通信约束下多无人机协同搜索航迹优化

doi:10.12305/j.issn.1001-506X.2026.05.27

摘要/Abstract

摘要：

针对多无人机（unmanned aerial vehicle，UAV）协同目标搜索复杂约束航迹规划问题，提出融合通信距离衰减与障碍物遮挡效应的多约束航迹规划模型，并设计一种混沌自适应周期能量哈里斯鹰优化（chaotic adaptive cycle Harris hawks optimization，CACHHO）算法。首先，在传统的路径长度、机动特性、避障避碰等约束基础上，引入视距（line-of-sight，LOS）与非LOS（none-LOS，NLOS）通信的动态权重机制，通过障碍物穿透损耗模型量化通信质量衰减效应，实现复杂环境下通信约束的精细化建模。其次，通过引进混沌映射、能量周期性递减和权重因子使算法实现全局探索与局部开发的自适应平衡，提高算法跳出局部最优的能力。最后，仿真实验对比显示，CACHHO算法较传统哈里斯鹰算法优化精度提高8.67%，任务成功率提高23%。仿真结果表明，该算法在多UAV协同搜索航迹优化问题具有显著优势，为多UAV在复杂地形中协同搜索提供了理论支撑与技术方案。

关键词: 多无人机, 协同搜索, 通信约束, 改进哈里斯鹰算法, 混沌映射

Abstract:

Aiming at the complex constrained path planning problem of multi-unmanned aerial vehicles （UAVs） cooperative target searching, a multi-constrained track planning model that integrates communication distance attenuation and obstacle occlusion effects is proposed. A chaotic adaptive cycle Harris hawks optimization （CACHHO） algorithm is designed. Firstly, based on traditional constraints, such as path length, maneuvering characteristics, obstacle avoidance, and collision avoidance, the dynamic weight mechanism of line-of-sight （LOS） and non-LOS （NLOS） communication is introduced, and the communication quality attenuation effect is quantified through the obstacle penetration loss model. It realizes the fine modeling of communication constraints in complex environment. Secondly, by introducing chaotic mapping, periodic energy decline and weight factor, the algorithm can achieve the adaptive balance between global exploration and local development, and improve the ability of the algorithm to jump out of the local optimal. Finally, through simulation experiments and comparisons, it is found that the CACHHO algorithm improves the optimization accuracy by 8.67% and the task success rate by 23% compared to the traditional Harris hawk optimization algorithm. This indicates that the algorithm has significant advantages in the problem of multi-UAV cooperative search path optimization, and provides theoretical support and technical solutions for multi-UAV cooperative search in complex terrain.

Key words: multi-unmanned aerial vehicles （UAVs）, cooperative search, communication constraints, improved Harris eagle algorithm, chaotic mapping

中图分类号:

V 279

杨秀霞, 姚文强, 张毅, 于浩. 通信约束下多无人机协同搜索航迹优化[J]. 系统工程与电子技术, 2026, 48(5): 1715-1727.

Xiuxia YANG, Wenqiang YAO, Yi ZHANG, Hao YU. Optimization of multi-UAV cooperative search paths under communication constraints[J]. Systems Engineering and Electronics, 2026, 48(5): 1715-1727.

图/表 21

图1

图2

图3

图4

图5

图6

图7

图8

表1

表2

表3

测试数据结果"

