Controlling a Fleet of Unmanned Aerial Vehicles to Collect Uncertain Information in a Threat Environment

Yan Xia (), Rajan Batta () and Rakesh Nagi ()
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Yan Xia: Department of Industrial and Systems Engineering, University at Buffalo (SUNY), Buffalo, New York 14260
Rajan Batta: Department of Industrial and Systems Engineering, University at Buffalo (SUNY), Buffalo, New York 14260
Rakesh Nagi: Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801

Operations Research, 2017, vol. 65, issue 3, 674-692

Abstract: Unmanned aerial vehicles (UAVs) have been proved to be successful and efficient for information collection in a modern battlefield, especially in areas that are considered to be dangerous for human pilots. Currently, a UAV is remotely controlled by a ground station through frequent data communications, which make the current system vulnerable in a threat environment. We propose a decentralized control strategy while requiring UAVs to maintain radio silence during the entire mission. The strategy is analyzed based on a scenario where a fleet of vehicles is assigned to search and collect uncertain information in a set of regions within a given mission time. We demonstrate that a region-sharing strategy is beneficial even when there is no extra reward gained from additional information collection. Implementing a region-sharing strategy requires solving a decentralized time allocation problem, which is computationally intractable. To overcome this, an approximate formulation is developed under an independence assumption for information collected by different vehicles. This approximate formulation allows us to decompose, by vehicle, the time allocation problem, and obtain an easily implementable policy that takes on a Markovian form. We develop a sufficient condition under which the approximate formulation becomes exact. A numerical study establishes the computational efficiency of the method; only a few CPU seconds are needed for problems with a planning horizon of 300 time units and 40 regions. We further present a case study to illustrate region-sharing behaviors among UAVs while using practical parameter values. Finally, we compare the obtained policy with the optimal policy found using a complete enumeration method for small instances. Under different parameter settings, the average optimality gap ranges from 0.23% to 19.90%.

Keywords: search theory; decentralized control; resource allocation; Markovian policy; multiagent system (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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