱 (성균관대학교 정보통신공학부)
  • Received : 2011.09.02
  • Accepted : 2011.12.23
  • Published : 2012.01.01

Abstract

We can efficiently collect crops or minerals by operating multi-robot foraging. As foraging spaces become wider, control algorithms demand scalability and reliability. Swarm robotics is a state-of-the-art algorithm on wide foraging spaces due to its advantages, such as self-organization, robustness, and flexibility. However, high initial and operating costs are main barriers in performing multi-robot foraging system. In this paper, we propose a novel method to improve the energy efficiency of the system to reduce operating costs. The idea is to employ a new behavior model regarding role division in concert with the search space division.

Keywords

References

  1. E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: from Natural to Artificial Systems, Oxford University Press, 1999.
  2. Reynolds, "Flocks, herds, and schools: a distributed behavioral model," Computer Graphics, vol. 21, no. 4, pp. 25-34, 1987. https://doi.org/10.1145/37402.37406
  3. W. Liu, A. Winfield, J. Sa, J. Chen, and L. Dou, "Strategies for energy optimisation in a swarm of foraging robots," Second International Workshop, LNCS, vol. 4433, Springer, Heidelberg, 2006.
  4. Alan F. T. Winfield, Foraging Robots, Springer, New York, pp. 3682-3700, 2009.
  5. N. Trawny, S. I. Roumeliotis, and G. B. Giannakis, "Cooperative multi-robot localization under communication constraints," International Conference on Robotics and Automation (ICRA'09), Kobe, Japan, pp. 4394-4400, 2009.
  6. K. Lerman, "Mathematical model of foraging in a group of robots: Effect of interference," Autonomous Robots, vol. 13, no. 2, pp. 127-141, 2002. https://doi.org/10.1023/A:1019633424543
  7. J. Guerrero and G. Oliver, "Multi-robot task allocation strategies using auction-like mechanisms," Artificial Research and Development in Frontiers in Artificial Intelligence and Applications, pp. 111-122, 2003.
  8. L. Li, A. Martinoli, and Y. Abu-Mostafa, "Learning and measuring specialization in collaborative swarm systems," Adaptive Behavior, special issue on Mathematics and Algorithms of Social Interactions, vol. 12, no. 3-4, pp. 199-212, 2004.
  9. S. Garnier, J. Gautrais, and G. Theraulaz, "The biological principals of swarm intelligence," Swarm Intelligence, Springer Newyork, vol. 1, no. 1, pp. 3-31, 2007. https://doi.org/10.1007/s11721-007-0004-y
  10. D. H. Kim, "Self-organization for multi-agent groups," International Journal of Control, Automation and Systems, vol. 2, no. 3, pp. 333-342, Sep. 2004.
  11. T. Balch and R. C. Arkin, "Behavior-based formation control for multirobot teams," IEEE Transactions on Robotics and Automation, vol. 14, pp. 926-939, Dec. 1998. https://doi.org/10.1109/70.736776
  12. D. Lambrinos, R. Möller, T. Labhart, R. Pfeifer, and R. Wehner, "A mobile robot employing insect strategies for navigation," Robotics and Autonomous Systems, vol. 30, no. 1-2, pp. 39-64, 2000. https://doi.org/10.1016/S0921-8890(99)00064-0
  13. N. Lemmens and K. Tuyls, "Stigmergic landmark foraging," International conference on Autonomous Agents and Multi Agent Systems (AAMAS), 2009.
  14. A. F. Winfield and O. E. Holland, "The application of wireless local area network technology to the control of mobile robots," Microprocessors and Microsystems, vol. 23, pp. 597-607, 2000. https://doi.org/10.1016/S0141-9331(99)00074-5
  15. G. Théraulaz, E. Bonabeau, and J.-L. Deneubourg, "Response threshold reinforcement and division of labour in insect societies," Biological Sciences, vol. 265, pp. 327-332, 1998. https://doi.org/10.1098/rspb.1998.0299