Optimal Search for Hidden Targets by Unmanned Aerial Vehicles under Imperfect Inspections
- 1 Department of Logistics, Ashkelon Academic College, Ashkelon, Israel
- 2 Department of Computer Science, Holon Institute of Technology, Holon, Israel
- 3 Department of Computer Science, Holon Institute of Technology, Holon, Israel
Abstract
Assume that a target is hidden or lost in one of several possible locations and is to be found by the unmanned aerial vehicle (UAV). A target can be either a hostile object or missing personnel in remote areas. Prior probabilities of target locations are known. Inspection operations done by the UAVs are imperfect, namely, probabilities of overlooking the hidden target and probabilities of false alarms exist for any possible location. The UAV has to sequentially inspect the locations so that to find the target with the minimum loss or damage incurred by the target before it is detected subject to a required level of confidence of target identification. A fast (polynomial-time) priority-based algorithm for finding an optimal search strategy is developed.
- Stone, L.D. (1989) Theory of Optimal Search. 2nd Edition, Academic Press, New York.
- Benkoski, S.J., Monticino, M.G. and Weisinger, J.R. (1991) A Survey of the Search Theory Literature. Naval Research Logistics, 38, 469-494. http://dx.doi.org/10.1002/1520-6750(199108)38:4 3.0.CO;2-E
- Valavanis, K.P. (2008) Advances in Unmanned Aerial Vehicles, Springer, Berlin.
- Dell, R.F., Eagle, J.N., Martins, G.H.A. and Santos, A.G. (1996) Using Multiple Searchers in Constrained-Path, Moving-Target Search Problems. Naval Research Logistics, 43, 463-480. http://dx.doi.org/10.1002/(SICI)1520-6750(199606)43:4 3.0.CO;2-5
- Kress, M., Lin, K.Y. and Szechtman, R. (2008) Optimal Discrete Search with Imperfect Specificity. Mathematical Methods of Operations Research, 68, 539-549. http://dx.doi.org/10.1007/s00186-007-0197-2
- Sato, H. and Royset, J.O. (2010) Path Optimization for the Resource-Constrained Searcher. Naval Research Logistics, 57, 422-444. http://dx.doi.org/10.1002/nav.20411
- Kress, M., Royset, J.O. and Rozen, N. (2012) The Eye and the Fist: Optimizing Search and Interdiction. European Journal of Operational Research, 220, 550-558. http://dx.doi.org/10.1016/j.ejor.2012.02.016
- Wegener, I. (1985) Optimal Search with Positive Switch Cost Is NP-Hard. Information Processing Letters, 21, 49-52. http://dx.doi.org/10.1016/0020-0190(85)90108-5
- Trummel, K.E. and Weisinger, J.R. (1986) The Complexity of the Optimal Searcher Path Problem. Operations Research, 34, 324-327. http://dx.doi.org/10.1287/opre.34.2.324
- Washburn, A.R. (2002) Search and Detection (Topics in Operations Research Series). 4th Edition, INFORMS, New York.
- Song, N.O. and Teneketzis, D. (2004) Discrete Search with Multiple Sensors. Mathematical Methods of Operations Research, 60, 1-13. http://dx.doi.org/10.1007/s001860400360
- Wilson, K.E., Szechtman, R. and Atkinson, M.P. (2011) A Sequential Perspective on Searching for Static Targets. European Journal of Operational Research, 215, 219-226. http://dx.doi.org/10.1016/j.ejor.2011.05.045
- Chung, T.H. and Burdick, J.W. (2012) Analysis of Search Decision Making Using Probabilistic Search Strategies. IEEE Transactions on Robotics, 28, 132-144. http://dx.doi.org/10.1109/TRO.2011.2170333
- Kriheli, B. and Levner, E (2013) Search and Detection of Failed Components in Repairable Complex Systems under Imperfect Inspections. In: Batyrshin, I. and Mendoza, M.G., Eds., Advances in Computational Intelligence, Lecture Notes in Artificial Intelligence, Vol. 7630, Springer, Berlin, 399-410. http://dx.doi.org/10.1007/978-3-642-37798-3_35