International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Univector Field Method based Multi-Agent Navigation for Pursuit Problem

  • Viet, Hoang Huu (Artificial Intelligence Lab, Department of Computer Engineering, Kyung Hee University) ;
  • An, Sang-Hyeok (Artificial Intelligence Lab, Department of Computer Engineering, Kyung Hee University) ;
  • Chung, Tae-Choong (Artificial Intelligence Lab, Department of Computer Engineering, Kyung Hee University)
  • Received : 2011.10.05
  • Accepted : 2012.03.03
  • Published : 2012.03.25
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Abstract

This paper presents a new approach to solve the pursuit problem based on a univector field method. In our proposed method, a set of eight agents works together instantaneously to find suitable moving directions and follow the univector field to pursue and capture a prey agent by surrounding it from eight directions in an infinite grid-world. In addition, a set of strategies is proposed to make the pursuit problem more realistic in the real world environment. This is a general approach, and it can be extended for an environment that contains static or moving obstacles. Experimental results show that our proposed algorithm is effective for the pursuit problem.

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References

  1. Benda, M., Jagannathan, B., Dodhiawalla, R., "On optimal cooperation of knowledge sources," Technical Report BCS-G2010-28, Boeing AI Center, Boeing Computer Services, Seattle, Wa., July 1986.
  2. Gasser, L., Rouquette, N., Hill, R. W., and Lieb, J., "Representing and using organizational knowledge in DAI systems," Distributed Artificial Intelligence, vol. 2, pp. 55-78, 1989.
  3. Stephens, L., and Merx, M., "Agent organization as an effectors of DAI system performance," Proc. of the Ninth Workshop on Distributed Artificial Intelligence, Eastsound, Washington, pp. 263-292, 1989.
  4. Korf, R.E., "A simple solution to pursuit games," Proc. of the 11th International Workshop on Distributed Artificial Intelligence, Glen Arbor, Mich, pp. 183-194, 1992.
  5. Reverte, J., Gallego, F., Llorens, F., "Extending Korf's Ideas on the Pursuit Problem," International Symposium on Distributed Computing and Artificial Intelligence 2008 - DCAI 2008, Advances in Intelligent and Soft Computing, vol. 50, pp. 245-249, 2009.
  6. Kyung-Dal Cho, "Learning of Emergent Behaviors in Collective Virtual Robots using ANN and Genetic Algorithm," International Journal of Fuzzy Logic and Intelligent Systems, vol. 4, no. 3, pp. 327-336, 2004. https://doi.org/10.5391/IJFIS.2004.4.3.327
  7. Kim, J. H., Kim, D. H., Kim, Y. J., Seow, K.T, Soccer Robotics, Springer-Verlag, 2004.
  8. Kim, Y. J., Kim, J. H, "Univector field navigation method for fast mobile robots in dynamic environment," Proc. of the 2002 FIRA Robot World Congress, pp.165-170, 2002.

Cited by

  1. An Adaptive Goal-Based Model for Autonomous Multi-Robot Using HARMS and NuSMV vol.16, pp.2, 2016, https://doi.org/10.5391/IJFIS.2016.16.2.95
  2. Univector field method-based multi-agent navigation for pursuit problem in obstacle environments vol.24, pp.4, 2017, https://doi.org/10.1007/s11771-017-3502-0