Applied Intelligence

, Volume 45, Issue 1, pp 18–29 | Cite as

Cooperative exploration based on supervisory control of multi-robot systems

  • Xuefeng Dai
  • Laihao Jiang
  • Yan Zhao


When multiple mobile robots cooperatively explore an unknown environment, the advantages of robustness and redundancy are guaranteed. However, available traditional economy approaches for coordination of multi-robot systems (MRS) exploration lack efficient target selection strategy under a few of situations and rely on a perfect communication. In order to overcome the shortages and endow each robot autonomy, a novel coordinated algorithm based on supervisory control of discrete event systems and a variation of the market approach is proposed in this paper. Two kinds of utility and the corresponding calculation schemes which take into account of cooperation between robots and covering the environment in a minimal time, are defined. Different moving target of each robot is determined by maximizing the corresponding utility at the lower level of the proposed hierarchical coordinated architecture. Selection of a moving target assignment strategy, dealing with communication failure, and collision avoidance are modeled as behaviors of each robot at the upper level. The proposed approach distinctly speeds up exploration process and reduces the communication requirement. The validity of our algorithm is verified by computer simulations.


Automaton Coordinated algorithms Future utility Immediate utility Multi-robot systems Supervisory control 



This work was supported in part by the Natural Science Fund of Heilongjiang Province, China under Grant F201331. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.


