Trajectory Planning for UAV Swarm Target Strikes in Urban Environments

doi:10.3969/j.issn.1009-086x.2025.01.001

Abstract

Abstract:

To solve the problem of UAV target strikes in complex urban environments， a UAV target strike method based on the electric eel foraging optimization （EEFO） algorithm is proposed. First， the method sets up two scenarios， namely the sparse environment without enemy defense and the dense environment with enemy defense， and designs corresponding constraint conditions and trajectory optimization cost functions to meet the flight needs in urban environments. Then， the EEFO algorithm is used to plan a reasonable target strike trajectory for the UAV. Finally， the flight trajectory and fitness value of the EEFO algorithm are obtained and compared with those of the other five algorithms： SO， SCA， WOA， MFO， and HHO. The experimental results show that in a sparse environment with enemy defense， the EEFO algorithm has higher trajectory planning efficiency and stability than the other five algorithms. It consumes the smallest trajectory cost and converges faster. In the dense environment with enemy defense， the EEFO algorithm has the optimal planned target strike trajectory and a better convergence trend of the consumed trajectory cost compared to the other five algorithms， with the highest task completion degree and better performance.

Key words: urban environment, UAV, target strike, track planning, electric eel foraging optimization

摘要：

针对复杂城市环境下无人机目标打击问题，引入一种基于电鳗觅食优化算法的无人机目标打击方法。该方法首先设置稀疏环境无敌防守和密集环境有敌防守2种场景并设计相应的约束条件和航迹优化代价函数以符合城市环境飞行需求，然后通过电鳗觅食优化算法（electric eel foraging optimization，EEFO）为无人机规划出一条合理的目标打击轨迹，最后得到其飞行轨迹和适应度值，并与SO，SCA，WOA，MFO，HHO 5种算法进行对比。实验结果表明，在稀疏环境无敌防守场景下EEFO算法比其他五种算法具有更高的轨迹规划效率和稳定性，消耗的航迹代价最小且收敛更快；在密集环境有敌防守场景下EEFO算法与其他5种算法相比，所规划出的目标打击轨迹最优且消耗的航迹代价收敛趋势更好，任务完成度最高，具有更好的表现。

关键词: 城市环境, 无人机, 目标打击, 航迹规划, 电鳗觅食优化

CLC Number:

TJ35

Chen FEI, Liang ZHAO, Yongliang HE, Yincheng LI, Song XU. Trajectory Planning for UAV Swarm Target Strikes in Urban Environments[J]. Modern Defense Technology, 2025, 53(1): 1-10.

费陈, 赵亮, 贺拥亮, 李银城, 徐嵩. 城市环境下无人机群目标打击航迹规划[J]. 现代防御技术, 2025, 53(1): 1-10.

Figures/Tables 16

Fig. 1 3D configuration space

Fig. 2 Urban construction

Fig. 3 Ground threat diagram

Fig. 4 Flight altitude constraints

Fig. 5 Maximum range constraints

Fig. 6 Obstacle avoidance constraints navigation of oints

Fig. 7 Cubic B-spline smoothed curve

Table 1 Simulation parameter settings

参数名称	仿真取值
规划空间大小	500×500
起始点位置	（20，10，20）
目标点位置	（410，380，30）
种群大小	30
最大航程约束	1 000
无人机飞行高度约束	（0，50）
航程代价权重	0.4
高度变化代价权重	0.4
转弯角代价权重	0.2
迭代次数	50

Fig. 8 Tanget strike trajectory in dense urban invincible defensive target in sparse urban 3D environment withact enemy defense

Table 2 Simulation scenario settings

场景名称	建筑物数量	威胁区域数量
无敌防守	10	0
有敌防守	80	10

Fig. 9 Target trajectory of invincible defensive target in sparse urban 2D environment without enemy defense

Fig. 10 Convergence for enivornment without enemy defense

Fig. 11 Target strike trajectory in dense urban 3D environment wilk enemy defense

Fig. 12 Target strike trajectory in dense urban environment with enemy defense

Fig. 13 Convergence for environment with enemy defense

Fig. 14 Algorithm task completion degree in dense urban environment with enemy defense

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