Journal of Intelligent & Robotic Systems

, Volume 62, Issue 1, pp 125–158 | Cite as

Multiple UAV Coalitions for a Search and Prosecute Mission

  • Joel G. Manathara
  • P. B. Sujit
  • Randal W. Beard
Unmanned Systems Paper


Unmanned aerial vehicles (UAVs) have the potential to carry resources in support of search and prosecute operations. Often to completely prosecute a target, UAVs may have to simultaneously attack the target with various resources with different capacities. However, the UAVs are capable of carrying only limited resources in small quantities, hence, a group of UAVs (coalition) needs to be assigned that satisfies the target resource requirement. The assigned coalition must be such that it minimizes the target prosecution delay and the size of the coalition. The problem of forming coalitions is computationally intensive due to the combinatorial nature of the problem, but for real-time applications computationally cheap solutions are required. In this paper, we propose decentralized sub-optimal (polynomial time) and decentralized optimal coalition formation algorithms that generate coalitions for a single target with low computational complexity. We compare the performance of the proposed algorithms to that of a global optimal solution for which we need to solve a centralized combinatorial optimization problem. This problem is computationally intensive because the solution has to (a) provide a coalition for each target, (b) design a sequence in which targets need to be prosecuted, and (c) take into account reduction of UAV resources with usage. To solve this problem we use the Particle Swarm Optimization (PSO) technique. Through simulations, we study the performance of the proposed algorithms in terms of mission performance, complexity of the algorithms and the time taken to form the coalition. The simulation results show that the solution provided by the proposed algorithms is close to the global optimal solution and requires far less computational resources.


