Cluster Computing

, Volume 22, Supplement 3, pp 7413–7421 | Cite as

Integrating predicate reasoning and reactive behaviors for coordination of multi-robot systems

  • Xuefeng DaiEmail author
  • Laihao Jiang
  • Dahui Li


To overcome computational expensive problem in coordination of multi-robot systems (MRS) for unknown environment explorations, an integrated coordinated algorithm is proposed in this paper. The algorithm integrated predicate based reasoning and reactive behaviors to realize coordination and obstacle avoidance. An MRS partitioning strategy is proposed to reduce the scale of problem. Then, an initialization strategy realizes dispersion of robots over the environment, and task assignments at the beginning. When a robot has finished its task, predicate based reasoning is used to assign task and to realize cooperative exploration among robots. Robots explore the unknown environment through a series of zigzag trajectories. To deal with obstacle avoidance, a few of reactive behaviors are defined. Supervisors are resident in middle level of a hierarchical architecture for each robot. The results are validated by computer simulations.


Multi-robot systems Predicate reasoning Reactive behaviors Coordination Zigzag trajectory 



This work was supported by the Natural Science Fund of Heilongjiang Province, China under Grant F201331 and also National Natural Science Fund of China (Grant No. 61672304). 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.: Market-based multi-robot coordination: a survey and analysis. Proc. IEEE 94(7), 1257–1270 (2006)CrossRefGoogle Scholar
  2. 2.
    Nanjanath, M., Gini, M.: Repeated auctions for robust task execution by a robot team. Robot. Auton. Syst. 58(7), 900–909 (2010)CrossRefGoogle Scholar
  3. 3.
    Ercan, U.A., Howie, C., Alfred, R., Prasad, A., Douglas, H.: Sensor-based coverage of unknown environments: incremental construction of Morse decompositions. Int. J. Robot. Res. 21(4), 345–366 (2002)CrossRefGoogle Scholar
  4. 4.
    Rekleitis, I., New, A.P., Rankin, E.S., Choset, H.: Efficient Boustrophedon multi-robot coverage: an algorithmic approach. Ann. Math. Artif. Intell. 52(2–4), 109–142 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Xu, B., Chen, L.P., Tan, Y., Xu, M.: Path planning based on minimum energy consumption for plant protection UAVs in sorties. Trans. Chin. Soc. Agric. Mach. 46(11), 36–42 (2015). (in Chinese)Google Scholar
  6. 6.
    Chen, H., He, K.F., Qian, W.Q.: Cooperative coverage path planning for multiple UAVs. Acta Aeronaut. Astronaut. Sin. 37(3), 928–935 (2016). (in Chinese)Google Scholar
  7. 7.
    Zhang, K., Collins, J.G., Barbu, A.: An efficient stochastic clustering auction for heterogeneous robotic collaborative teams. J. Intell. Robot. Syst. 72(3–4), 541–558 (2013)CrossRefGoogle Scholar
  8. 8.
    Ozturk, S., Kuzucuoglu, A.E.: Optimal bid valuation using path finding for multi-robot task allocation. J. Intell. Manuf. 26(5), 1049–1062 (2015)CrossRefGoogle Scholar
  9. 9.
    Tsalatsanis, A., Yalcin, A., Valavanis, K.P.: Dynamic task allocation in cooperative robot reams. Int. J. Adv. Robot. Syst. 6(4), 309–318 (2009)CrossRefGoogle Scholar
  10. 10.
    Burgard, W., Moors, M., Stachniss, C., et al.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005)CrossRefGoogle Scholar
  11. 11.
    Puig, D., Garcia, M.A., Wu, L.: A new global optimization strategy for coordinated multi-robot exploration: development and comparative evaluation. Robot. Auton. Syst. 59(9), 635–653 (2011)CrossRefGoogle Scholar
