多障碍场景下基于多策略进化机制的无人机三维路径规划

朱润泽; 赵静; 陆宁云; 马亚杰; 宋来收

doi:10.16383/j.aas.c250319

多障碍场景下基于多策略进化机制的无人机三维路径规划

doi: 10.16383/j.aas.c250319 cstr: 32138.14.j.aas.c250319

1.
南京邮电大学自动化学院南京 210023
2.
南京航空航天大学自动化学院南京 210016

基金项目: 航空航天结构力学及控制国家重点实验室开放课题(MCMS-E-0123G04), 直升机动力学全国重点实验室开放课题(2024-ZSJ-LB-02-05), 江苏省研究生科研与实践创新计划项目(KYCX24_1214)资助

详细信息

作者简介:
朱润泽：南京邮电大学硕士研究生. 2023年获得南通大学学士学位. 主要研究方向为机器人路径规划. E-mail: 19850968797@163.com

赵静：南京邮电大学自动化学院副教授. 2014年获得南京航空航天大学博士学位. 主要研究方向为智能无人系统, 旋翼无人机的容错控制. 本文通信作者. E-mail: zhaojing@njupt.edu.cn

陆宁云：南京航空航天大学自动化学院教授. 2004年获得东北大学博士学位. 主要研究方向为故障诊断和寿命预测. E-mail: luningyun@nuaa.edu.cn

马亚杰：南京航空航天大学自动化学院教授. 2015年获得南京航空航天大学博士学位. 主要研究方向为飞行器智能故障诊断与容错控制, 多智能体系统任务规划. E-mail: yajiema@nuaa.edu.cn

宋来收：南京航空航天大学自动化学院副研究员. 2014年获得南京航空航天大学博士学位. 主要研究方向为旋翼机动力学及结构设计. E-mail: lss05012@nuaa.edu.cn

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出版历程
- 收稿日期: 2025-07-13
- 录用日期: 2025-11-26
- 网络出版日期: 2026-01-27
- 刊出日期: 2026-02-20

Multi-Strategy Evolutionary Mechanism for UAV 3D Path Planning in Multi-Obstacle Environments

1.
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023
2.
College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016

Funds: Supported by State Key Laboratory of Aerospace Structural Mechanics and Control (MCMS-E-0123G04), National Key Laboratory Foundation of Helicopter Aeromechanics (2024-ZSJ-LB-02-05), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_1214)

More Information

Author Bio:
ZHU Run-Ze　Master student at Nanjing University of Posts and Telecommunications. He received his bachelor degree from Nantong University in 2023. His main research interest is path planning of robot

ZHAO Jing　Associate professor at the College of Automation, Nanjing University of Posts and Telecommunications. She received her Ph.D. degree from Nanjing University of Aeronautics and Astronautics in 2014. Her research interests include intelligent unmanned systems and fault-tolerant control of rotorcraft drones. Corresponding author of this paper

LU Ning-Yun　Professor at the College of Automation, Nanjing University of Aeronautics and Astronautics. She received her Ph.D. degree from Northeastern University in 2004. Her research interests include fault diagnosis and life prediction

MA Ya-Jie　Professor at the College of Automation, Nanjing University of Aeronautics and Astronautics. He received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics in 2015. His research interests include intelligent fault diagnosis and fault-tolerant control of aircraft, and task planning of multi-agent systems

SONG Lai-Shou　Associate researcher at the College of Automation, Nanjing University of Aeronautics and Astronautics. He received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics in 2014. His research interests include rotorcraft dynamics and structural design

