Modeling of unmanned aerial vehicles cooperative target assignment with allocation order and its solving of genetic algorithm

被引：10
|
作者：
Chen Z.-W. [1 ,2 ]
Xia S. [1 ]
Li J.-X. [1 ,2 ]
Wang H. [1 ]
Wang C.-M. [1 ]
机构：
[1] Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, Hebei
[2] National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao, 066004, Hebei
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Assignment model; Genetic algorithms; Target assignment; Unmanned aerial vehicles;
D O I：
10.7641/CTA.2018.80176
中图分类号：
V27 [各类型航空器];
学科分类号：
082503 ;
摘要：
This article is concerned with the Multi-UAVs cooperative target assignment (MUCTA) of dynamic battlefield environment. Firstly, By means of the influence of unmanned aerial vehicle (UAV) allocation order on total revenue of strike task, the updating rules of dynamic battlefield environment are designed. The cost of flight path length and task is used as penalty term in objective function, and the optimization model of UAVs cooperative target assignment with allocation order is established. Secondly, the coding method of genetic algorithm is improved based on the physical significance of the optimization model, and the MUCTA genetic algorithm is proposed. According to state transition, SDR operator is used to obtain different population of various allocation order, single mutation operator is used to adjust the correspondence relation between UAVs and targets, the methods of optimal individual selection and roulette are used to screen offspring individuals. Finally, simulation results verify the effectiveness of the algorithm. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1072 / 1082
页数：10
相关论文
共 22 条
  • [1] Beard R.W., Mclain T.W., Goodrich M., Coordinated target assignment and intercept for unmanned air vehicles, IEEE International Conference on Robotics and Automation, 3, 6, pp. 2581-2586, (2002)
  • [2] Zhou S., Yin G., Wu Q., UAV cooperative multiple task assignment based on discrete particle swarm optimization, International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 81-86, (2015)
  • [3] Hu X., Ma H., Ye Q., Et al., Hierarchical method of task assignment for multiple cooperating UAV teams, Journal of Systems Engineering and Electronics, 26, 5, pp. 1000-1009, (2015)
  • [4] Edison E., Shima T., Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms, Computers & Operations Research, 38, 1, pp. 340-356, (2011)
  • [5] Wu W., Guan Y., Guo J., Et al., Research on cooperative task assignment method used to the mission SEAD with real constraints, Control & Decision, 32, 9, pp. 1574-1582, (2017)
  • [6] Zhao M., Su X., Ma P., Et al., A unified modeling method of UAVs cooperative target assignment by complex multiconstraint conditions, Acta Automatica Sinica, 38, 12, pp. 2038-2048, (2012)
  • [7] Wang Z., Liu L., Long T., Et al., Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding, Chinese Journal of Aeronautics, 31, 2, pp. 339-350, (2018)
  • [8] Shima T., Rasmussen S.J., Sparks A.G., Et al., Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms, Computers & Operations Research, 33, 11, pp. 3252-3269, (2006)
  • [9] Turner J., Meng Q., Schaefer G., Et al., Distributed task rescheduling with time constraints for the optimization of total task allocations in a multirobot system, IEEE Transactions on Cybernetics, 99, pp. 1-15, (2017)
  • [10] Yan J., Li X., Liu B., Multi-agents cooperative task allocation with precedence constrains, Control & Decision, 30, 11, pp. 1999-2003, (2015)
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