基于EMSDBO算法的无人机三维航迹规划

doi:10.12305/j.issn.1001-506X.2024.05.28

Abstract

Abstract:

In view of the unmanned aerial vehicle (UAV) three-dimensional path planning problem, an enhanced multi-strategy dung beetle algorithm of UAV path planning is proposed. Firstly, constraints on the flight proximity rate and response time are introduced and added to the threat cost, considering the influence of UAV turning performance, a three-dimensional task space model and trajectory cost function are established. Secondly, the dung beetle algorithm is enhanced by introducing offset estimation strategy, variable spiral search strategy, quasi-inverse learning strategy, and dimensional mutation strategy to improve the algorithm's global optimization capability and convergence speed. Finally, simulation results of the improved algorithm for three-dimensional trajectory planning in an environment are presented. Results demonstrate that by considering both the maneuverability and turning performance of the UAV, the planned path can safely and efficiently avoid hazards. Compared to other algorithms, the enhanced multi-strategy dung beetle algorithm shows better optimization capability and generates higher-quality trajectories.

Key words: unmanned aerial vehicle (UAV), path planning, flight proximity rate, dung beetle optimizer algorithm

CLC Number:

V249

Dong SUI, Zhenyu YANG, Songbin DING, Tingting ZHOU. Three-dimensional path planning of UAV based on EMSDBO algorithm[J]. Systems Engineering and Electronics, 2024, 46(5): 1756-1766.

Figures/Tables 15

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飞行模式	判断条件	速度
水平飞行	ϕ_t≤ϕ₀且ψ_t≤ψ₀	v₁
偏转转弯	ϕ_t>ϕ₀且ψ_t≤ψ₀	v₂
爬升下降	ϕ_t≤ϕ₀且ψ_t>ψ₀	v₃

函数类别	公式表达式	维度	搜索范围	最优值
单峰函数	$f_1(x)=\sum\limits_{i=1}^n x_i^2$	30	[-100, 100]	0
	$f_2(x)=\sum\limits_{i=1}^n\left(\sum\limits_{j=1}^i x_j\right)^2$	30	[-100, 100]	0
	$f_3(x)=\sum\limits_{i=1}^n\left(\left\|x_i+0.5\right\|\right)^2$	30	[-100, 100]	0
	$f_4(x)=\sum\limits_{i=1}^n i x_i^4+\operatorname{random}[0, 1)$	30	[-1.28, 1.28]	0
多峰函数	$f_5(x)=\sum\limits_{i=1}^n\left[x_i^2-10 \cos 2 {\rm{ \mathsf{ π} }} x_i+10\right]$	30	[-5.12, 5.12]	0
多峰函数	$f_6(x)=\frac{1}{4000} x_i^2-\prod\limits_{i=1}^n \cos \left(\frac{x_i}{\sqrt{i}}\right)+1$	30	[-600, 600]	0

测试函数	统计值	EMSDBO	DBO	GWO	WOA	NGO
f₁	最优值	0.00 E+00	1.12 E-142	5.76 E-30	2.75 E-82	1.43 E-89
	平均值	0.00 E+00	2.83 E-106	1.14 E-27	6.91 E-73	4.37 E-87
	标准差	0.00 E+00	1.92 E-105	2.22 E-27	2.72 E-72	1.86 E-86
f₂	最优值	0.00 E+00	2.02 E-96	9.17 E-09	7.56 E+04	4.98 E-28
	平均值	0.00 E+00	2.56 E-40	5.87 E-06	1.49 E+04	1.84 E-22
	标准差	0.00 E+00	1.29 E-39	1.40 E-05	4.79 E+04	8.18 E-22
f₃	最优值	6.67 E-12	4.80 E-06	2.49 E-01	9.34 E-02	7.17 E-07
	平均值	1.17 E-10	4.86 E-03	7.76 E-01	3.85 E-01	7.73 E-04
	标准差	1.30 E-10	2.38 E-02	3.50 E-01	2.41 E-01	3.40 E-03
f₄	最优值	3.45 E-06	9.52 E-05	3.38 E-04	5.58 E-07	1.86 E-04
	平均值	7.22 E-05	1.39 E-03	2.00 E-03	2.97 E-03	6.29 E-04
	标准差	5.66 E-05	1.04 E-03	1.09 E-03	2.94 E-03	3.16 E-04
f₅	最优值	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00
	平均值	0.00 E+00	7.96 E-01	2.87 E+00	1.14 E-15	0.00 E+00
	标准差	0.00 E+00	4.01 E+00	3.71 E+00	8.04 E-15	0.00 E+00
f₆	最优值	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00
	平均值	0.00 E+00	4.26 E-04	5.03 E-03	1.76 E-02	0.00 E+00
	标准差	0.00 E+00	3.01 E-03	1.04 E-02	6.83 E-03	0.00 E+00

