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Multi-UAV Task Assignment Based on Quantum Genetic Algorithm
Wang Zheng Yang 1 and Yan Xin 1
Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1824 , The 2020 International Conference on Artificial Intelligence and Application Technologies (AIAT 2020) 26-29 December 2020, Tokyo, Japan Citation Wang Zheng Yang and Yan Xin 2021 J. Phys.: Conf. Ser. 1824 012010 DOI 10.1088/1742-6596/1824/1/012010
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1 School of Computer Science and Technology, Wuhan University of Technology, Hubei Wuhan 435000, China
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Multi UAV cooperation is an important application of multi UAV cooperation to complete complex tasks. In the aspect of multi UAV system coordination consists of some problems such as difficult to describe complex tasks, difficult to allocate load balancing, and difficult to model tasks. Therefore, making full use of all UAV resources and reasonable task modeling and task allocation to minimize the resource consumption of UAV system is the core problem of multi UAV cooperation. Task allocation is one of the important links of UAV cooperation, which has an important impact on the overall combat effectiveness of the system. This paper establishes the optimization model of multi UAV cooperative task allocation, and then designed a hybrid task allocation method. Quantum genetic algorithm is used for global task allocation in the initial state, and the grouping optimization strategy of hybrid frog leaping algorithm is used to greatly reduce the overall iteration times of the algorithm; the simulated annealing criterion is used to accept new solutions, which can better maintain the diversity of the population and help to jump out of the local extremum.
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UAV Swarm Task Assignment Method Based on Artificial Gorilla Troops Optimizer
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Optimal Task Assignment for UAV Swarm Operations in Hostile Environments
- Original Paper
- Published: 02 September 2020
- Volume 22 , pages 456–467, ( 2021 )
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- Jongyun Kim 1 ,
- Hyondong Oh ORCID: orcid.org/0000-0002-1051-9477 1 ,
- Beomyeol Yu 2 &
- Seungkeun Kim 2
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This paper proposes the engagement model and optimal task assignment algorithm for small-UAV swarm operations in hostile maritime environments. To alleviate the complexity of a real engagement environment, several assumptions are made: in the proposed engagement model, a vessel can attack the UAV within a certain range with a constant kill probability rate; and the ability of a vessel to attack UAVs is reduced if the multiple UAVs are involved. The objective function for optimal task assignment is constructed from the engagement model which estimates the total damage to vessels as the engagement outcome. Considering computational time and non-convex nature of the optimization problem, a heuristic approach, SL-PSO (social-learning particle swarm optimization), is adopted to maximize the objective function. In particular, a modified SL-PSO approach is introduced to deal with the optimization problem in a discrete domain. Numerical simulation results for two scenarios are presented to analyze the characteristics of the proposed engagement model and the performance of the optimal task assignment algorithm in the given environment.
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Acknowledgements
This research has been supported by the Defense Challengeable Future Technology Program of Agency for Defense Development, Republic of Korea, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03040570), and a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration and Agency for Defense Development, Republic of Korea (UD190018ID).
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School of Mechanical, Aerospace and Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea
Jongyun Kim & Hyondong Oh
Department of Aerospace Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 305-764, South Korea
Beomyeol Yu & Seungkeun Kim
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Kim, J., Oh, H., Yu, B. et al. Optimal Task Assignment for UAV Swarm Operations in Hostile Environments. Int. J. Aeronaut. Space Sci. 22 , 456–467 (2021). https://doi.org/10.1007/s42405-020-00317-z
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Received : 01 January 2020
Revised : 09 June 2020
Accepted : 31 July 2020
Published : 02 September 2020
Issue Date : April 2021
DOI : https://doi.org/10.1007/s42405-020-00317-z
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