基于多精度规划窗口的无人机航迹规划方法研究

doi:10.12305/j.issn.1001-506X.2024.05.29

摘要/Abstract

摘要：

航迹规划是无人机(unmanned aerial vehicle, UAV)任务规划系统的核心部分之一, 其主要任务是结合战场环境等约束条件, 寻找一条安全系数高、满足任务需求且飞行代价小的UAV最优飞行航迹。基于现有蚁群优化(ant colony optimization, ACO)算法, 在其并行能力基础上提出一种多精度规划窗口方法。该方法在初始航迹基础上, 进一步针对局部飞行环境特点, 自动配置局部规划窗口、规划精度和规划参数, 并行地开展多精度窗口航迹调整, 可在较短时间内优化出一条适应战场环境的飞行航迹。仿真分析表明, 不同战场环境下所需的算法参数配置、规划精度各有不同, 通过多精度规划窗口的优化与调整, 最终飞行航迹可适应不同战场环境, 且具备较好的规划效率与精度。

关键词: 无人机, 航迹规划, 蚁群优化算法, 多精度优化, 优化算法

Abstract:

Path planning plays a significant role on the unmanned aerial vehicle (UAV) mission planning. It is aiming at finding a safest UAV trajectory of optimal flying cost, considering the battlefield environment and other mission requirements. Based on the parallel ability of ant colony optimization (ACO) algorithm, a multi-precision planning window method is proposed. Based on the initial trajectory, it can automatically set multiple local planning windows with specifical planning precisions and optimization parameters, and then parallel path modification in a short time. Simulation analysis shows that the algorithm parameters configuration and planning accuracy are different in different battlefield environments. Through the optimization and adjustment of multi-precision planning window, the final flight path can adapt to different battlefield environment, and has better planning efficiency and accuracy.

Key words: unmanned aerial vehicle (UAV), path planning, ant colony optimization (ACO) algorithm, multi-precision optimization, optimization algorithm

中图分类号:

TP391

余婧, 吴晓军, 蒋安林, 雍恩米. 基于多精度规划窗口的无人机航迹规划方法研究[J]. 系统工程与电子技术, 2024, 46(5): 1767-1776.

Jing YU, Xiaojun WU, Anlin JIANG, Enmi YONG. Research on UAV path planning method based on the multi-precision planning windows[J]. Systems Engineering and Electronics, 2024, 46(5): 1767-1776.

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坐标参数	雷达			火力		地形		气象		禁飞区
坐标参数	1	2	3	4	5	6	7	8	9	10
X轴坐标/km	520	460	200	420	340	120	80	240	320	420
Y轴坐标/km	260	500	540	100	340	80	360	400	200	340
威胁半径/km	120	120	120	100	100	10	10	10	10	10

参数	数值
种群数量	100
α	1
β	7
ρ	0.3
Q	10
最大迭代次数	400

参数	算例编码
参数	A-1	A-2	A-3
w₁	0.8	0.5	0.2
w₂	0.2	0.5	0.8
航程/km	539.41	539.41	539.41
归一化航程	0.899	0.899	0.899
威胁代价	0	0	0
归一化威胁	0	0	0
总代价	0.719 2	0.449 5	0.179 8

参数	算例编码
参数	B-1	B-2	B-3
w₁	0.8	0.5	0.2
w₂	0.2	0.5	0.8
航程/km	366.27	382.842 7	411.13
归一化航程	0.610 5	0.638 1	0.685 2
威胁代价	4.030 9	3.679 9	3.465 9
归一化威胁	0.806 2	0.736 0	0.693 2
总代价	0.649 6	0.687 0	0.691 6

参数	算例编码
参数	A-4	A-5(即A-2)	A-6
精度/km	40×40	20×20	10×10
规划时间/s	21.70	27.46	107.71
航程/km	555.98	539.41	563.553 4
归一化航程	0.926 6	0.899 0	0.939 3
威胁代价	0.428 0	0	0.367 9
归一化威胁	0.042 8	0	0.036 8
总代价	0.484 7	0.449 5	0.488 0