  1. 1.
    Dias M B, Zlot R, Kalra N, Stentz A (2006) Market-based multi-robot coordination: a survey and analysis. Proc. IEEE 94(7):1257–1270CrossRefGoogle Scholar
  2. 2.
    Sheng W, Yang Q, Tan J, Xi N (2006) Distributed multi-robot coordination in area exploration. Robot Auton Syst 54(12):945–955CrossRefGoogle Scholar
  3. 3.
    Nanjanath M, M Gini (2010) Repeated auctions for robust task execution by a robot team. Robot Auton Syst 58(7):900–909CrossRefGoogle Scholar
  4. 4.
    Kaleci B, Parlaktuna O (2013) Performance analysis of bid calculation methods in multirobot market-based task allocation, Turk. J Elec Eng & Comp Sci 21(2):565–585Google Scholar
  5. 5.
    Kensler J A, Agah A (2009) Neural networks-based adaptive bidding with the contract net protocol in multi-robot systems. Appl Intell 31(3):347–362CrossRefGoogle Scholar
  6. 6.
    Yuan Q, Guan Y, Hong B, Meng X (2013) Multi-robot task allocation using CNP combines with neural network. Neural Comput Applic 23(7–8):1909–1914CrossRefGoogle Scholar
  7. 7.
    Liu L, Ji X, Zheng Z (2006) Multi-robot task allocation based on market and capability classification. Robot 28(3):337–343. (in Chinese)Google Scholar
  8. 8.
    Dai X F, Yao Z F, Zhao Y (2014) A discrete adaptive auction-based algorithm for task assignments of multi-robot systems. J Robot Mech 26(3):369–376Google Scholar
  9. 9.
    Capitan J, Spaan M T J, Merino L, Ollero A (2013) Decentralized multi-robot cooperation with auctioned POMDPs. Int J Robot Res 32(6):650–671CrossRefGoogle Scholar
  10. 10.
    Nieto-Granda Carlos, John G. Rogers III, Henrik I C (2014) Coordination strategies for multi-robot exploration and mapping. Int J Robot Res 33(4):519–533CrossRefGoogle Scholar
  11. 11.
    Xu D, Zou W (2008) Perception localization and control for indoor mobile service robots. China: Science Press, Beijing, pp 300–325. in ChineseGoogle Scholar
  12. 12.
    Kim D W, Lasky T A, Velinsky S A (2013) Autonomous multi-mobile robot system: Simulation and implementation using fuzzy logic. Int J Control Autom 11(3):545–554CrossRefGoogle Scholar
  13. 13.
    Cui R, Gao B, Guo J (2012) Pareto-optimal coordination of multiple robots with safety guarantees. Auton Robot 32:189–205CrossRefGoogle Scholar
  14. 14.
    Zhang G, Fricke G K, Garg D P (2013) Spill detection and perimeter surveillance via distributed swarming agents. IEEE/ASME Trans Mechatronics 18(1):121–129CrossRefGoogle Scholar
  15. 15.
    Puig D, Garcia M A, Wu L (2011) A new global optimization strategy for coordinated multi-robot exploration: Development and comparative evaluation. Robot Auton Syst 59(9):635–653CrossRefGoogle Scholar
  16. 16.
    Ramadge P J, Wonham W M (1989) The control of discrete event systems. Proc. IEEE 77(1):81–98MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Gamage G W, Mann G K I, Gosine R G (2009) Discrete event systems based formation control framework to coordinate multiple nonholonomic mobile robots. In: Proceedings of IEEE/RSJ international conference on intelligent robotic system (IROS), pp 4831–4836Google Scholar
  18. 18.
    Karimoddini H L, Chen BM, Lee TH (2011) Hybrid formation control of the unmanned aerial vehicles. Mechatronics 21(5):886– 898CrossRefGoogle Scholar
  19. 19.
    Roszkowska E (2007) DES-based coordination of space-sharing mobile robots. In: Moreno-Diaz R, et al. (eds) EUROCAST 2007, LNCS 4739, pp 1041–1048Google Scholar
  20. 20.
    Koo T J, Li R, Quottrup M M, Cliton C A, Izadi-Zamanabadi R, Bak T (2012) A framework for multi-robot motion planning from temporal logic specifications. Sci China Inf Sci 55(7):1675– 1692MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Jayasiri G K I M, Gosine R G (2011) Tightly-coupled multi robot coordination using decentralized supervisory control of fuzzy discrete event systems. In: Proceedings of 2011 IEEE international conference on robotics and automation. IEEE, Shanghai, China, pp 3358–3363Google Scholar
  22. 22.
    Ebadi T, Purvis M, Purvis M (2010) A framework for facilitating cooperation in multi-agent systems. J Supercomput 51(3):393– 417CrossRefGoogle Scholar
  23. 23.
    Ziparo V A, Iocchi L, Lima P U, Nardi D, Palamara P F (2011) Petri net plans: A framework for collaboration and coordination in multi-robot systems. Auton Agent Multi-Agent Syst 23(3):344– 383CrossRefGoogle Scholar
  24. 24.
    Sheng W, Yang Q (2005) Peer-to-peer multi-robot coordination algorithms: petri net based analysis and design. In: Proceedings of 2005 IEEE/ASME international conference on advanced intelligent mechatronics. Monterey, pp 1407–1412Google Scholar
  25. 25.
    Lin F, Wonham W M (1990) Decentralized control and coordination of discrete-event systems with partial observation. IEEE Trans Automatic Control 35(12):1330–1337MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Korsah G A, Stentz A, Dias M B (2013) A comprehensive taxonomy for multi-robot task allocation. Int J Robot Res 32(12):1495–1512CrossRefGoogle Scholar
  27. 27.
    Burgard W, Moors M, Stachniss C, Schneider FE (2005) Coordinated multi-robot exploration. IEEE Trans Robot 21(3):376– 386CrossRefGoogle Scholar
  28. 28.
    Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: Part I. IEEE Robot Autom Mag 13(2):99–110CrossRefGoogle Scholar
  29. 29.
    Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: Part I. IEEE Robot Autom Mag 13(3):108–117CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Centre of Networks and InformationQiqihar UniversityHeilongjiangChina
  2. 2.The School of Computer and Control EngineeringQiqihar UniversityHeilongjiangChina
  3. 3.The School of Communication and Electronic EngineeringQiqihar UniversityHeilongjiangChina

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