Track planning of multi-UAV cooperative reconnaissance based on improved genetic algorithm

被引：0
|
作者：
Li W. [1 ]
Hu Y. [1 ]
Pang Q. [1 ,2 ]
Li Y. [1 ]
Jia H. [1 ]
机构：
[1] Department of Unmanned Aerial Vehicle Engineering, Army Engineering University, Shijiazhuang
[2] Department of Weapon Engineering, Naval University of Engineering, Wuhan
来源：
Hu, Yongjiang (huyongjiang_jxxy@163.com) | 1600年 / Editorial Department of Journal of Chinese Inertial Technology卷 / 28期
关键词：
Cooperative; Genetic algorithm; Multiple unmanned aerial vehicles; Track planning;
D O I：
10.13695/j.cnki.12-1222/o3.2020.02.018
中图分类号：
学科分类号：
摘要：
Aiming at the problem of how to efficiently reconnoitre multiple types of targets for multiple unmanned aerial vehicles (multi-UAV) in complex battlefield environments, a multi-UAV cooperative reconnaissance trajectory planning algorithm based on improved genetic algorithm is proposed. Firstly, according to the reconnaissance requirements of different types of targets in the battlefield environment, the track planning model with the minimum overall time cost as the target function is established. Then, the coding, crossover and mutation of the genetic algorithm are improved to realize the centralized and integrated solution of heterogeneous target assignment and track planning. Finally, in order to improve the convergence speed of the algorithm, the fitness update strategy is added in the process of crossover and mutation operation. The simulation results verify the feasibility of the algorithm. Moreover, compared with the opposition-based genetic algorithm using double-chromosomes encoding and multiple mutation operators (OGA-DEMMO), under the simulation conditions where 4 drones observe 3 types of 10 targets, the time cost of the proposed algorithm is reduced by 5.79% and the convergence speed is increased by nearly 12 times, which effectively improves the accuracy and efficiency of trajectory planning. © 2020, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
引用
下载
收藏
页码：248 / 255
页数：7
相关论文
共 19 条
  • [1] Zhang X, Duan H., An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning, Applied Soft Computing, 26, pp. 270-284, (2015)
  • [2] Manyam S G, Rasmussen S, Casbeer D W, Et al., Multi-UAV routing for persistent intelligence surveillance and reconnaissance missions, Proc. of International Conference on Unmanned Aircraft Systems, pp. 573-580, (2017)
  • [3] Stodola Petr, Kozubek Jaroslav, Drozd Jan, Using unmanned aerial systems in military operations for autonomous reconnaissance, Proc. of International Conference on Modelling and Simulation for Autonomous Systesm, pp. 514-529, (2018)
  • [4] ALVEAR O, CALAFATE C T, ZEMA N R, Et al., A discretized approach to air pollution monitoring using UAV-based sensing, Mobile Networks and Applications, 23, 6, pp. 1693-1702, (2018)
  • [5] Zhang Y, Hu B, Li J, Et al., UAV multi-mission reconnaissance decision-making under uncertainty environment, Journal of Northwestern Polytechnical University, 34, 6, pp. 1028-1034, (2016)
  • [6] Hou Y, Liang X, He L, Et al., Cooperative area search algorithm for UAV swarm in unknown environment, Journal of Beijing University of Aeronautics and Astronautics, 45, 2, pp. 347-356, (2019)
  • [7] Sathyan A, Boone N., Comparison of approximate approaches to solving the travelling salesman problem and its application to UAV swarming, International Journal of Unmanned System Engineering, 3, 1, pp. 1-16, (2015)
  • [8] Agatz Niels, Bouman Paul, Schmidt Marie, Optimization approaches for the traveling salesman problem with drone, Transportation Science, 52, 4, pp. 1-17, (2018)
  • [9] Li J, Zhang S, Zheng Z, Et al., Research on Multi-UAV loading multi-type sensors cooperative reconnaissance task planning based on genetic algorithm, Proc. of International Conference on Intelligent Computing Theories and Application, pp. 485-500, (2017)
  • [10] GALCERAN E, CARRERAS M., A survey on coverage path planning for robotics, Robotics and Autonomous Systems, 61, 12, pp. 1258-1276, (2013)
12