Genetic algorithm based approach for multi-UAV cooperative reconnaissance mission planning problem
J Tian, L Shen, Y Zheng - International Symposium on Methodologies for …, 2006 - Springer
J Tian, L Shen, Y Zheng
International Symposium on Methodologies for Intelligent Systems, 2006•SpringerMultiple UAV cooperative reconnaissance is one of the most important aspects of UAV
operations. This paper presents a genetic algorithm (GA) based approach for multiple UAVs
cooperative reconnaissance mission planning problem. The objective is to conduct
reconnaissance on a set of targets within predefined time windows at minimum cost, while
satisfying the reconnaissance resolution demands of the targets, and without violating the
maximum travel time for each UAV. A mathematical formulation is presented for the problem …
operations. This paper presents a genetic algorithm (GA) based approach for multiple UAVs
cooperative reconnaissance mission planning problem. The objective is to conduct
reconnaissance on a set of targets within predefined time windows at minimum cost, while
satisfying the reconnaissance resolution demands of the targets, and without violating the
maximum travel time for each UAV. A mathematical formulation is presented for the problem …
Abstract
Multiple UAV cooperative reconnaissance is one of the most important aspects of UAV operations. This paper presents a genetic algorithm(GA) based approach for multiple UAVs cooperative reconnaissance mission planning problem. The objective is to conduct reconnaissance on a set of targets within predefined time windows at minimum cost, while satisfying the reconnaissance resolution demands of the targets, and without violating the maximum travel time for each UAV. A mathematical formulation is presented for the problem, taking the targets reconnaissance resolution demands and time windows constraints into account, which are always ignored in previous approaches. Then a GA based approach is put forward to resolve the problem. Our GA implementation uses integer string as the chromosome representation, and incorporates novel evolutionary operators, including a subsequence crossover operator and a forward insertion mutation operator. Finally the simulation results show the efficiency of our algorithm.
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