基于三阶段优化的无人机-无人车空地协同路径规划方法

doi:10.7527/S1000-6893.2025.32649

Abstract

Abstract:

Unmanned Aerial Vehicle-Unmanned Ground Vehicle （UAV-UGV） air-ground collaborative systems are widely applied and garner significant attention across various domains. UAVs offer advantages in high speed and maneuverability but suffer from limited endurance， while UGVs， though possessing relatively constrained mobility， provide substantial ground payload capacity and can serve as mobile relay stations for UAVs. Path planning for such collaborative systems presents a complex and challenging problem， aiming to generate feasible paths for both UAVs and UGVs to cooperatively accomplish missions. This paper comprehensively considers practical constraints frequently encountered in real-world scenarios， including speed， energy consumption， power limitations， and obstacles， overcoming the limitation of traditional air-ground systems which often focus on a single perspective （either airborne or ground-based）. We establish a task-oriented UAV-UGV air-ground collaborative system model and propose a three-stage-optimization collaborative path planning method. Specifically， we develop a Meta-learning Local-search Genetic Algorithm （MLGA）， combining meta-learning strategies with local search， to solve the first-stage global path generation problem within the collaborative model. For the second-stage UGV obstacle avoidance， we introduce a temporal particle A* algorithm， integrating an improved particle filter algorithm with an optimized temporal A* algorithm. Finally， addressing the third stage， we formulate a time-constrained UAV charging scheduling solution based on the distinct speed， energy， and power characteristics of UAVs and UGVs. The effectiveness of the proposed three-stage planning method is rigorously validated through simulations using virtual scenario data and real-world experiments.

Key words: Unmanned Aerial Vehicle (UAV), Unmanned Ground Vehicle (UGV), air-ground collaborative systems, three-stage optimization, path planning method

CLC Number:

V249.12

Shufang XU, Wenxuan FEI, Heng LI, Hongmin GAO. UAV-UGV collaborative air-ground path planning method based on three-stage optimization[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(7): 332649.

Figures/Tables 20

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Parameter setting of cooperative system

系统	参数	数值
无人机	$v a 1$ /（km·h^-1）	40
	$v a 2$ /（m·s^-1）	4
	$T a m a x$ /h	0.5
	$T 1$ /h	0.4
无人车	$v g$ /（km·h^-1）	15（空车）
	$v g$ /（km·h^-1）	7.5（载无人机）
	$T 2$ /h	0.8

Table 1

Table 2

Setting of parameters of various algorithms solving TSP

参数	意义	数值
$G m a x$	迭代次数	200
$S p o p$	种群规模	100
$p c$	交叉概率	0.9
$p m$	变异概率	0.05
$p l$	局部搜索个体比重	0.3
$N m a x$	蚁群迭代次数	200
$N a n t s$	蚂蚁数量	30
$α$	信息素因子	1
$β$	启发函数因子	2
$ρ$	信息素挥发因子	0.5
$Q$	信息素常量	100
$l$	学习率	0.5
$N i t e r$	训练轮次	10 000

Table 2

Table 3

Fig.7

Table 4

Setting of parameters of four PF algorithms

参数	意义	数值
$T 0$ /℃	初始温度	$1$
$c$	扰动系数	0.6
$α$	衰减系数	0.95
$G P F$	迭代次数	100
$G S A$	迭代次数	100

Table 4

Table 5

Comparison of mean distance errors under different particle counts in 2D space

算法	平均距离误差
算法	$N = 100$	$N = 200$	$N = 500$	$N = 1 000$
PF	1.693 1	1.192 1	0.761 9	0.541 8
APF	1.147 3	0.864 1	0.662 4	0.509 3
SPF	0.940 1	0.721 0	0.570 9	0.518 9
ASPF	0.786 7	0.613 7	0.555 7	0.502 4

Table 5

Fig.8

Table 6

Setting of parameters in simulation experiment

参数	意义	数值
$d$ /m	安全距离	10
$Δ t$ /s	时间步长	0.4
$T p$ /s	预测时间窗	3
$θ t h$ /（°）	角度阈值	$5$

Table 6

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

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算法	平均路径长度/km
算法	N _T=20	N _T=30	N _T=40
MLGA	143.59	190.86	206.03
遗传算法	153.45	202.07	290.34
蚁群算法	143.64	193.31	216.22
贪婪算法	165.94	211.86	222.16
Q-Learning	146.96	198.67	211.52

UAV-UGV collaborative air-ground path planning method based on three-stage optimization

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