Multi UAV Coalition formation Task allocation Particle swarm optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gerkey, B., Mataric, M.J.: A formal framework for the study of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)CrossRefGoogle Scholar
  2. 2.
    Nygard, K.E., Chandler, P.R., Pachter, M.: Dynamic network flow optimization models for air vehicle resource allocation. In: Proc. of the American Control Conference, Arlington, Texas, pp. 1853–1858Google Scholar
  3. 3.
    Schumacher, C., Chandler, P.: UAV task assignment with timing constraints via mixed-integer linear programming. In: AIAA Unmanned Unlimited Technical Conference, Workshop and Exhibit, Chicago, Illinois. AIAA-2004-6410 (2004)Google Scholar
  4. 4.
    Darrah, M., Niland, W., Stolarik, B.: UAV cooperative task assignments for a SEAD mission using genetic algorithms. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, Colorado. AIAA-2006-6456 (2006)Google Scholar
  5. 5.
    Alighanbari, M., How, J.: Robust decentralized task assignment for cooperative UAVs. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, Colorado, 21–24 Aug 2006Google Scholar
  6. 6.
    Sujit, P.B., Sinha, A., Ghose, D.: Multi-UAV task allocation using team theory. In: Proc. of the IEEE Conference on Decision and Control, and European Control Conference, Seville, Spain, pp. 1497–1502 (2005)Google Scholar
  7. 7.
    Sujit, P.B., Sinha, A., Ghose, D.: Multi-UAV task allocation using negotiation. In: Autonomous Agents and Multi-Agent Systems, Hakodate, Japan, (2006)Google Scholar
  8. 8.
    Shehory, O.M.: Methods for task allocation via agent coalition formation. Artif. Intell. 101(12), 165200 (1998)MathSciNetGoogle Scholar
  9. 9.
    Sandholm, T., Larson, K., Andersson, M., Shehory, O., Tohme, F.: Coalition structure generation with worst case guarantees. Artif. Intell. 111(12), 209238 (1999)MathSciNetGoogle Scholar
  10. 10.
    Shehory, O.M., Sycara, K., Jha, S.: Multi-agent coordination through coalition formation. In: Rao, A., Singh, M., Wooldridge, M. (eds.) Lecture Notes in Artificial Intelligence, no. 1365. Intelligent Agents IV, pp. 143–154. Springer, New York (1997)Google Scholar
  11. 11.
    Vig, L., Adams, J.A.: Multi-robot coalition formation. IEEE Trans Robot 22(4), 637–649 (2006)CrossRefGoogle Scholar
  12. 12.
    Vig, L., Adams, J.A.: Market-based multi-robot coalition formation. In: Gini, M., Voyles, R. (eds.) Proc. of the International Symposium on Distributed Autonomous Robotic Systems, pp. 227–236. Springer, Minneapolis (2006)CrossRefGoogle Scholar
  13. 13.
    Vig, L., Adams, J.A.: A framework for multi-robot coalition formation. In: Proc. of the Indian International Conference on Artificial Intelligence. India (2005)Google Scholar
  14. 14.
    Parker, L.E., Tang, F.: Building multi-robot coalitions through automated task solution synthesis. In: Proc. of the IEEE Special Issue on Multi-robot Systems, vol. 94, no. 7, pp. 1289–1305 (2006)Google Scholar
  15. 15.
    Lin, J., Morse, A.S., Anderson, B.D.O.: The multi-agent rendezvous problem. In: Proc. of the IEEE Conference on Decision and Control, Maui, Hawaii, pp. 1508–1513 (2003)Google Scholar
  16. 16.
    Tiwari, A., Fung, J., Carson, J.M., Bhattacharya, R., Murray, R.M.: A framework for Lyapunov certificates for multi-vehicle rendezvous problems. In: Proc. of the American Control Conference, Boston, MA, pp. 5582–5587 (2004)Google Scholar
  17. 17.
    Lin, Z., Francis, B., Maggiore, M.: Necessary and sufficient graphical conditions for formation control of unicycles. IEEE Trans. Automat. Contr. 50(1), 121–127 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    McLain, T.W., Beard, R.W.: Coordination variables, coordination functions, and cooperative timing missions. AIAA J. Guid. Control Dyn. 28(1), 150–161 (2005)CrossRefGoogle Scholar
  19. 19.
    Furukawa, T., Bourgault, F., Whyte, H.F.D., Dissanayake, G.: Dynamic allocation and control of coordinated UAVs to engage multiple targets in a time-optimal manner. In: Proc. of the IEEE Conference on Robotics and Automation, Barcelona, Spain, pp. 2353–2358 (2005)Google Scholar
  20. 20.
    Notarstefano, G., Bullo, F.: Distributed consensus on enclosing shapes and minimum time rendezvous. In: Proc. of the IEEE Conference on Decision and Control, San Diego, California, pp. 4295–4300 (2006)Google Scholar
  21. 21.
    Dubins, L.E.: On curves of minimal length with a constraint on average curvature and prescribed initial and terminal positions and tangents. Am. J. Math. 79, 497–516 (1957)MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Kingston, D.B., Schumacher, C.J.: Time-dependent cooperative assignment. In: Proc. of the American Control Conference, Portland, Oregon, pp. 4084–4089 (2005)Google Scholar
  23. 23.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. of the Symposium on Micro Machine and Human Science, Piscataway, NJ, p. 3943 (1995)Google Scholar
  24. 24.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of the IEEE International Conference on Neural Networks, Piscataway, NJ, p. 1942–1948 (1995)Google Scholar
  25. 25.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. of the IEEE Conference on Evolutionary Computation, Anchorage, Alaska, pp. 69–73 (1998)Google Scholar
  26. 26.
    Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. In: Natural Computing, vol. 1, pp. 235–306. Springer, New York (2002)Google Scholar
  27. 27.
    Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization for integer programming. In: Proc. of the IEEE Congress on Evolutionary Computation, Honolulu, pp. 1582–1587 (2002)Google Scholar
  28. 28.
    Nemhauser, G.L., Wolsey, L.A.: Integer programming. In: Nemhauser, G.L., Rinnoy Kan, A.H.G., Todd, M.J. (eds.) Handbooks in Operations Research and Management Science. Vol. 1: Optimization. Elsevier, Amsterdam (1999)Google Scholar
  29. 29.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity, Prentice-Hall, Englewood Cliffs (1981)Google Scholar
  30. 30.
    Sujit, P.B., George, J.M., Beard, R.: Multiple UAV coalition formation. In: Proc. of the American Control Conference, Seattle, Washington (2008)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Joel G. Manathara
    • 1
  • P. B. Sujit
    • 2
  • Randal W. Beard
    • 3
  1. 1.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia
  2. 2.Department of Electrical and Computer EngineeringUniversity of PortoPortoPortugal
  3. 3.Department of Electrical and Computer EngineeringBrigham Young UniversityProvoUSA

Personalised recommendations