函数	CACHHO	NHPSO	HHO	ABC	WPA	PSO	ACO
$ {f_1} $	0.00E+00	2.61E−23	0.00E+00	6.40E−15	0.00E+00	1.24E+01	8.30E+02
$ {f_1} $	±0.00E+00	±5.03E−23	±0.00E+00	±1.41E−16	±0.00E+00	±9.10E−01	±8.47E+01
$ {f_2} $	2.82E−10	2.21E+01	6.05E−08	2.66E+01	2.50E+01	3.05E+01	1.84E+02
$ {f_2} $	±4.74E-09	±1.74E+00	±9.12E−08	±3.31E+01	±2.69E+01	±5.92E+01	±1.16E+01
$ {f_3} $	0.00E+00	1.12E−15	0.00E+00	1.96E−13	0.00E+00	4.58E+00	4.72E+01
$ {f_3} $	±0.00E+00	±1.39E−15	±0.00E+00	±7.28E−13	±0.00E+00	±1.02E+00	±9.64E+00
$ {f_4} $	9.66E−07	1.49E−03	4.08E−06	6.15E−01	1.41E−04	1.30E−03	9.58E−03
$ {f_4} $	±1.33E-08	±4.74E−04	±4.95E−06	±7.55E−01	±2.76E−04	±4.34E−03	±8.39E−03
$ {f_5} $	0.00E+00	6.24E−36	0.00E+00	1.60E+01	0.00E+00	0.00E+00	5.25E+02
$ {f_5} $	±0.00E+00	±1.49E−35	±0.00E+00	±3.60E+01	±0.00E+00	±0.00E+00	±1.77E+02
$ {f_6} $	0.00E+00	7.93E−12	0.00E+00	1.81E−01	9.40E−323	5.09E−01	7.09E+00
$ {f_6} $	±0.00E+00	±2.52E−11	±0.00E+00	±5.03E−01	±8.30E−324	±5.94E−01	±2.95E+00
$ {f_7} $	4.44E−16	8.88E−13	9.37E−12	1.79E+00	4.00E−15	5.39E+00	1.79E+01
$ {f_7} $	±3.93E−15	±7.09E−13	±9.35E−15	±1.12E+00	±7.08E−14	±6.22E+00	±5.31E+01
$ {f_8} $	3.82E−04	1.18E+01	3.86E−04	1.86E+03	6.51E+03	6.30E+03	2.57E+03
$ {f_8} $	±7.59E−04	±3.55E+00	±3.56E−05	±1.03E+02	±8.40E+02	±3.61E+02	±2.59E+02
$ {f_9} $	0.00E+00	8.93E−08	0.00E+00	1.31E+01	0.00E+00	2.12E+01	1.90E+02
$ {f_9} $	±0.00E+00	±5.01E−07	±0.00E+00	±6.14E+00	±0.00E+00	±8.88E+00	±7.34E+01
$ {f_{10}} $	0.00E+00	8.05E−07	0.00E+00	1.89E+01	0.00E+00	3.50E+01	1.21E+02
$ {f_{10}} $	±0.00E+00	±2.17E−06	±0.00E+00	±1.96E+00	±0.00E+00	±5.73E+00	±1.96E+01
$ {f_{11}} $	0.00E+00	0.00E+00	0.00E+00	2.66E−02	0.00E+00	9.39E+00	1.81E+01
$ {f_{11}} $	±0.00E+00	±0.00E+00	±0.00E+00	±3.27E−02	±0.00E+00	±1.77E+00	±4.20E+00
$ {f_{12}} $	6.51E−10	1.05E−24	7.65E−08	3.56E−21	2.32E+01	2.60E+00	1.81E+01
$ {f_{12}} $	±5.48E−10	±1.65E−24	±7.20E−08	±3.95E−21	±7.12E+00	±4.89E+00	±7.26E+00
$ {f_{13}} $	5.16E−10	1.47E−23	2.90E−07	1.07E−15	2.89E−01	1.00E−01	7.04E+03
$ {f_{13}} $	±3.97E−10	±2.68E−23	±9.37E−08	±5.66E−14	±3.08E−02	±2.41E−01	±9.44E+01

表3

图9

图10

表4

表5

图11

图12

图13

表6

图14

图15

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测试函数	名称	特征	最小值	搜索空间
$ {f_1} $	Sphere	单峰	0	[−100,100]
$ {f_2} $	Rosenbrock	单峰	0	[−2.048,2.048]
$ {f_3} $	Schwefel P2.22	单峰	0	[−10,10]
$ {f_4} $	Quartic noise	单峰	0	[−1.28,1.28]
$ {f_5} $	De Jong	单峰	0	[−1.28,1.28]
$ {f_6} $	Alpine	多峰	0	[−10,10]
$ {f_7} $	Ackley	多峰	0	[−32,32]
$ {f_8} $	Schwefel	多峰	0	[−500,500]
$ {f_9} $	Rastrigin	多峰	0	[−5.12,5.12]
$ {f_{10}} $	Noncontinuous Rastrigin	多峰	0	[−5.12,5.12]
$ {f_{11}} $	Weierstrass	多峰	0	[−0.5,0.5]
$ {f_{12}} $	Penalized 1	多峰	0	[−50,50]
$ {f_{13}} $	Penalized 2	多峰	0	[−50,50]

坐标点	UAV1	UAV2	UAV3	UAV4	UAV5
$ ({x_0},{y_0}) $	（20,80）	（8,60）	（5,47）	（5,40）	（10,20）
$ ({x_{\text{d}}},{y_{\text{d}}}) $	（85,50）	（85,50）	（85,50）	（85,50）	（85,50）

障碍物序号	类型	位置
障碍物1	圆形	（20，55），r=6
障碍物2	圆形	（65，70），r=6
障碍物3	圆形	（70，40），r=8
障碍物4	矩形	（22，32），l=8，w=11
障碍物5	矩形	（40，60），l=8，w=11

参数	CACHHO	HHO	ABC	WPA	PSO	ACO
AOFV	89.12	97.58	102.41	102.72	104.27	119.44
OOFV	80.182	86.65	90.38	93.81	96.73	106.25
WOFV	103.60	111.44	126.30	125.32	129.60	150.41
Std	2.88	3.79	5.22	4.71	5.07	6.20
IP/%	25.39	18.30	13.26	14.00	12.70	—
任务完成率/%	83	60	63	57	60	53

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