弱信息交互条件下的无人机集群决策方法

王子泉; 李杰; 李娟; 刘畅

doi:10.13700/j.bh.1001-5965.2022.0066

弱信息交互条件下的无人机集群决策方法

doi: 10.13700/j.bh.1001-5965.2022.0066

北京理工大学机电学院，北京 100081

基金项目: 国家自然科学基金(62003043,62373053)；北京理工大学青年教师学术启动计划(XSQD-202102003)

详细信息

通讯作者:
E-mail：juanli@bit.edu.cn

中图分类号: V279
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出版历程
- 收稿日期: 2022-02-03
- 录用日期: 2022-05-21
- 网络出版日期: 2023-09-14
- 整期出版日期: 2023-12-31

UAV swarm decision methods under weak information interaction conditions

School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

Funds: National Natural Science Foundation of China (62003043,62373053); Beijing Institute of Technology Research Fund Program for Young Scholars (XSQD-202102003)

More Information

Corresponding author: E-mail：juanli@bit.edu.cn

摘要

摘要:
随着无人系统与智能技术的发展，作为无人系统的典型应用之一的无人机集群，在民用与军事领域的应用前景越来越广阔，当集群规模较大时，传统的组网通信方式会受到带宽、干扰等限制，极大影响无人机集群的协同作战效能。基于此，提出一种弱信息交互条件下的无人机集群决策模型(WIIUSM)，不依赖无人机之间的双向数据交互，仅依靠单向视觉感知的方式实现期望的集群行为。建立了弱信息交互的无人机集群模型，采用改进后的遗传算法(IGA)作为优化方法对决策模型进行优化。以区域搜索任务为例进行仿真测试，将所提方法与基于顶层规划的蛇形方法进行对比，证明了所提方法在搜索效率层面的有效性；测试了不同比例无人机失效条件下搜索效率的下降程度，与蛇形方法进行对比，证明所提方法具有一定的鲁棒性。
- 无人机集群 /
- 弱信息交互 /
- 集群决策 /
- 遗传算法 /
- 区域搜索
Abstract:
The development of unmanned systems and intelligent technology has presented a broad application prospect of UAV swarms, one of the typical applications of unmanned systems in both civilian and military fields. When the swarm size is large, however, the traditional networking communication method will be limited by bandwidth and interference, which greatly affects the cooperative combat effectiveness of UAV swarms. This paper proposes a weak information interaction UAV swarm model (WIIUSM), not relying on two-way data interaction between UAVs but achieving the desired swarm behavior by using only one-way visual perception. Firstly, this paper establishes a weak information-interaction UAV swarm model. Next, an improved genetic algorithm (IGA) is used as an optimization method for the decision model, and several simulation tests are conducted with the area search task. A comparison with the snake search method based on top-level planning reveals the effectiveness of search efficiency of the proposed method. The degradation of search effectiveness under the conditions of different proportions of UAV failure is also tested, showing the robustness of our methods compared with the snake method.
- UAV swarm /
- weak information interaction /
- swarm decision /
- genetic algorithm /
- area search

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图 1 无人机各模型关系

Figure 1. Model relations of UAV

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图 2 无人机二维空间感知范围示意图

Figure 2. Schematic of two-dimensional space perception range of UAV

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图 3 行为原型的组成

Figure 3. The composition of behavior prototype

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图 4 IGA流程

Figure 4. Flow chart of IGA

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图 5 不同变异参数的优化过程

Figure 5. Optimization process for different variation parameters

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图 6 不同无人机数量下2种方法的搜索覆盖率变化

Figure 6. Change in search coverage of two methods with different numbers of UAVs

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图 7 IGA决策模型的搜索航迹

Figure 7. IGA decision model for search trajectory

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图 8 蛇形方法的搜索航迹

Figure 8. Serpentine method of search trajectory

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图 9 2种方法在不同比例无人机失效比例下的区域搜索覆盖率变化

Figure 9. Changes in search coverage for two methods with different failure ratios

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图 10 2种方法在不同失效比例下的区域搜索效率下降情况对比

Figure 10. Comparison of search efficiency degradation of two methods with different failure ratios

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表 1 搜索任务IGA优化参数设置

Table 1. IGA optimization parameter settings of search task

参数	数值
种群规模$p_{\rm{pop}}$	10
迭代次数	20
最优保持比例	0.5
适应度计算次数$n_{\rm{num}}$	5
行为原型数量	1
交叉概率	0.1
变异概率$m_{\rm{r}}$	0.4，0.9
变异比率$ r $	0.05，0.15，0.25

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表 2 不同变异参数下种群适应度变化

Table 2. Changes in population fitness with different variation parameters

r	初代平均适应数值		末代平均适应数值		增长率/%
r	m_r=0.4	m_r=0.9	m_r=0.4	m_r=0.9	m_r=0.4	m_r=0.9
0.05	0.696	0.649	0.738	0.746	5.93	14.9
0.15	0.656	0.653	0.708	0.738	7.97	13.1
0.25	0.636	0.629	0.721	0.693	13.3	10.3

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表 3 搜索任务最优个体的决策模型参数

Table 3. Decision model parameters for optimal individual of search task

$ {C}_{1} $	$ {C}_{2} $	$ {W}_{1} $	$ {W}_{2} $	$ {W}_{3} $	$ {W}_{4} $
0.07	0.07	0.87	0.22	0.78	0.56

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表 4 区域搜索任务场景设置

Table 4. Scenario setting of regional search task

参数	数值
无人机数量$ n $	1，3，5，10，20
感知视场角$ \psi $/(°)	120
感知视场半径$ {r}_{1},{r}_{2} $/km	0.1，0.5
区域范围$ {E}_{x}\times {E}_{y} $/(km×km)	4×4
无人机初始位置${\boldsymbol{p}}$/km	$ x,y\in \left[-1.8,-1.4\right] $
无人机初始速度${\boldsymbol{v}}$/(k·m⁻¹)	$ {v}_{x},{v}_{y}\in \left[\mathrm{0,0.01}\right] $
仿真步长$ {t}_{\max} $	3000

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