2009
DOI: 10.2514/1.38510
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Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithm

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Cited by 97 publications
(33 citation statements)
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“…For large-scale problems, the most efficient and scalable approaches have used some form of randomized search algorithm, such as a genetic algorithm (GA) [11][12][13][14][15]. Other heuristic approaches to the problem include simulated annealing [16] and particle swarm optimization [17].…”
Section: Introductionmentioning
confidence: 99%
“…For large-scale problems, the most efficient and scalable approaches have used some form of randomized search algorithm, such as a genetic algorithm (GA) [11][12][13][14][15]. Other heuristic approaches to the problem include simulated annealing [16] and particle swarm optimization [17].…”
Section: Introductionmentioning
confidence: 99%
“…Multiple aircraft are required to complete the mission cooperatively under the dynamic environment because it increases the probability of a successful mission [11,12]. To make the complicated mission simple, the mission is usually decomposed into a series of basic tasks so that the tasks can be executed directly by every aircraft [13][14][15]. Hierarchical planning method is effective in dealing with the complicated task allocation problem [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…The literature [5] adopted PSO route search in a two-dimensional environment, to convert a variety of threats into the ground radar detection model, and circumvent the treats, which can better handle the situation in real-time multi-threats airlines planning. The literature [6] proposed genetic algorithms to route search, it can achieve adaptive route planning in an uncertain battlefield environment, but when in a large-scale environments, it is easy to fall into local optimum, not to get the best route. The complex battlefield situation needs to be adjusted quickly.…”
Section: Introductionmentioning
confidence: 99%