函数	统计值	DBO	ODBO	SDBO	QDBO	VDBO	EMSDBO
f₁	最优值	1.12 E-142	2.24 E-148	5.82 E-155	0.00 E+00	2.11 E-213	0.00 E+00
	平均值	2.83 E-106	3.84 E-121	1.94 E-114	0.00 E+00	4.93 E-107	0.00 E+00
	标准差	1.92 E-105	1.22 E-110	1.06 E-113	0.00 E+00	3.49 E-106	0.00 E+00
f₂	最优值	2.02 E-96	1.20 E-72	2.21 E-75	0.00 E+00	1.90 E-101	0.00 E+00
	平均值	2.56 E-40	1.56 E-42	5.84 E-54	0.00 E+00	5.91 E-38	0.00 E+00
	标准差	1.29 E-39	8.93 E-39	3.81 E-53	0.00 E+00	3.95 E-40	0.00 E+00
f₃	最优值	4.80 E-06	2.82 E-07	4.22 E-06	5.76 E-07	8.94 E-10	6.67 E-12
	平均值	4.86 E-03	1.51 E-06	6.46 E-04	3.56 E-03	8.54 E-08	1.17 E-10
	标准差	2.38 E-02	8.10 E-07	5.45 E-04	1.97 E-02	2.13 E-07	1.30 E-10
f₄	最优值	9.52 E-05	2.28 E-05	2.10 E-06	1.57 E-04	3.54 E-05	3.45 E-06
	平均值	1.39 E-03	1.14 E-03	1.14 E-04	1.04 E-03	7.96 E-04	7.22 E-05
	标准差	1.04 E-03	8.27 E-04	9.01 E-05	8.05 E-04	7.11 E-04	5.66 E-05
f₅	最优值	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00
	平均值	7.96 E-01	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00
	标准差	4.01 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00
f₆	最优值	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00
	平均值	4.26 E-04	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00
	标准差	3.01 E-03	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00	0.00 E+00

参数名称	设置值	参数名称	设置值
m	15	t_min/s	3
ϕ_max/(°)	45	v₁/(m/s)	15
ψ_max/(°)	45	v₂/(m/s)	10
l_min/m	30	v₃/(m/s)	6
α₁	0.6	t_min/s	2
α₂	0.4	T	10
ϕ₀/(°)	30	w₁	0.4
ψ₀/(°)	30	w₂	0.2
h_max/m	400	w₃	0.2
h_min/m	100	w₄	0.2

Three-dimensional path planning of UAV based on EMSDBO algorithm

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实验场景	最小响应时间/s	平均航程距离/m	威胁成本
本文场景	5.44	1 018.5	45.9
场景1	5.68	978.02	61.0
场景2	11.48	1 068.2	28.7

算法名称	F₁	F₂	F₃	F₄	F			寻路成功率/%
算法名称	F₁	F₂	F₃	F₄	F_best	F_mean	F_std	寻路成功率/%
EMSDBO	1 125.8	142.1	114.7	60.0	476.6	517.9	18.8	100
SPSO	1 278.5	227.1	158.7	70.3	506.8	602.6	64.9	86
CASSA	1 276.7	251.1	159.3	83.5	515.9	609.4	40.4	100
DBO	1 263.9	224.0	180.5	37.4	553.0	593.9	26.2	80

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