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Multiagent Robot Systems

  • Chenguang YangEmail author
  • Hongbin MaEmail author
  • Mengyin Fu
Chapter

Abstract

In this chapter, we will first give an introduction of multiagent systems, which can serve as abstraction or simplified models for vast real-life complex systems, where local interactions among agents lead to complex global behaviors such as coordination, synchronization, formation, and so on. Then, as simple yet nontrivial examples of cooperation among robots, two typical cases of three-robot line formations are investigated and illustrated in a general mathematical framework of optimal multirobot formation. The robots moving with different speeds are expected to row on one straight line with the minimum formation time, so that the formation can be formulated in the most efficient way. Next, we investigate the hunting issue of a multirobot system in a dynamic environment. The proposed geometry-based strategy has the advantages of fast calculation and can be applied to three-dimensional space easily. At the end of the chapter, we investigate a few important problems in multirobot cooperative lifting control, and present an simulation study showing an example of four arms lifting one desk.

References

  1. 1.
    Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75(6), 1226 (1995)CrossRefGoogle Scholar
  2. 2.
    Jadbabaie, A., Lin, J., et al.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48(6), 988–1001 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Moreau, L.: Stability of multiagent systems with time-dependent communication links. IEEE Trans. Autom. Control 50(2), 169–182 (2005)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Tanner, H.G., Christodoulakis, D.K.: State synchronization in local-interaction networks is robust with respect to time delays. In: 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC’05, pp. 4945–4950. IEEE (2005)Google Scholar
  5. 5.
    Xiao, F., Wang, L.: State consensus for multi-agent systems with switching topologies and time-varying delays. Int. J. Control 79(10), 1277–1284 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Angeli, D., Bliman, P.-A.: Stability of leaderless discrete-time multi-agent systems. Math. Control Signals Syst. 18(4), 293–322 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Hongbin, M., Meiling, W., Zhenchao, J., Chenguang, Y.: A new framework of optimal multi-robot formation problem. In: Control Conference (CCC), 2011 30th Chinese, pp. 4139–4144. IEEE (2011)Google Scholar
  8. 8.
    Jia, Z., Ma, H., Yang, C., Wang, M.: Three-robot minimax travel-distance optimal formation. In: 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 7641–7646. IEEE (2011)Google Scholar
  9. 9.
    Jia, Z.C., Ma, H.B., Yang, C.G., Wang, M.L.: Three-robot minimax travel-distance optimal formation. In: 2011 50th Ieee Conference on Decision and Control and European Control Conference (Cdc-Ecc), pp. 7641–7646 (2011)Google Scholar
  10. 10.
    Wang, M.L., Jia, Z.C., Ma, H.B., Fu, M.: Three-robot minimum-time optimal line formation. In: 2011 9th IEEE International Conference on Control and Automation (ICCA 2011), pp. 1326–31. IEEE (2011)Google Scholar
  11. 11.
    Jose, G., Gabriel, O.: Multi-robot coalition formation in real-time scenarios. Robot. Auton. Syst. 60(10), 1295–1307 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang, H., Guo, Y., IEEE.: Minimal persistence control on dynamic directed graphs for multi-robot formation. In: 2012 IEEE International Conference on Robotics and Automation (2012)Google Scholar
  13. 13.
    Madden, J., Arkin, R.C., MacNulty, D.R.: Multi-robot system based on model of wolf hunting behavior to emulate wolf and elk interactions. In: 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1043–1050 (2010)Google Scholar
  14. 14.
    Weitzenfeld, A., Vallesa, A., Flores, H.: A biologically-inspired wolf pack multiple robot hunting model, pp. 90–97 (2006)Google Scholar
  15. 15.
    Ma, Y., Cao, Z.Q., Dong, X., Zhou, C., Tan, M.: A multi-robot coordinated hunting strategy with dynamic alliance. In: 21st Chinese Control and Decision Conference, pp. 2338–2342 (2009)Google Scholar
  16. 16.
    Sun, W., Dou, L.H., Fang, H., Zhang, H.Q.: Task allocation for multi-robot cooperative hunting behavior based on improved auction algorithm, pp. 435–440 (2008)Google Scholar
  17. 17.
    Gong, J.W., Qi, J.Y., Xiong, G.M., Chen, H.Y., Huang, W.N.: A GA based combinatorial auction algorithm for multi-robot cooperative hunting. In: International Conference on Computational Intelligence and Security (2007)Google Scholar
  18. 18.
    Li, J., Pan, Q.S., Hong, B.R.: A new approach of multi-robot cooperative pursuit based on association rule data mining. Int. J. Adv. Robot. Syst. 6(4), 329–336 (2009)Google Scholar
  19. 19.
    Ge, S.S., Ma, H.B., Lum, K.Y.: Detectability in games of pursuit evasion with antagonizing players. In: Proceedings of the 46th IEEE Conference on Decision and Control, 12–14 Dec. 2007, pp. 1404–1409. IEEE (2007)Google Scholar
  20. 20.
    Ma, H.B., Ge, S.S., Lum, K.Y.: Attackability in games of pursuit and evasion with antagonizing players. In: Proceedings of the 17th World Congress, International Federation of Automatic Control. IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 17. Elsevier (2008)Google Scholar
  21. 21.
    Wang, C., Zhang, T., Wang, K., Lv, S., Ma, H.B.: A new approach of multi-robot cooperative pursuit. In: Control Conference (CCC), 2013 32nd Chinese, pp. 7252–7256. IEEE (2013)Google Scholar
  22. 22.
    Choi, H.S., Ro, P.I.: A force/position control for two-arm motion coordination and its stability robustness analysis. KSME J. 8(3), 293–302 (1994)Google Scholar
  23. 23.
    Wang, X., Qin, J., Han, S., Shao, C., et al.: Coordinated dynamic load distribution for multiple robot manipulators carrying a common object system. Acta Mechanica Sinica 15(1), 119–125 (1999)Google Scholar
  24. 24.
    Agravante, D.J., Cherubini, A., Bussy, A., Kheddar, A.: Human-humanoid joint haptic table carrying task with height stabilization using vision. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Tokyo,Japan), pp. 4609–4614. IEEE (2013)Google Scholar
  25. 25.
    Pouli, R.: Robot manipulators mathematics. In: Programming and Control (1981)Google Scholar
  26. 26.
    Chien, M.C., Huang, A.C.: Adaptive impedance control of robot manipulators based on function approximation technique. Robotica 22(04), 395–403 (2004)Google Scholar

Copyright information

© Science Press and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Key Lab of Autonomous Systems and Networked Control, Ministry of EducationSouth China University of TechnologyGuangzhouChina
  2. 2.Centre for Robotics and Neural SystemsPlymouth UniversityDevonUK
  3. 3.School of AutomationBeijing Institute of TechnologyBeijingChina
  4. 4.State Key Lab of Intelligent Control and Decision of Complex SystemBeijing Institute of TechnologyBeijingChina

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