基于自适应黏菌算法优化的无人机三维路径规划

doi:10.16183/j.cnki.jsjtu.2022.191

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (10): 1282-1291.doi: 10.16183/j.cnki.jsjtu.2022.191

所属专题：《上海交通大学学报》2023年“交通运输工程”专题

基于自适应黏菌算法优化的无人机三维路径规划

黄鹤^a^,^b, 高永博^a, 茹锋^a^,^b, 杨澜^c(), 王会峰^b

a.长安大学电子与控制工程学院, 西安 710064
b.长安大学西安市智慧高速公路信息融合与控制重点实验室, 西安 710064
c.长安大学信息工程学院,西安 710064

收稿日期:2022-06-01 修回日期:2022-09-01 接受日期:2022-10-17 出版日期:2023-10-28 发布日期:2023-10-31
通讯作者: 杨澜 E-mail:lanyang@chd.edu.cn.
作者简介:黄鹤(1979-),教授,博士生导师,研究方向为无人系统测控、信息融合等.
基金资助:
国家重点研发计划项目(2021YFB2501200);国家自然基金面上项目(52172324);国家自然基金面上项目(52172379);陕西省重点研发计划项目(2021SF-483);陕西省自然科学基础研究计划项目(2021JM-184);西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金项目(300102321502);中央高校基本科研业务费资助项目(300102240203)

3D Path Planning of UAV Based on Adaptive Slime Mould Algorithm Optimization

HUANG He^a^,^b, GAO Yongbo^a, RU Feng^a^,^b, YANG Lan^c(), WANG Huifeng^b

a. School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
b. Xi’an Key Laboratory of Intelligent Expressway Information Fusion and Control, Chang’an University, Xi’an 710064
c. School of Information Engineering, Chang’an University, Xi’an 710064, China

Received:2022-06-01 Revised:2022-09-01 Accepted:2022-10-17 Online:2023-10-28 Published:2023-10-31
Contact: YANG Lan E-mail:lanyang@chd.edu.cn.

摘要/Abstract

摘要：

针对无人机在三维路径规划时存在搜素范围和寻优性能不足等问题,以及现有黏菌算法(SMA)寻优精度不足, 易陷入局部最优的缺陷,提出了一种基于自适应黏菌算法(GSMA)优化的无人机三维路径规划方法.首先,根据无人机经过的实际环境,建立三维地形、威胁源和无人机自身约束条件;其次,针对搜素范围不足的问题,设计改进的Logistic混沌映射增加种群的多样性并扩大搜索范围,提升了SMA的全局搜索能力;然后,设计一种非线性自适应惯性权重因子,将线性收敛方式改进为非线性收敛,利用权重值更新黏菌位置,提高了收敛速度;最后,在算法后期中设计自适应柯西变异,增大了黏菌的搜索空间,寻优精度也得到了提高.实验结果表明,GSMA相比于灰狼优化(GWO)算法、SMA和海鸥算法(SOA)3种算法,路径更短且更平滑,收敛速度更快,寻优精度更高,同时能耗更低,进一步提升了无人机的路径规划能力.

关键词: 无人机, 路径规划, 黏菌算法, 混沌映射, 自适应柯西变异, 自适应权重

Abstract:

Aimed at the problems of insufficient search range and optimization performance in 3D path planning of unmanned aerial vehicles (UAVs), and the lack of optimization accuracy of the existing slime mould algorithm (SMA), which is easy to fall into local optimization, a 3D path planning method for UAV based on adaptive slime mould algorithm optimization is proposed. First, according to the actual environment that the UAV passes through, the 3D terrain, the threat source and the constraints of the AUV were established. Next, for the problem of insufficient search range, an improved Logistic chaotic map is designed to increase the diversity of the population and expand the search range, which improves the global search ability of SMA. Then, a nonlinear adaptive inertia weight factor is designed to change the linear convergence method into nonlinear convergence, and the weight value is used to update the position of the slime mould, which improves the convergence speed. Finally, in the later stage of the algorithm, the adaptive cauchy mutation is designed, which increases the search space of the slime mould and improves the optimization accuracy. The experimental results show that GSMA has a shorter and smoother path, a faster convergence, a higher optimization accuracy, and a lower energy consumption compared with the gray wolf optimizer (GWO) algorithm, the SMA, and the seagull algorithm (SOA), which further improves the path planning capability of the UAV.

Key words: unmanned aerial vehicle (UAV), path planning, slime mould algorithm (SMA), chaos mapping, adaptive Cauchy variation, adaptive weights

中图分类号:

U666.1

黄鹤, 高永博, 茹锋, 杨澜, 王会峰. 基于自适应黏菌算法优化的无人机三维路径规划[J]. 上海交通大学学报, 2023, 57(10): 1282-1291.

HUANG He, GAO Yongbo, RU Feng, YANG Lan, WANG Huifeng. 3D Path Planning of UAV Based on Adaptive Slime Mould Algorithm Optimization[J]. Journal of Shanghai Jiao Tong University, 2023, 57(10): 1282-1291.

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参考文献 17

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测试函数	算法	F_best	F_avg
Rosenbrock	SMA	28.63	28.98
	GWO	28.17	28.34
	SOA	29.67	29.93
	GSMA	27.89	28.15
Schwefel	SMA	-1.22×10⁴	-1.31×10⁴
	GWO	-5.66×10³	-5.61×10³
	SOA	-5.66×10³	-5.66×10³
	GSMA	-1.26×10⁴	-1.32×10⁴
Foxholes	SMA	0.068	0.153 8
	GWO	2.699	2.981
	SOA	0.928	0.563
	GSMA	0.076	0.095
Kowalik	SMA	1.28×10^-3	1.71×10^-3
	GWO	2.04×10^-3	2.43×10^-3
	SOA	2.04×10^-3	2.12×10^-3
	GSMA	6.34×10^-4	6.97×10^-4
Hartman 6	SMA	-3.11	-3.19
	GWO	-3.17	-3.75
	SOA	-2.65	-2.82
	GSMA	-3.16	-3.27
Shekel 10	SMA	-3.75	-3.63
	GWO	-10.50	-10.46
	SOA	-4.87	-4.87
	GSMA	-10.56	-10.53

参数	数值
v/(km·h^-1)	30~40
K_Z	100
K'_Z	100
K_F	100
k_d	0.6
k_g/(km·s^-2)	5
Q_h	10
Q_v	10
a_max	60
β_max	60
K_thr	R_max,_p/3
ω₁	0.23
ω₂	0.45
ω₃	0.2
ω₄	0.12
d	15

算法	航程/km	最优适应度	转弯和爬升角代价	寻优成功率/%
SMA	166.23	30.86	100	76
GWO	159.38	34.09	70	79
SOA	162.17	36.72	120	82
GSMA	133.54	29.43	10	95

算法	航程/km	最优适应度	转弯和爬升角代价	寻优成功率/%
SMA	165.43	27.06	100	85
GWO	163.85	34.37	80	78
SOA	171.65	37.78	90	75
GSMA	139.86	28.89	20	93

基于自适应黏菌算法优化的无人机三维路径规划

3D Path Planning of UAV Based on Adaptive Slime Mould Algorithm Optimization

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