摘要

摘要: 针对无人机在三维多障碍物场景下路径规划存在的收敛精度低、稳定性不足等问题, 提出一种多策略进化粒子群算法(MSEPSO). 在初始化阶段, 针对粒子群算法对粒子初始位置敏感的问题, 采用拉丁超立方采样优化粒子初始分布, 提高种群多样性; 在进化阶段, 设计“平衡−记忆−增强”进化框架, 即利用非线性迭代策略来平衡全局开发和局部搜索, 采用个体历史记忆启发机制增强算法的全局开发能力, 并引入进化粒子, 增强种群对于群体极值附近空间的探索能力, 降低算法陷入局部最优的概率. 在CEC2020测试函数集上与山地/城市场景下的对比实验结果表明, MSEPSO展现出稳定的寻优性能, 可以规划长度更短、平滑度更高的安全路径.
- 无人机 /
- 三维路径规划 /
- 粒子群算法 /
- 多策略进化
Abstract: Aiming at the problems such as low convergence accuracy and insufficient stability in path planning for unmanned aerial vehicles (UAVs) in 3D multi-obstacle scenarios, a multi-strategy evolutionary particle swarm optimization (MSEPSO) algorithm is proposed. In the initialization stage, aiming at the problem that particle swarm optimization is sensitive to the initial position of particles, the initial distribution of particles is optimized through Latin hypercube sampling to improve the population diversity; During the evolutionary stage, a “balance-memory-enhancement” evolutionary framework is designed, which utilizes a nonlinear iterative strategy to balance global development and local search. The personal history memory mechanism is adopted to enhance the global exploitation ability of the algorithm. Evolutionary particles are introduced to enhance the exploration ability of the population in the vicinity of the group＇s extreme values, reducing the probability of the algorithm getting stuck in local optima. Experimental results from comparisons on the CEC2020 test function set and in mountain/urban scenarios demonstrate that MSEPSO exhibits stable optimization performance, enabling the planning of safer paths with shorter lengths and higher smoothness.
- UAVs /
- 3D path planning /
- particle swarm algorithm /
- multi-strategy evolution

