基于<i>γ</i>随机搜索策略的无人机集群海上任务分配

吴秋实; 郭杰; 康振亮; 张宝超; 王浩凝; 唐胜景

doi:10.13700/j.bh.1001-5965.2022.0882

基于γ随机搜索策略的无人机集群海上任务分配

doi: 10.13700/j.bh.1001-5965.2022.0882

吴秋实¹,
郭杰^1, ,,
康振亮^{1, 2},
张宝超¹,
王浩凝¹,
唐胜景¹

1.
北京理工大学宇航学院，北京 100081
2.
上海机电工程研究所，上海 201109

基金项目: 上海航天科技创新基金(SAST201711)

详细信息

通讯作者:
E-mail：guojie1981@bit.edu.cn

中图分类号: V279；TP301.6
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-03
- 录用日期: 2022-12-23
- 网络出版日期: 2023-01-10
- 整期出版日期: 2024-12-31

Maritime mission assignment of UAV clusters based on γ random search strategy

1.
School of Aerospace Engineering，Beijing Institute of Technology，Beijing 100081，China
2.
Shanghai Electro-Mechanical Engineering Institute，Shanghai 201109，China

Funds: Shanghai Aerospace Science and Technology Innovation Fund (SAST201711)

More Information

Corresponding author: E-mail：guojie1981@bit.edu.cn

摘要

摘要:
针对无人机(UAV)集群海上作战态势复杂、作战任务多样、作战单元异构的特点，建立了海上无人机集群多目标任务分配优化模型，并针对该模型提出了一种基于$\gamma $随机搜索策略的改进离散粒子群算法(γ-DPSO)。将作战态势细节与复杂作战需求等引入无人机集群任务分配问题，建立契合作战场景的无人机集群任务分配作战模型；基于粒子编码矩阵，设计均衡搜索策略、$\gamma $随机搜索策略、分阶段自适应参数，提出基于$\gamma $随机搜索策略的改进离散粒子群算法，解决离散粒子群算法易陷入局部最优造成未成熟收敛的问题。仿真结果表明：针对所建立的符合海上作战特点的无人机集群多目标任务分配优化模型，所提算法可有效解决无人机集群多目标任务分配问题，所提改进策略提高了算法的收敛速度与算法精度。
- 无人机 /
- 协同任务分配 /
- 离散粒子群算法 /
- 随机搜索策略 /
- 均衡搜索策略
Abstract:
In view of the characteristics of complex maritime combat situations, diverse combat missions, and heterogeneous combat units of unmanned aerial vehicle (UAV) clusters, a multi-objective mission assignment optimization model for maritime UAV clusters was established, and an improved discrete particle swarm optimization algorithm based on $\gamma $ random search strategy (γ-DPSO) was proposed for this model. Firstly, the combat situation details and complex combat requirements were introduced into the mission assignment problem of UAV clusters, and a mission assignment combat model of UAV clusters that fitted the combat scenario was established. Secondly, based on the particle coding matrix, the equilibrium search strategy, the $\gamma $ random search strategy, and the phased adaptive parameters were designed, and the improved discrete particle swarm optimization algorithm based on the $\gamma $ random search strategy was proposed to solve the problem that the discrete particle swarm optimization algorithm was easy to fall into local optimum and caused immature convergence. The simulation results show that the proposed improved algorithm can effectively solve the multi-objective mission assignment problem of UAV clusters for the multi-objective mission assignment optimization model of UAV clusters established in this paper that meets the characteristics of maritime combat, and the proposed improved strategy improves the convergence speed and accuracy of the algorithm.
- unmanned aerial vehicle /
- cooperative mission assignment /
- discrete particle swarm optimization algorithm /
- random search strategy /
- equilibrium search strategy

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图 1 多无人机协同海上对抗任务分配问题建模

Figure 1. Modeling of cooperative maritime combat mission assignment problem of multiple UAVs

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图 2 海上无人机集群交战场景

Figure 2. Combat scenario of maritime UAV clusters

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图 3 粒子间的交叉操作

Figure 3. Intersection operation between particles

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图 4 粒子间的变异操作

Figure 4. Mutation operation between particles

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图 5 仿真初始位置设置

Figure 5. Initial position setting in simulation

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图 6 自适应惯性权重和加速系数变化曲线

Figure 6. Variation curves of adaptive inertia weight and acceleration coefficient

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图 7 总适应度值变化曲线

Figure 7. Variation of total adaptation value

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图 8 各无人机飞行航程代价变化曲线

Figure 8. Variation of flight range cost of each UAV

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图 9 仿真收敛曲线

Figure 9. Simulation convergence curve

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图 10 随机收敛曲线

Figure 10. Convergence curves of random particles

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图 11 算法对比适应度收敛曲线

Figure 11. Algorithm comparison adaptation convergence curves

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表 1 海上无人机协同任务分配优化模型基本参数

Table 1. Basic parameters of cooperative mission assignment optimization model for maritime UAVs

