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

Abstract

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

CLC Number:

V 279

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.

Figures/Tables 21

Fig.1

Fig.2

Fig.3

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Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Table 2

Table 3

Test data result"

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

Table 3

Fig.9

Fig.10

Table 4

Table 5

Fig.11

Fig.12

Fig.13

Table 6

Fig.14

Fig.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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Optimization of multi-UAV cooperative search paths under communication constraints

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算法	主要参数
CACHHO	$ \mu =1.999，\beta =1.5，{k}=2 $
HHO	$ \beta =1.5，{E=2} \left( 1-\dfrac{t}{T}\right) $
NHPSO	${{{w}}_0} = 0.9，{{{w}}_1} = 0.4，{{c}} = 1.494，{{n}} = 7， {{{m}}_0}= 4 $
ABC	$ {\text{limit = 20}} $
WPA	$ \alpha {{ = 2 - }}\dfrac{{2t}}{T} $
PSO	$ \omega =0.8，c1=2，c2=2 $
ACO	$ \alpha =1，\beta =2，\rho =0.1，{Q}=1 $