基于MOEA/D-ARMS的无人机在线航迹规划

doi:10.12305/j.issn.1001-506X.2022.11.26

摘要/Abstract

摘要：

无人机(unmanned aerial vehicle, UAV)在线航迹规划是UAV协同控制关键技术之一, 在线航迹规划问题本质上是一种动态多目标优化问题。为了求解该问题, 提出了一种基于自适应应答机制选择的动态多目标进化算法(multi-objective evolutionary algorithon based on decomposition-adaptive reaction mechanism selection, MOEA/D-ARMS)。多种应答机制构成应答机制池, 以应答机制最近一次的整体表现赋予应答机制一定的奖励, 并采用基于概率的方法从应答机制池中选择应答机制。MOEA/D-ARMS分别在静态环境情况、突发威胁情况、突变威胁情况和偏好改变情况下进行仿真实验。仿真结果表明, MOEA/D-ARMS可有效求解UAV在线航迹规划问题。

关键词: 无人机, 航迹规划, 动态多目标进化算法, 自适应选择

Abstract:

Unmanned aerial vehicle (UAV) online trajectory planning is one of the key technologies of UAV collaborative control. The problem of online trajectory planning is essentially a dynamic multi-objective optimization problem. To solve this problem, a dynamic multi-objective evolutionry algorithm based on decomposition-adaptive reaction mechanism selection (MOEA/D-ARMS) is proposed. A variety of response mechanisms constitute a response mechanism pool. The response mechanism is given a certain reward based on the most recent overall performance of the response mechanism, and the response mechanism is selected from the response mechanism pool using a probability-based method. MOEA/D-ARMS is applied to four simulation instances, such as static environment instance, sudden threat instance, mutation threat instance and preference change instance. The simulation results demonstrate MOEA/D-ARMS is effective in solving the UAV online trajectory planning problem.

Key words: unmanned aerial vehicle (UAV), trajectory planning, dynamic multi-objective evolutionary algorithm (DMEA), adaptive selection

中图分类号:

TP18

汪瀚洋, 陈亮, 徐海, 白景波. 基于MOEA/D-ARMS的无人机在线航迹规划[J]. 系统工程与电子技术, 2022, 44(11): 3505-3514.

Hanyang WANG, Liang CHEN, Hai XU, Jingbo BAI. UAV online trajectory planning based on MOEA/D-ARMS[J]. Systems Engineering and Electronics, 2022, 44(11): 3505-3514.

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偏好	规划方法	飞行时间/s	生存概率	航向角变化平均值/(°)
(ω₁, ω₂)^T=(0.3, 0.7)^T	MOEA/D-ARMS	180	0.972	7.586
(ω₁, ω₂)^T=(0.3, 0.7)^T	MOEA/D-KF	186	0.969	13.029
(ω₁, ω₂)^T=(0.7, 0.3)^T	MOEA/D-ARMS	169	0.934	8.000
(ω₁, ω₂)^T=(0.7, 0.3)^T	MOEA/D-KF	176	0.920	11.741

偏好	规划方法	飞行时间/s	生存概率	航向角变化平均值/(°)
(ω₁, ω₂)^T=(0.3, 0.7)^T	MOEA/D-ARMS	330	0.973	7.82
(ω₁, ω₂)^T=(0.3, 0.7)^T	MOEA/D-KF	344	0.950	12.68
(ω₁, ω₂)^T=(0.7, 0.3)^T	MOEA/D-ARMS	315	0.884	7.45
(ω₁, ω₂)^T=(0.7, 0.3)^T	MOEA/D-KF	336	0.855	13.22

偏好	规划方法	飞行时间/s	生存概率	航向角变化平均值/(°)
(ω₁, ω₂)^T=(0.3, 0.7)^T	MOEA/D-ARMS	460	0.931	7.49
(ω₁, ω₂)^T=(0.3, 0.7)^T	MOEA/D-KF	475	0.912	12.26
(ω₁, ω₂)^T=(0.7, 0.3)^T	MOEA/D-ARMS	430	0.837	7.03
(ω₁, ω₂)^T=(0.7, 0.3)^T	MOEA/D-KF	443	0.809	11.93

偏好	规划方法	飞行时间/s	生存概率	航向角变化平均值/(°)
(ω₁, ω₂)^T=(0.3, 0.7)^T	MOEA/D-ARMS	442	0.896	6.42
(ω₁, ω₂)^T=(0.7, 0.3)^T	MOEA/D-KF	450	0.871	12.16

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