Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1827-1837.doi: 10.13229/j.cnki.jdxbgxb20170704

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Distributed integrated method for mission planning of heterogeneous unmanned aerial vehicles

WU Wei-nan1(),CUI Nai-gang1,GUO Ji-feng1,ZHAO Yang-yang2   

  1. 1. Department of Aerospace Engineering, Harbin Institute of Technology, Harbin 150001,China
    2. No.624, China North Industries Group, Harbin 150001,China
  • Received:2017-07-05 Online:2018-11-20 Published:2018-12-11

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Abstract:

This paper studies the planning problem of investigation, strike and evaluation task for known targets with multi-heterogeneous fixed wing Unmanned Aerial Vehicles (UAVs). Firstly, the task execution time-consuming constraints, collaborative attack constraints are added to the cooperative multiple task assignment problem (CMTAP). Then, based on the distributed planning architecture, the integrated solution of the two sub-problems is completed by improving the gene coding method. In order to optimize the uniformity of the UAVs' planning trajectories, the total time index of the task completion is added to the cost function. The feasibility of this method is verified by the six-degree-of-freedom model and the Vector-based path following algorithm. The simulation results show the rapidity of this method.

Key words: flight vehicle design, task assignment, trajectory generation, distributed genetic algorithm, heterogeneous unmanned aerial vehicle, Dubins trajectory

CLC Number: 

  • TP29

Fig.1

Graphical representation for task execution time"

Fig.2

Genes representation for distributed genetic algorithm"

Table 1

Parameters setting for UAVs"

UAV 巡航速度
/(m·s-1)		 初始位置
和航向
/(m,m,(°))		 保持的
高度/m		 最小转弯
半径/m		 极限探测
距离/m
UAV-R 55 (0,0,0) 300 300 520
UAV-C 55 (0,0,0) 350 300 520
UAV-S 65 (0,0,0) 400 260 -
UAV-S 65 (0,0,0) 430 260 -

Table 2

Parameters setting for targets"

目标 位置/m 包括的任务 相应任务需要的
执行时间/s
1 (600,5200) {R,S,V} 5
2 (1120,5800) {R,S,S,V} 5
3 (5600,5350) {R,S,V} 5

Table 3

Parameters setting for DGA"

Np Ng Ncp Ne Ncro Pmuta
1000 500 10 100 800 0.03

Fig.3

Trajectory generated by mission planner and actual flight trajectory for each UAV"

Fig.4

Height keeping command and actual height for each UAV"

Fig.5

Heading angle command and actual heading angle for each UAV"

Table 4

Actual flight time and flight time by mission planning s"

时间 UAV1 UAV2 UAV3 UAV4
实际 297.1 304.3 100.2 236.5
规划 301.9 309.1 105.7 240.9

Fig.6

Quality score for three given algorithms based on different weight coefficient in the case which involves 4 UAVs and 3 targets"

Fig.7

Quality score for three given algorithms based on different weight coefficient in the case which involves 8 UAVs and 6 targets"

Fig.8

Convergence rate for three given algorithms in the case which involves 4 UAVs and 3 targets"

Fig.9

Convergence rate for three given algorithms in the case which involves 8 UAVs and 6 targets"

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