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.
Highlights
The need for searching for hidden or lost objects arises in many applications
Complementary to the discrete search model developed by Chung and Burdick [13], in which the search objectives are to maximize the probability of detecting the targets or to minimize the search time, in the present study we consider the objective function of the min-loss type; we investigate the different search scenario and stopping rule, develop a new greedy index-based search strategy and, prove its properties
Research results in Chung and Burdick [13] and the references therein presented a Bayesian construction for the problem of searching for multiple targets by imperfect autonomous devices such as robots, wherein the probability of detection of these targets is an objective for the optimal search trajectory
Summary
The need for searching for hidden or lost objects arises in many applications. Common civilian applications are policing, control of air/water/soil pollution, fire fighting, prevention of natural disaster like floods, and others. We consider a different setting, namely, we intend to minimize the incurred loss or damage Such problem arises in military search, surveillance, and reconnaissance operations with patrol aircraft and UAVs, where physical, logistic and operational constraints limit the search time. In military search-and-detection missions of the UAVs, the problem of finding good strategies has its own specificity because, unlike manned aircraft, these mobile agents need to operate in autonomous regime, without human advice and involvement. Complementary to the discrete search model developed by Chung and Burdick [13], in which the search objectives are to maximize the probability of detecting the targets or to minimize the search time, in the present study we consider the objective function of the min-loss type; we investigate the different search scenario and stopping rule, develop a new greedy index-based search strategy and, prove its properties.
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