Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 4370635, 7 pages
http://dx.doi.org/10.1155/2016/4370635
Research Article

Method of Quantitative Analysis for Multirobot Cooperative Hunting Behaviors

School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai 264209, China

Received 17 February 2016; Accepted 2 August 2016

Academic Editor: Ricardo Aguilar-López

Copyright © 2016 Yong Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Gautam and S. Mohan, “A review of research in multi-robot systems,” in Proceedings of the IEEE 7th International Conference on Industrial and Information Systems (ICIIS '12), pp. 1–5, Chennai, India, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. L. E. Parker, Multiple Mobile Robot Systems, Springer, Berlin, Germany, 2008.
  3. K. Hanguen, K. Donghoon, K. Hyungjin et al., “An extended any-angle path planning algorithm for maintaining formation of multi-agent jellyfish elimination robot system,” International Journal of Control Automation and Systems, vol. 14, no. 2, pp. 598–607, 2016. View at Google Scholar
  4. A. O. de Sá, N. Nedjah, and L. M. Mourelle, “Distributed efficient localization in swarm robotics using min-max and particle swarm optimization,” Expert Systems with Applications C, vol. 50, pp. 55–65, 2016. View at Publisher · View at Google Scholar
  5. X. Wang, Z. Zeng, and Y. Cong, “Multi-agent distributed coordination control: developments and directions via graph viewpoint,” Neurocomputing, vol. 199, pp. 204–218, 2016. View at Publisher · View at Google Scholar
  6. S. Schaal and C. G. Atkeson, “Learning control in robotics,” IEEE Robotics & Automation Magazine, vol. 17, no. 2, pp. 20–29, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Stephan, J. Fink, and A. Ribeiro, “System architectures for communication-aware multi-robot navigation,” in Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pp. 6395–6399, Shanghai, China, March 2016.
  8. D. Portugal and R. P. Rocha, “Cooperative multi-robot patrol with Bayesian learning,” Autonomous Robots, vol. 40, no. 5, pp. 929–953, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. T. P. Nascimento, A. G. S. Conceicąõ, and A. P. Moreira, “Multi-Robot nonlinear model predictive formation control: the obstacle avoidance problem,” Robotica, vol. 34, no. 3, pp. 549–567, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Sergey, L. Valery, D. Sekou et al., “Multi-robot system learning based on evolutionary classification,” in Proceedings of the International Conference on Control, Mechatronics and Automation, pp. 1–4, Barcelona, Spain, December 2016.
  11. K. Chen and B. T. Han, “A survey of state space reconstruction of chaotic time series analysis,” Computer Science, vol. 34, no. 4, pp. 67–70, 2005. View at Google Scholar
  12. Z. Wang and M. Schwager, “Kinematic multi-robot manipulation with no communication using force feedback,” in Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA '16), pp. 427–432, Stockholm, Sweden, May 2016.
  13. S. El Ferik, M. Tariq Nasir, and U. Baroudi, “A Behavioral Adaptive Fuzzy controller of multi robots in a cluster space,” Applied Soft Computing, vol. 44, pp. 117–127, 2016. View at Publisher · View at Google Scholar
  14. L. Deng, X. Ma, J. Gu, and Y. Li, “Improved poly-clonal artificial immune network for multi-robot dynamic path planning,” in Proceedings of the IEEE International Conference on Information and Automation (ICIA '13), pp. 128–133, Yinchuan, China, August 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. U. Nehmzow and K. Walker, “The behaviour of a mobile robot is chaotic,” Journal of Artificial Intelligence and the Simulation of Behaviour, vol. 1, no. 4, pp. 373–388, 2003. View at Google Scholar
  16. R. Beckers, O. E. Holland, and J. L. Deneubourg, “From local actions to global tasks: stigmergy and collective robotics,” in Proceedings of the International Workshop on the Synthesis and Simulation of Living Systems, pp. 181–189, Cambridge, UK, 1994.
  17. S.-W. Seo, H.-C. Yang, and K.-B. Sim, “Behavior learning and evolution of swarm robot system for cooperative behavior,” in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM '09), pp. 673–678, Singapore, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Kobayashi, K. Nakano, T. Kuremoto, and M. Obayashi, “Cooperative behavior acquisition of multiple autonomous mobile robots by an objective-based reinforcement learning system,” in Proceedings of the International Conference on Control, Automation and Systems (ICCAS '07), pp. 777–780, Seoul, Republic of Korea, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. D.-W. Lee, S.-W. Seo, and K.-B. Sim, “Online evolution for cooperative behavior in group robot systems,” International Journal of Control, Automation and Systems, vol. 6, no. 2, pp. 282–287, 2008. View at Google Scholar · View at Scopus
  20. C. Moulin-Frier and P.-Y. Oudeyer, “Exploration strategies in developmental robotics: a unified probabilistic framework,” in Proceedings of the IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL '13), Osaka, Japan, August 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Fox, W. Burgard, H. Kruppa, and S. Thrun, “Probabilistic approach to collaborative multi-robot localization,” Autonomous Robots, vol. 8, no. 3, pp. 325–344, 2000. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Martinoli, K. Easton, and W. Agassounon, “Modeling swarm robotic systems: a case study in collaborative distributed manipulation,” The International Journal of Robotics Research, vol. 23, no. 4-5, pp. 415–436, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. U. Nehmzow, “Quantitative analysis of robot-environment interaction—towards scientific mobile robotics,” Robotics and Autonomous Systems, vol. 44, no. 1, pp. 55–68, 2003. View at Publisher · View at Google Scholar · View at Scopus
  24. J. H. Lv, J. N. Lu, and S. H. Chen, The Analyse and Application of Chaotic Time Serises, Wuhan University Press, Wuhan, China, 2002.
  25. U. Nehmzow, Robot Behaviour Design, Description, Analysis and Modelling, Springer, London, UK, 2009.
  26. L. X. Tian, J. Xu, and M. Sun, “Adaptive synchronization of energy resource system with uncertain parameters,” Journal of Jiangsu University: Natural Science, vol. 28, no. 4, pp. 879–888, 2007. View at Google Scholar
  27. G.-L. Cai, W.-H. Zhou, S. Zheng, and H.-X. Wang, “Synchronization of generalized Hénon hyperchaotic system and its application to secure communication,” Journal of Jiangsu University: Natural Science, vol. 28, no. 5, pp. 457–460, 2008. View at Google Scholar · View at Scopus
  28. N. H. Packard, J. P. Crutchfield, J. D. Farmer, and R. S. Shaw, “Geometry from a time series,” Physical Review Letters, vol. 45, no. 9, pp. 712–716, 1980. View at Publisher · View at Google Scholar · View at Scopus
  29. F. Takens, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick 1980, vol. 898 of Lecture Notes in Mathematics, pp. 366–381, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  30. Z. Y. Xie and K. J. Wang, “Selection of embedding parameters in phase space reconstruction,” in Proceedings of the 2nd International Conference on Intelligent Computing Technology and Automation (ICICTA '09), pp. 637–640, IEEE, Changsha, China, October 2009. View at Publisher · View at Google Scholar · View at Scopus