Abstract
This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.
Highlights
Unmanned aerial vehicle (UAV) mission allocation means to allocate and aggregate the availableUAV resources depending on the mission requirements and the resource status [1,2]
The mathematical modeling and optimizing for multi-UAV mission allocation is a key issue in the study of UAV issues [5,6,7,8]
Tian et al proposed a mathematical model with time window constraints and used a multi-objective genetic algorithm (CR-MOGA) to solve this problem [10,11]
Summary
Unmanned aerial vehicle (UAV) mission allocation means to allocate and aggregate the availableUAV resources depending on the mission requirements and the resource status [1,2]. The mathematical modeling and optimizing for multi-UAV mission allocation is a key issue in the study of UAV issues [5,6,7,8]. Mehdi et al described a mathematical model with tightly coupled missions and rigid relative timing constraints and achieved good simulation results of the model with a tabu search algorithm [9]. Tian et al proposed a mathematical model with time window constraints and used a multi-objective genetic algorithm (CR-MOGA) to solve this problem [10,11]. Steven J. and Shima presented an improved tree search algorithm for the UAV mission planning problem and completed the overall simulation experiment [12]. Meir et al considered this problem with sequential resource allocation and proposed an information gain in the mathematical model [13].
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