A Game of Drones: Game Theoretic Approaches for Multi-robot Task Allocation in Security Missions

  • Kala Garapati
  • Juan Jesús RoldánEmail author
  • Mario Garzón
  • Jaime del Cerro
  • Antonio Barrientos
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 693)


This work explores the potential of game theory to solve the task allocation problem in multi-robot missions. The problem considers a swarm with dozens of drones that only know their neighbors, as well as a mission that consists of visiting a series of locations and performing certain activities. Two algorithms have been developed and validated in simulation: one competitive and another cooperative. The first one searches the best Nash equilibrium for each conflict where multiple UAVs compete for multiple tasks. The second one establishes a voting system to translate the individual preferences into a task allocation with social welfare. The results of the simulations show both algorithms work under the limitation of communications and the partial information, but the competitive algorithm generates better allocations than the cooperative one.


Multi-robot mission Swarm Task allocation Game theory Security 



This work is framed on SAVIER (Situational Awareness Virtual EnviRonment) Project, which is both supported and funded by Airbus Defence & Space. The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU, and from the DPI2014-56985-R project (Protección robotizada de infraestructuras críticas) funded by the Ministerio de Economía y Competitividad of Gobierno de España.


  1. 1.
    Garzón, M., Valente, J., Roldán, J.J., Cancar, L., Barrientos, A., Del Cerro, J.: A multirobot system for distributed area coverage and signal searching in large outdoor scenarios. J. Field Robot. (2015)Google Scholar
  2. 2.
    Garzón, M., Valente, J., Zapata, D., Barrientos, A.: An aerial-ground robotic system for navigation and obstacle mapping in large outdoor areas. Sensors 13(1), 1247–1267 (2013)CrossRefGoogle Scholar
  3. 3.
    Alvarado, M., Gonzalez, F., Fletcher, A., Doshi, A.: Towards the development of a low cost airborne sensing system to monitor dust particles after blasting at open-pit mine sites. Sensors 15(8), 19667–19687 (2015)CrossRefGoogle Scholar
  4. 4.
    Roldán, J.J., Garcia-Aunon, P., Garzón, M., de León, J., del Cerro, J., Barrientos, A.: Heterogeneous multi-robot system for mapping environmental variables of greenhouses. Sensors 16(7), 1018 (2016)CrossRefGoogle Scholar
  5. 5.
    Yan, Z., Jouandeau, N., Cherif, A.A.: A survey and analysis of multi-robot coordination. Int. J. Adv. Rob. Syst. 10(12), 399 (2013)CrossRefGoogle Scholar
  6. 6.
    Mosteo, A.R., Montano, L.: A survey of multi-robot task allocation. Instituto de Investigación en Ingeniería de Aragón, University of Zaragoza, Zaragoza, Spain, Technical report No. AMI-009-10-TEC (2010)Google Scholar
  7. 7.
    Jiang, Y.: A survey of task allocation and load balancing in distributed systems. IEEE Trans. Parallel Distrib. Syst. 27(2), 585–599 (2016)CrossRefGoogle Scholar
  8. 8.
    Jia, X., Meng, M.Q.H.: A survey and analysis of task allocation algorithms in multi-robot systems. In: 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2280–2285. IEEE (2013)Google Scholar
  9. 9.
    Khamis, A., Hussein, A., Elmogy, A.: Multi-robot task allocation: a review of the state-of-the-art. In: Koubâa, A., Martínez-de Dios, J. (eds.) Cooperative Robots and Sensor Networks 2015, pp. 31–51. Springer, Cham (2015)CrossRefGoogle Scholar
  10. 10.
    Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D., Camacho, D.: Performance evaluation of multi-UAV cooperative mission planning models. In: Núñez, M., Nguyen, N., Camacho, D., Trawinski, B. (eds.) Computational Collective Intelligence, pp. 203–212. Springer International Publishing, Cham (2015)CrossRefGoogle Scholar
  11. 11.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefzbMATHGoogle Scholar
  12. 12.
    Roldán, J.J., Lansac, B., del Cerro, J., Barrientos, A.: A proposal of multi-UAV mission coordination and control architecture. In: Robot 2015: Second Iberian Robotics Conference, pp. 597–608. Springer International Publishing (2016)Google Scholar
  13. 13.
    Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D., Camacho, D.: Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Comput. 1–18 (2016)Google Scholar
  14. 14.
    Yang, J., Zhang, H., Ling, Y., Pan, C., Sun, W.: Task allocation for wireless sensor network using modified binary particle swarm optimization. IEEE Sens. J. 14(3), 882–892 (2014)CrossRefGoogle Scholar
  15. 15.
    Hernández, E., Barrientos, A., Del Cerro, J.: Selective smooth fictitious play: an approach based on game theory for patrolling infrastructures with a multi-robot system. Expert Syst. Appl. 41(6), 2897–2913 (2014)CrossRefGoogle Scholar
  16. 16.
    Schneider, E., Sklar, E.I., Parsons, S., Ozgelen, A.T.: Auction-based task allocation for multi-robot teams in dynamic environments. In: Conference Towards Autonomous Robotic Systems, pp. 246–257. Springer International Publishing (2015)Google Scholar
  17. 17.
    Jiang, Y.: Concurrent collective strategy diffusion of multiagents: the spatial model and case study. IEEE Trans. Syst. Man Cybern Part C (Appl. Rev.) 39(4), 448–458 (2009)CrossRefGoogle Scholar
  18. 18.
    Padmanabhan, M., Suresh, G.R.: Coalition formation and task allocation of multiple autonomous robots. In: 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), pp. 1–5. IEEE (2015)Google Scholar
  19. 19.
    Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., Dorigo, M.: Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Auton. Agent. Multi-Agent Syst. 28(1), 101–125 (2014)CrossRefGoogle Scholar
  20. 20.
    Jackson, M.O.: A brief introduction to the basics of game theory. Stanford University (2011)Google Scholar
  21. 21.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  22. 22.
    Chamberlin, J.R., Courant, P.N.: Representative deliberations and representative decisions: proportional representation and the Borda rule. Am. Polit. Sci. Rev. 77(03), 718–733 (1983)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Kala Garapati
    • 1
  • Juan Jesús Roldán
    • 1
    Email author
  • Mario Garzón
    • 1
  • Jaime del Cerro
    • 1
  • Antonio Barrientos
    • 1
  1. 1.Centro de Automática y Robótica (UPM-CSIC)Universidad Politécnica de MadridMadridSpain

Personalised recommendations