Journal of Intelligent & Robotic Systems

, Volume 61, Issue 1–4, pp 355–367 | Cite as

Search Strategies for Multiple UAV Search and Destroy Missions

Article

Abstract

Multiple UAVs are deployed to carry out a search and destroy mission in a bounded region. The UAVs have limited sensor range and can carry limited resources which reduce with use. The UAVs perform a search task to detect targets. When a target is detected which requires different type and quantities of resources to completely destroy, then a team of UAVs called as a coalition is formed to attack the target. The coalition members have to modify their route to attack the target, in the process, the search task is affected, as search and destroy tasks are coupled. The performance of the mission is a function of the search and the task allocation strategies. Therefore, for a given task allocation strategy, we need to devise search strategies that are efficient. In this paper, we propose three different search strategies namely; random search strategy, lanes based search strategy and grid based search strategy and analyze their performance through Monte-Carlo simulations. The results show that the grid based search strategy performs the best but with high information overhead.

Keywords

UAV Coalition formation Task allocation Search 

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References

  1. 1.
    Nygard, K.E., Chandler, P.R., Pachter, M.: Dynamic network flow optimization models for air vehicle resource allocation. In: Proc. of the the American Control Conference, pp. 1853–1858. Arlington, Texas (2001)Google Scholar
  2. 2.
    Schumacher, C., Chandler, P.: UAV task assignment with timing constraints via mixed-integer linear programming. In: AIAA Unmanned Unlimited Technical Conference, Workshop and Exhibit, AIAA-2004-6410. Chicago, Illinois (2004)Google Scholar
  3. 3.
    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, AIAA-2006-6456. Keystone, Colorado (2006)Google Scholar
  4. 4.
    Alighanbari, M., How, J.: Robust decentralized task assignment for cooperative UAVs. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, Colorado, 21–24 August 2006Google Scholar
  5. 5.
    Curtis, J.W., Murphey, R.: Simultaneous area search and task assignment for a team of cooperative agents. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, AIAA 2003-5584. Austin, Texas (2003)Google Scholar
  6. 6.
    Bolonkin, A., Cloutier, J.R.: Some optimal problems in search, observation, and attack. In: AIAA Atmospheric Flight Mechanics Conference and Exhibit, AIAA 2005-6232. San Francisco, California (2005)Google Scholar
  7. 7.
    Slater, G.L.: Cooperation between UAVs in a search and destroy mission. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, AIAA 2003-5797. Austin, Texas (2003)Google Scholar
  8. 8.
    Kingston, D.B., Schumacher, C.J.: Time-dependent cooperative assignment. In: Proc. of the American Control Conference, pp. 4084–4089. Portland, Oregon (2005)Google Scholar
  9. 9.
    Sujit, P.B., George, J.M., Beard, R.W.: Multiple UAV coalition formation. In: Proc. of the American Control Conference, pp. 2010–2015. Seattle, Washington (2008)Google Scholar
  10. 10.
    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)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Yamauchi, B.: A Frontier-based approach for autonomous exploration. In: Proc. of CIRA. Monterey, California (1997)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia
  2. 2.Department of Electrical and Computer EngineeringUniversity of PortoPortoPortugal

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