  12. 12.
    Julia, M., Gil, A., Reinoso, O.: A comparison of path planning strategies for autonomous exploration and mapping of unknown environments. Auton. Robot. 33(4), 427–444 (2012)CrossRefGoogle Scholar
  13. 13.
    Roy, N., Dudek, G.: Collaborative robot exploration and rendezvous: Algorithms, performance bounds and observations. Auton. Robot. 11(2), 117–136 (2001)CrossRefGoogle Scholar
  14. 14.
    Kuyucu, T., Tanev, I., Shimohara, K.: Super additive effect of multi-robot coordination in the exploration of unknown environments via stigmergy. Neurocomputing 148, 83–90 (2015)CrossRefGoogle Scholar
  15. 15.
    Iocchi, L., Nardi, D.: Salerno M. Reactivity and deliberation: a survey on multi-robot systems. In: Hannebauer, M., et al. (eds.) Reactivity and Deliberation in MAS. LNAI 2103, pp. 9–32. Springer, Berlin (2001)zbMATHGoogle Scholar
  16. 16.
    Korsah, G.A., Stentz, A., Dias, M.B.: A comprehensive taxonomy for multi-robot task allocation. Int. J. Robot. Res. 32(12), 1495–1512 (2013)CrossRefGoogle Scholar
  17. 17.
    Balch, T.R., Arkin, R.C.: Behavior based formation control for multi-robot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)CrossRefGoogle Scholar
  18. 18.
    Ramadge, P.J., Wonham, W.M.: The control of discrete event systems. Proc. IEEE 77(1), 81–98 (1989)CrossRefGoogle Scholar
  19. 19.
    Sharifi, F., Chamseddine, A., Mahboubi, H., et al.: A distributed deployment strategy for a network of cooperative autonomous vehicles. IEEE Trans. Control Syst. Technol. 23(2), 737–745 (2015)CrossRefGoogle Scholar
  20. 20.
    Schwager, M., Rus, D., Slotine, J.J.: Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment. Int. J. Robot. Res. 30(3), 371–383 (2010)CrossRefGoogle Scholar
  21. 21.
    Yamauchi, B.: Decentralized coordination for multirobot exploration. Robot. Auton. Syst. 29(2–3), 111–118 (1999)CrossRefGoogle Scholar
  22. 22.
    Dai, X.F., Jiang, L.H., Zhao, Y.: Cooperative exploration based on supervisory control of multi-robot systems. Appl. Intell. 45(1), 18–29 (2016)CrossRefGoogle Scholar
  23. 23.
    Gerkey, B.P., Mataric, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)CrossRefGoogle Scholar
  24. 24.
    Lozenguez, G., Adouane, L., Beynier, A., et al.: Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation. Auton. Robot. 40(4), 599–613 (2016)CrossRefGoogle Scholar
  25. 25.
    Andre, T., Bettstetter, C.: Collaboration in multi-robot exploration: to meet or not to meet? J. Intell. Robot. Syst. 82(2), 1–13 (2016)CrossRefGoogle Scholar
  26. 26.
    Nagarajan, T., Thondiyath, A.: An algorithm for cooperative task allocation in scalable, constrained multiple robot systems. Intell. Serv. Robot. 7(4), 221–233 (2014)CrossRefGoogle Scholar
  27. 27.
    Nieto-Granda, C., Rogers, J.G., Christensen, H.I.: Coordination strategies for multi-robot exploration and mapping. Int. J. Robot. Res. 33(4), 519–533 (2014)CrossRefGoogle Scholar
  28. 28.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)CrossRefGoogle Scholar
  29. 29.
    Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping: part II. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006)CrossRefGoogle Scholar
  30. 30.
    Cai, Z.X., Meng, Z.Q.: Fundamentals of Artificial Intelligence, 2nd edn, pp. 43–46. Higher Education Press, Beijing (2010). (in Chinese)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and Control EngineeringQiqihar UniversityQiqiharChina

Personalised recommendations