考虑子系统执行能力的多无人机协同任务规划

doi:10.12305/j.issn.1001-506X.2023.01.16

Abstract

Abstract:

Task assignment is one of the key techniques to improve the operation efficiency of unmanned aerial vehicle (UAV). To solve the UAV swarm cooperative task assignment problem under subsystem capability constraints, an improved wolf pack algorithm incorporating auction mechanism is proposed. Firstly, a subsystem capability matrix is defined to facilitate the unified description of UAVs' heterogeneity and task execution capability. Secondly, the individual wolf is encoded by matrix, and a correction strategy based on auction mechanism is proposed to deal with the infeasible solution of the number of violation attacks. Thirdly, the idea of genetic algorithm is incorporated in the process of individual wolf position updating, and the adjacent row exchange operation and interval column crossover operation are carried out in the exploration stage and the round-up stage respectively to achieve rapid optimization. Finally, the third best wolf is introduced into the wolf pack renewal process, thus enhancing the diversity of the population. Simulation experiments show that the proposed method can effectively solve the multi-UAV collaborative task planning problem under subsystem execution capability constraints. Compared with other improved evolutionary algorithms, the proposed method has better optimality and convergence speed.

Key words: cooperative task assignment, wolf pack algorithm, matrix encoding, auction mechanism, subsystem capability matrix

CLC Number:

V279

Hongyun ZHANG, Lei WANG, Xu ZHANG, Yu DING, Chen LYU, Xinwei WANG. Multi-UAV cooperative mission planning considering subsystem execution capability[J]. Systems Engineering and Electronics, 2023, 45(1): 127-138.

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无人机	任务序列
U₁	无
U₂	(T₂, 攻击)
U₃	(T₃, 识别)→(T₄, 识别)→(T₁, 攻击)→(T₁, 评估)
U₄	无
U₅	(T₃, 攻击)→(T₃, 评估)
U₆	(T₁, 识别)→(T₄, 攻击)→(T₄, 评估)
U₇	(T₅, 识别)→(T₅, 攻击)→(T₅, 评估)
U₈	(T₂, 识别)→(T₂, 评估)

无人机	任务序列
U₁	无
U₂	(T₅, 识别)→(T₉, 识别)→(T₉, 攻击)
U₃	(T₁, 攻击)→(T₁, 评估)
U₄	(T₁, 识别)→(T₁₀, 识别)→(T₁₀, 攻击)
U₅	无
U₆	(T₄, 识别)→(T₄, 攻击)
U₇	(T₄, 评估)
U₈	(T₇, 识别)→(T₇, 攻击)
U₉	(T₈, 识别)→(T₈, 攻击)→(T₈, 评估)→(T₉, 评估)
U₁₀	无
U₁₁	(T₅, 攻击)→(T₅, 评估)
U₁₂	(T₃, 识别)→(T₃, 攻击)→(T₃, 评估)→(T₁₀, 评估)
U₁₃	(T₆, 识别)→(T₆, 攻击)→(T₆, 评估)
U₁₄	无
U₁₅	(T₂, 识别)→(T₂, 攻击)→(T₂, 评估)

算法	f_best	f_worst	f_avg	std	T_avg
AMIWPA	32 119.95	34 269.15	33 155.61	673.87	347.32
AMIPSOA	53 958.71	55 466.04	54 797.71	989.30	631.59
AMISAA	42 302.04	46 615.64	44 203.33	1 016.28	573.35

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Multi-UAV cooperative mission planning considering subsystem execution capability

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无人机	能力值
无人机	识别	攻击	评估
U₁	60	30	70
U₂	50	60	80
U₃	90	20	50
U₄	40	60	70
U₅	30	70	50

目标	最低能力值需求
目标	识别	攻击	评估
T₁	60	40	30
T₂	50	20	50
T₃	20	60	30

无人机	初始位置	能力值/%
无人机	初始位置	识别	攻击	评估
U₁	(0, 0)	90	10	60
U₂	(0, 0)	20	60	80
U₃	(0, 0)	50	60	70
U₄	(0, 0)	80	80	20
U₅	(0, 0)	10	90	90
U₆	(0, 0)	70	30	60
U₇	(0, 0)	20	60	80
U₈	(0, 0)	70	20	80

目标	初始位置	最低需求能力值/%
目标	初始位置	识别	攻击	评估
T₁	(99, 57)	70	40	50
T₂	(13, 26)	30	50	40
T₃	(68, 52)	30	70	30
T₄	(97, 42)	50	20	50
T₅	(57, 24)	10	60	60

目标	初始位置	最低需求能力值/%
目标	初始位置	识别	攻击	评估
T₁	(18, 18)	60	50	40
T₂	(35, 119)	60	50	30
T₃	(166, 84)	50	30	50
T₄	(94, 81)	60	30	60
T₅	(239, 45)	80	30	20
T₆	(283, 51)	50	20	20
T₇	(142, 12)	60	70	30
T₈	(293, 103)	50	50	30
T₉	(293, 134)	40	80	40
T₁₀	(284, 245)	30	30	60