Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Path Planning of Swarm Mobile Robots Using Firefly Algorithm

Firefly Algorithm을 이용한 군집 이동 로봇의 경로 계획

  • 김휴찬 (캐논코리아 비즈니스 솔루션 기구설계팀) ;
  • 김제석 (한양대학교 자동차공학과) ;
  • 지용관 (현대모비스 ADAS설계팀) ;
  • 박장현 (한양대학교 미래자동차공학과)
  • Received : 2013.02.20
  • Accepted : 2013.03.15
  • Published : 2013.05.01
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Abstract

A swarm robot system consists of with multiple mobile robots, each of which is called an agent. Each agent interacts with others and cooperates for a given task and a given environment. For the swarm robotic system, the loss of the entire work capability by malfunction or damage to a single robot is relatively small and replacement and repair of the robot is less costly. So, it is suitable to perform more complex tasks. The essential component for a swarm robotic system is an inter-robot collaboration strategy for teamwork. Recently, the swarm intelligence theory is applied to robotic system domain as a new framework of collective robotic system design. In this paper, FA (Firefly Algorithm) which is based on firefly's reaction to the lights of other fireflies and their social behavior is employed to optimize the group behavior of multiple robots. The main application of the firefly algorithm is performed on path planning of swarm mobile robots and its effectiveness is verified by simulations under various conditions.

Keywords

References

  1. F. Ducatelle,G.A. Di Caro,and L. M. Gambardella,"Cooperative self-organization in a heterogeneous swarm robotic system,"Proc.of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 87-94, Jul. 2010
  2. J. S. Kim and Y. H. Joo, "Asynchronous behavior control algorithm of the swarm robot for surrounding intruders," Journal of Institute of Control, Robotics and Systems (in Korean),vol. 18, no. 9, pp. 812-818, 2012. https://doi.org/10.5302/J.ICROS.2012.18.9.812
  3. S. J. Russell, P. Norvig, J. F. Canny, J. M. Malik, and D. D. Edwards, "Artificial intelligence: a modern approach,"Prentice hall Englewood Cliffs, NJ, pp. 1-60, 1995.
  4. G. Beni, From Swarm Intelligence to Swarm Robotics, in Swarm Robotics, Springer, Berlin Heidelberg,pp. 1-9, 2005.
  5. S. Garnier, J. Gautrais, and G. Theraulaz,"The biological principles of swarm intelligence" Swarm Intelligence, vol. 1, no. 4, pp. 3-31, Jul. 2007. https://doi.org/10.1007/s11721-007-0004-y
  6. J. H. Lee,J. W. Ahn, and C. W. Ahn, "Energy efficient cooperative foraging swarm robots using adaptive behavioral model," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 1, pp. 21-27, 2012. https://doi.org/10.5302/J.ICROS.2012.18.1.021
  7. X.S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Pr, pp. 79-90, 2008.
  8. X.S. Yang, Firefly Algorithms for Multimodal Optimization, in Stochastic Algorithms: Foundations and Applications, Springer, Berlin Heidelberg, pp. 169-178, 2009.
  9. G.K. Jati, Evolutionary Discrete Firefly Algorithm for Travelling Salesman Problem, in Adaptive and Intelligent Systems, Springer, Berlin Heidelberg, pp. 393-403, 2011.
  10. Y. AltintasandK. Erkorkmaz, "Feedrate optimization for spline interpolation in high speed machine tools,"CIRP Annals-Manufacturing Technology, vol. 52, no. 1, pp. 297-302, Jun. 2003. https://doi.org/10.1016/S0007-8506(07)60588-5
  11. H.T. Kim, Geometric Path Planning of a Mobile Robot Using B-spline,Hanyang University(in Korean), Seoul, pp. 5-16, 2009.
  12. J.O. Kim and P.K. Khosla, "Real-time obstacle avoidance using harmonic potential functions,"Robotics and Automation, IEEE Transactions, vol. 8, no. 3, pp. 338-349, Jun.1992. https://doi.org/10.1109/70.143352
  13. S. Lukasik and S. Zak, "Firefly algorithm for continuous constrained optimization tasks," Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, pp. 97-106, 2009.

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