基本参数	我方无人机	敌方船只
个数	$ {N_{{V}}} $	$ {N_{{T}}} $
攻击半径	${R_{{{{\mathrm{a}},V}}}}$	${R_{{{{\mathrm{a}},T}}}}$
探测半径	${R_{{{{\mathrm{s}},V}}}}$	${R_{{{{\mathrm{s}},T}}}}$
命中概率	${p_{{{{\mathrm{h}},V}}}}$	${p_{{{{\mathrm{h}},T}}}}$
毁伤概率	${p_{{{{\mathrm{d}},V}}}}$	${p_{{{{\mathrm{d}},T}}}}$
耐久极限	$ {e_{{V}}} $	$ {e_{{T}}} $
携带作战载荷数量	${\varepsilon _{{V}}}$	∞

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表 2 我方无人机携带作战载荷数量与探测半径

Table 2. Number of combat payload carried by Chinese UAVs and detection radius

无人机编号	${\varepsilon _{{V}}}$	${R_{{{{\mathrm{s}},V}}}}$	无人机编号	${\varepsilon _{{V}}}$	${R_{{{{\mathrm{s}},V}}}}$
V₁	3	7	V₁₁	2	6
V₂	2	11	V₁₂	1	10
V₃	3	11	V₁₃	2	9
V₄	3	9	V₁₄	1	11
V₅	2	6	V₁₅	2	12
V₆	2	7	V₁₆	1	9
V₇	1	12	V₁₇	1	6
V₈	2	6	V₁₈	1	10
V₉	1	7	V₁₉	1	10
V₁₀	2	11	V₂₀	1	12

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表 3 敌方船只探测半径

Table 3. Detection radius of enemy vessel

目标编号	$ {R_{{{{\mathrm{s}},T}}}} $	目标编号	$ {R_{{{{\mathrm{s}},T}}}} $	目标编号	$ {R_{{{{\mathrm{s}},T}}}} $
T₁	12	T₁₁	12	T₂₁	8
T₂	10	T₁₂	8	T₂₂	8
T₃	7	T₁₃	9	T₂₃	8
T₄	9	T₁₄	10	T₂₄	10
T₅	8	T₁₅	12	T₂₅	8
T₆	9	T₁₆	12	T₂₆	12
T₇	7	T₁₇	6	T₂₇	11
T₈	10	T₁₈	11	T₂₈	12
T₉	7	T₁₉	9	T₂₉	10
T₁₀	11	T₂₀	11	T₃₀	8

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表 4 模型参数设置

Table 4. Model parameters setting

参数	数值	参数	数值
$ {N_{{V}}} $	20	${N_{{T}}}$	30
$ {w_1} $	0.5	$ {w_2} $	0.5
$ {w_3} $	0.25	$q$	20
$\Delta {t_{1,\min }}$	10	$\Delta {t_{1,\max }}$	20
$\Delta {t_{6,\min }}$	10	$\Delta {t_{6,\max }}$	20

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表 5 算法参数设置

Table 5. Algorithm parameters setting

参数	数值	参数	数值
$N$	1000	$ {K_{\rm{G}}} $	200
$ {\omega _0} $	0.9	$ {\omega _{\mathrm{e}}} $	0.4
$ {c_{1,0}} $	0.35	$ {c_{1,{\mathrm{e}}}} $	0.95
$ {c_{2,0}} $	0.35	$ {c_{2,{\mathrm{e}}}} $	0.95

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表 6 分配结果与顺序

Table 6. Assignment results and order

无人机编号	分配结果与顺序	无人机编号	分配结果与顺序
V₁	T_25,c→T_19,c→T_14,c→T_6,c→T_30,a	V₁₁	T_15,a→T_26,a
V₂	T_20,a→T_25,a	V₁₂	T_17,c→T_12,a
V₃	T_30,c→T_15,c→T_4,c→T_24,c→T_22,c→T_27,c→T_1,a→ T_23,a→T_9,c→T_11,a	V₁₃	T_2,c→T_13,a→T_16,c→T_23,c→T_5,c→T_7,c→ T_3,c→T_18,c→T_14,a
V₄	T_1,c→T_29,c→T_27,a→T_28,a→T_10,a	V₁₄	T_13,c→T_4,a
V₅	T_6,a	V₁₅	T_10,c→T_8,c→T_18,a
V₆	T_17,a→T_5,a	V₁₆	T_28,c→T_16,a
V₇	T_22,a→T_20,c	V₁₇	T_24,a
V₈	T_11,c→T_2,a→T_29,a→T_12,c	V₁₈	T_26,c→T_8,a
V₉	T_19,a	V₁₉	T_21,a
V₁₀	T_21,c→T_3,a→T_7,a	V₂₀	T_9,a

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表 7 迭代至200代时添加与未添加随机粒子的收敛精度

Table 7. Convergence accuracy with and without random particles in iteration to 200 generations

是否添加随机粒子	迭代至200代时的适应度值
添加随机粒子	1.667
未添加随机粒子	1.810

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表 8 迭代至200代时不同算法的收敛精度

Table 8. Convergence accuracy of different algorithms in iteration to 200 generations

算法	迭代至200代时的适应度值
$\gamma $-DPSO	1.667
DPSO	4.425
改进DPSO ^[22]	2.090
CPSO^[26]	1.989

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