Cover
Vol. 15 No. 2 (2019)

Published: December 31, 2019

Pages: 101-114

Original Article

Self-Organization of Multi-Robot System Based on External Stimuli

Yousif Abdulwahab Kheerallah 1 Ali Fadhil Marhoon Mofeed Turky Rashid Abdulmuttalib Turky Rashid
1Electrical Engineering Department University of Basrah Basrah, Iraq
History
  • Received: October 31, 2019
  • Available Online: December 31, 2019
DOI

https://doi.org/10.37917/ijeee.15.2.11

Abstract

In modern robotic field, many challenges have been appeared, especially in case of a multi-robot system that used to achieve tasks. The challenges are due to the complexity of the multi-robot system, which make the modeling of such system more difficult. The groups of animals in real world are an inspiration for modeling of a multi- individual system such as aggregation of Artemia. Therefore, in this paper, the multi-robot control system based on external stimuli such as light has been proposed, in which the feature of tracking Artemia to the light has been employed for this purpose. The mathematical model of the proposed design is derived and then Simulated by V-rep software. Several experiments are implemented in order to evaluate the proposed design, which is divided into two scenarios. The first scenario includes simulation of the system in situation of attraction of robot to fixed light spot, while the second scenario is the simulation of the system in the situation of the robots tracking of the movable light spot and formed different patterns like a straight-line, circular, and zigzag patterns. The results of experiments appeared that the mobile robot attraction to high-intensity light, in addition, the multi-robot system can be controlled by external stimuli. Finally, the performance of the proposed system has been analyzed.

Keywords

References

  1. Y.-L. Lin and K.-L. Su, “Multi-robot-based intelligent security system,” Artificial Life and Robotics, vol. 16, pp. 137–141, 2011.
  2. L. Matignon and O. Simonin, “Multi-Robot Simultaneous Coverage and Mapping of Complex Scene – Comparison of Different Strategies,” Robotics Track, Stockholm, Sweden, pp. 559–567, July 2018.
  3. A. Marino, L. E. Parker, G. Antonelli, and F. Caccavale, “A decentralized architecture for multi-robot systems based on the null-space behavioral control with application to multi-robot border patrolling,” Journal of Intelligent & Robotic Systems, vol. 71, pp. 423–444, 2013.
  4. A. Khamis and A. ElGindy, “Minefield mapping using cooperative multirobot systems,” Journal of Robotics, 2012.
  5. A. Goldhoorn, A. Garrell, R. Alquézar, and A. Sanfeliu, “Searching and tracking people with cooperative mobile robots,” vol. 42, issue 4, pp. 739–759, 2018.
  6. F. Shkurti, A. Xu, M. Meghjani, J. C. Gamboa Higuera, Y. Girdhar, P. Giguere, G. Dudek, et al., “Multi-domain monitoring of marine environments using a heterogeneous robot team,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1747–1753, 2012.
  7. M. Shiomi, K. Kamei, T. Kondo, T. Miyashita, and N. Hagita, “Robotic service coordination for elderly people and caregivers with ubiquitous network robot platform,” IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), 2013.
  8. A. Filella, F. Nadal, C. Sire, E. Kanso, and C. Eloy, “Model of collective fish behavior with hydrodynamic interactions,” vol. 120, issue 19, May 2018.
  9. K. M. Schultz, K. M. Passino, and T. D. Seeley, “Mechanism of flight guidance in honeybee swarms: subtle guides or streaker bees,” The Company of Biologists, 2008.
  10. M. A. Haque, A. R. Rahmani, and M. B. Egerstedt, “Biologically inspired confinement of multi-robot systems,” International Journal of Bio-Inspired Computation, vol. 3, no. 4, 2011.
  11. Y. Katz, K. Tunstrøm, C. C. Ioannou, C. Huepe, and I. D. Couzin, “Inferring the structure and dynamics of interactions in schooling fish,” Proceedings of the National Academy of Sciences (PNAS), Nov. 15, 2011.
  12. S. Kernbach, R. Thenius, O. Kernbach, and T. Schmick, “Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system,” Adaptive Behavior, vol. 17, issue 3, pp. 237–259, 2009.
  13. Y. Kambayashi, Y. Tsujimura, H. Yamachi, M. Takimoto, and H. Yamamoto, “Inferring the structure and dynamics of interactions in schooling fish,” 2nd International Conference on System Sciences, Hawaii, 2011.
  14. A. T. Rashid, “Leader follower tracking with obstacle avoidance using circular paths algorithm,” Basrah Journal for Engineering Science, 2016.
  15. T. Krajník, M. Nitsche, J. Faigl, P. Vaněk, M. Saska, L. Přeučil, T. Duckett, and M. Mejail, “A practical multirobot localization system,” Journal of Intelligent & Robotic Systems, vol. 76, issues 3–4, pp. 539–562, 2014.
  16. M. T. Rashid, “Design model for wireless multi-mobile robot system based on modeling of collective motion of Artemia population,” Electrical Engineering Department, University of Basrah, 2011.
  17. L. Fortuna, M. Frasca, M. G. Xibilia, A. A. Ali, and M. T. Rashid, “Motion control of a population of Artemias,” Iraq Journal of Electrical and Electronic Engineering, vol. 6, no. 1, 2010.
  18. M. T. Rashid, M. Frasca, A. A. Ali, R. S. Ali, and L. Fortuna, “Artemia swarm dynamics and path tracking,” Springer, vol. 68, issue 4, pp. 555–563, 2010.
  19. O. A. Hasan, A. T. Rashid, and R. S. Ali, “Centralized approach for multi-node localization and identification,” Iraq Journal of Electrical and Electronic Engineering, vol. 12, no. 2, pp. 178–187, 2016.
  20. A. E. Parr, “A contribution to the theoretical analysis of the schooling behaviour of fishes,” Occasional Papers of the Bingham Oceanographic Collection, vol. 1, pp. 1–32, 1927.
  21. C. Wong, H. Wang, S. Li, and C. Cheng, “Fuzzy controller designed by GA for two-wheeled mobile robots,” International Journal of Fuzzy Systems, vol. 9, no. 1, 2007.