基于虚拟力粒子群算法的多无人机覆盖优化

doi:10.3969/j.issn.1009-086x.2026.01.017

摘要/Abstract

摘要：

为提高无人机集群部署对灾害及战场等随机环境中的覆盖效果，提出了一种基于自适应虚拟力粒子群优化的多无人机通信覆盖部署策略。将虚拟力模型引入传统粒子群优化算法中，修改粒子群优化算法的适应度函数，实现覆盖与能耗之间的平衡；为了减少无人机之间的通信干扰，设计了基于排斥模型的自适应升力控制策略，减少了多无人机之间的重复覆盖；选择了5种算法进行对比分析，验证所提方法的有效性。实验结果表明，所提方法在确保最大区域覆盖的同时，重复覆盖率仅为21.01%，平均能量消耗仅为4.02×10² kJ，显著低于对比算法。此外，所提方法能够有效降低通信干扰和资源浪费，避免陷入局部最优，并且不受无人机数量限制，具备在无人机数量随机变化环境中部署的能力，展现出良好的环境适应性。

关键词: 多无人机, 虚拟力, 覆盖优化, 粒子群算法, 通信干扰虚拟力

Abstract:

To improve the coverage effect of UAV swarm deployment in random environments such as disasters and battlefields， this paper proposed a multi-UAV communication coverage deployment strategy based on adaptive virtual force particle swarm optimization. Firstly， the gravity model was introduced into the traditional particle swarm optimization algorithm to modify the fitness function of the particle swarm optimization algorithm to achieve the balance between coverage and energy consumption. Secondly， in order to reduce the communication interference between UAV， an adaptive lift control strategy based on the exclusion model is designed to reduce the repeated coverage between multiple UAV. Finally， five algorithms were selected for comparative analysis to verify the effectiveness of the proposed method. The experimental results show that， while ensuring the maximum area coverage， the repeated coverage rate of the proposed method is only 21.01%， and the average energy consumption is only 4.02×10² kJ， which is significantly lower than other comparison algorithms. In addition， the proposed method can effectively reduce communication interference and resource waste， avoid falling into local optimum， and is not limited by the number of UAV. It has the ability to deploy in an environment with random changes in the number of UAV， showing good environmental adaptability.

Key words: multiple drones UAV, virtual force, coverage optimization, particle swarm optimization（PSO）, algorithm, communication interference

中图分类号:

V244.12

范新明. 基于虚拟力粒子群算法的多无人机覆盖优化[J]. 现代防御技术, 2026, 54(1): 165-175.

Xinming FAN. Multi-UAV Coverage Optimization Based on Virtual Force Particle Swarm Algorithm[J]. Modern Defense Technology, 2026, 54(1): 165-175.

图/表 13

图1 多无人机区域覆盖模型

Fig.1 Multi-UAV regional coverage model

图2 算法流程图

Fig. 2 Algorithm flow chart

表1 不同参数下所提算法性能

Table 1 Performance of the proposed algorithm under different parameters

c₁	c₂	c₃	c₄	N_t	能量消耗/kJ	覆盖率/%
0.1	0.1	0.9	0.9	300	4.30×10²	99.57
0.2	0.2	0.8	0.8	269	4.26×10²	100
0.3	0.3	0.7	0.7	183	4.22×10²	100
0.4	0.4	0.6	0.6	118	4.02×10²	1 00
0.5	0.5	0.5	0.5	185	4.17×10²	100
0.6	0.6	0.4	0.4	209	4.25×10²	100
0.7	0.7	0.3	0.3	300	4.40×10²	99.74
0.8	0.8	0.2	0.2	300	3.75×10²	97.88
0.9	0.9	0.1	0.1	300	3.59×10²	90.95

图3 不同大小目标区域的覆盖率和重复覆盖率比较

Fig. 3 Comparison of coverage and repetition of target areas of different sizes

图4 不同规模目标区域的能耗比较

Fig. 4 Comparison of energy consumption in target areas of different sizes

图5 无人机数量对覆盖率和重复覆盖率的影响

Fig. 5 Impact of the number of drones on coverage and repeated coverage

图6 无人机数量对能耗比较的影响

Fig. 6 Impact of the number of drones on energy consumption comparison

图7 同一位置部署的无人机最优覆盖范围

Fig. 7 Optimal coverage of drones deployed at the same location

图8 无人机具有相同初始位置情境下的覆盖率

Fig. 8 Coverage rate of drones with the same initial position

图9 无人机初始位置相同的场景中不同算法的重复覆盖率和能耗比较

Fig. 9 Comparison of repeated coverage and energy consumption of different algorithms in scenarios with the same initial position of the drone

图10 不同位置部署的无人机最佳覆盖

Fig. 10 Optimal coverage of drones deployed at different locations

图11 不同位置的无人机场景中不同算法的覆盖率

Fig. 11 Coverage of different algorithms in drone scenes at different locations

图12 不同初始位置无人机的场景中重复覆盖率与能耗的比较

Fig. 12 Comparison of repeated coverage and energy consumption in scenarios with drones at different initial positions

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