Skip to main content
Log in

Cooperative exploration based on supervisory control of multi-robot systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Dias M B, Zlot R, Kalra N, Stentz A (2006) Market-based multi-robot coordination: a survey and analysis. Proc. IEEE 94(7):1257–1270

    Article  Google Scholar 

  2. Sheng W, Yang Q, Tan J, Xi N (2006) Distributed multi-robot coordination in area exploration. Robot Auton Syst 54(12):945–955

    Article  Google Scholar 

  3. Nanjanath M, M Gini (2010) Repeated auctions for robust task execution by a robot team. Robot Auton Syst 58(7):900–909

    Article  Google Scholar 

  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):565–585

    Google Scholar 

  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):347–362

    Article  Google Scholar 

  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):1909–1914

    Article  Google Scholar 

  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. 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):369–376

    Google Scholar 

  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):650–671

    Article  Google Scholar 

  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):519–533

    Article  Google Scholar 

  11. Xu D, Zou W (2008) Perception localization and control for indoor mobile service robots. China: Science Press, Beijing, pp 300–325. in Chinese

    Google Scholar 

  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):545–554

    Article  Google Scholar 

  13. Cui R, Gao B, Guo J (2012) Pareto-optimal coordination of multiple robots with safety guarantees. Auton Robot 32:189–205

    Article  Google Scholar 

  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):121–129

    Article  Google Scholar 

  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):635–653

    Article  Google Scholar 

  16. Ramadge P J, Wonham W M (1989) The control of discrete event systems. Proc. IEEE 77(1):81–98

    Article  MathSciNet  MATH  Google Scholar 

  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 4831–4836

  18. Karimoddini H L, Chen BM, Lee TH (2011) Hybrid formation control of the unmanned aerial vehicles. Mechatronics 21(5):886– 898

    Article  Google Scholar 

  19. Roszkowska E (2007) DES-based coordination of space-sharing mobile robots. In: Moreno-Diaz R, et al. (eds) EUROCAST 2007, LNCS 4739, pp 1041–1048

  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– 1692

    Article  MathSciNet  MATH  Google Scholar 

  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 3358–3363

  22. Ebadi T, Purvis M, Purvis M (2010) A framework for facilitating cooperation in multi-agent systems. J Supercomput 51(3):393– 417

    Article  Google Scholar 

  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– 383

    Article  Google Scholar 

  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 1407–1412

  25. Lin F, Wonham W M (1990) Decentralized control and coordination of discrete-event systems with partial observation. IEEE Trans Automatic Control 35(12):1330–1337

    Article  MathSciNet  MATH  Google Scholar 

  26. Korsah G A, Stentz A, Dias M B (2013) A comprehensive taxonomy for multi-robot task allocation. Int J Robot Res 32(12):1495–1512

    Article  Google Scholar 

  27. Burgard W, Moors M, Stachniss C, Schneider FE (2005) Coordinated multi-robot exploration. IEEE Trans Robot 21(3):376– 386

    Article  Google Scholar 

  28. Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: Part I. IEEE Robot Autom Mag 13(2):99–110

    Article  Google Scholar 

  29. Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: Part I. IEEE Robot Autom Mag 13(3):108–117

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xuefeng Dai.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, X., Jiang, L. & Zhao, Y. Cooperative exploration based on supervisory control of multi-robot systems. Appl Intell 45, 18–29 (2016). https://doi.org/10.1007/s10489-015-0741-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-015-0741-3

Keywords

Navigation