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图 1 环境建模

Fig. 1 Environment model

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图 2 基于不同采样策略生成1000个随机数

Fig. 2 Generate 1000 random numbers based on different sampling strategies

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图 3 参数非线性迭代策略

Fig. 3 Nonlinear iterative strategy of parameters

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图 4 基于PHMM启发粒子探索

Fig. 4 Particle exploration inspired by PHMM

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图 5 基于进化粒子逼近最优解

Fig. 5 Approach to optimal solution based on evolutionary particles

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图 6 基于CEC2020基准函数的适应度值曲线

Fig. 6 Fitness curve based on CEC2020 benchmark function

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图 7 不同场景下的路径对比图

Fig. 7 Path comparison diagrams in various scenarios

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图 8 不同场景下的适应度曲线对比图

Fig. 8 Fitness curve comparison in various scenarios

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表 1 基于10D CEC2020基准函数的数值实验

Table 1 Numerical experiments based on 10D CEC2020 benchmark functions

		PSO	LWPSO	PSO-SA	ACVDEPSO	WOA	SHO	HHO	DBO	MSEPSO
F1	B	1.893E+07	1.000E+03	7.981E+06	5.071E+03	9.614E+05	1.105E+06	5.737E+04	4.140E+09	1.001E+02
	W	5.174E+09	4.415E+09	1.142E+10	6.100E+09	1.230E+10	4.426E+09	2.765E+06	3.774E+10	1.274E+04
	M	9.222E+08	3.598E+08	1.914E+09	7.338E+07	6.539E+08	3.973E+08	5.985E+05	1.913E+10	3.744E+03
	V	7.913E+17	4.678E+17	2.644E+18	1.237E+17	1.492E+18	3.631E+17	9.071E+10	3.617E+19	1.101E+07
F2	B	1.300E+03	1.176E+03	1.234E+03	1.236E+03	1.328E+03	1.236E+03	1.274E+03	2.547E+03	1.129E+03
	W	3.009E+03	3.023E+03	3.809E+03	3.296E+03	3.494E+03	2.860E+03	2.729E+03	3.913E+03	2.422E+03
	M	2.325E+03	2.034E+03	2.455E+03	2.190E+03	2.337E+03	2.061E+03	2.010E+03	3.352E+03	1.815E+03
	V	6.864E+04	1.092E+05	2.010E+05	1.225E+05	1.283E+05	5.768E+04	7.708E+04	5.539E+04	1.003E+04
F3	B	7.410E+02	7.100E+02	7.260E+02	7.180E+02	7.290E+02	7.270E+02	7.220E+02	7.880E+02	7.090E+02
	W	8.890E+02	7.870E+02	9.500E+02	8.560E+02	9.090E+02	8.090E+02	8.440E+02	1.340E+03	7.870E+02
	M	7.870E+02	7.690E+02	7.620E+02	7.630E+02	7.910E+02	7.590E+02	7.850E+02	1.010E+03	7.450E+02
	V	1.790E+02	1.810E+02	5.140E+02	5.020E+02	5.780E+02	1.710E+02	4.530E+02	1.260E+04	1.740E+02
F4	B	1.904E+03	1.900E+03	1.902E+03	1.901E+03	1.902E+03	1.904E+03	1.900E+03	3.410E+03	1.900E+03
	W	1.526E+05	3.840E+03	1.098E+05	3.440E+03	8.571E+04	8.797E+04	1.921E+03	3.074E+06	1.905E+03
	M	2.227E+03	1.987E+03	2.545E+03	1.924E+03	3.048E+03	2.713E+03	1.908E+03	2.978E+05	1.902E+03
	V	2.280E+07	4.104E+04	2.202E+07	6.884E+03	3.645E+07	1.817E+07	8.804E+00	7.966E+10	7.103E−01
F5	B	4.636E+03	1.758E+03	4.178E+03	1.812E+03	3.518E+03	3.116E+03	2.690E+03	7.260E+04	1.754E+03
	W	4.258E+06	2.942E+06	4.426E+06	2.588E+06	4.022E+06	6.114E+05	2.762E+05	5.436E+07	1.660E+04
	M	1.524E+05	2.609E+04	3.046E+05	1.652E+04	5.891E+05	1.250E+05	3.694E+04	4.845E+06	2.425E+03
	V	2.207E+11	4.834E+10	6.701E+11	2.670E+10	5.060E+11	1.719E+10	1.624E+09	4.373E+13	3.242E+05
F6	B	1.610E+03	1.601E+03	1.603E+03	1.602E+03	1.603E+03	1.601E+03	1.602E+03	1.900E+03	1.601E+03
	W	2.452E+03	2.615E+03	2.657E+03	2.445E+03	2.365E+03	2.088E+03	2.301E+03	3.288E+03	1.975E+03
	M	1.858E+03	1.834E+03	1.994E+03	1.860E+03	1.892E+03	1.767E+03	1.833E+03	2.562E+03	1.716E+03
	V	1.763E+04	1.833E+04	2.662E+04	2.207E+04	1.953E+04	8.210E+03	1.557E+04	5.267E+04	6.462E+03
F7	B	2.777E+03	2.112E+03	2.334E+03	2.104E+03	2.971E+03	2.202E+03	2.269E+03	7.448E+03	2.103E+03
	W	3.541E+06	3.541E+06	7.669E+06	3.541E+06	1.497E+06	2.011E+05	2.449E+05	2.621E+07	4.111E+03
	M	7.344E+04	2.477E+04	1.569E+05	4.227E+04	3.757E+05	1.053E+04	1.350E+04	2.926E+06	2.610E+03
	V	2.046E+11	7.235E+10	6.043E+11	1.339E+11	1.033E+12	2.370E+08	3.252E+08	6.092E+12	6.713E+04
F8	B	2.254E+03	2.222E+03	2.254E+03	2.229E+03	2.225E+03	2.220E+03	2.221E+03	2.415E+03	2.213E+03
	W	3.796E+03	4.052E+03	5.085E+03	4.065E+03	4.544E+03	3.681E+03	4.016E+03	5.117E+03	3.450E+03
	M	2.375E+03	2.342E+03	2.465E+03	2.341E+03	2.449E+03	2.366E+03	2.336E+03	3.970E+03	2.305E+03
	V	1.076E+04	1.892E+04	7.994E+04	1.807E+04	1.299E+05	1.410E+04	2.766E+04	2.444E+05	4.133E+03
F9	B	2.530E+03	2.500E+03	2.508E+03	2.501E+03	2.501E+03	2.502E+03	2.501E+03	2.790E+03	2.400E+03
	W	2.890E+03	2.931E+03	2.919E+03	3.071E+03	3.038E+03	2.851E+03	2.975E+03	3.254E+03	2.784E+03
	M	2.773E+03	2.730E+03	2.796E+03	2.774E+03	2.787E+03	2.765E+03	2.796E+03	2.969E+03	2.743E+03
	V	2.705E+03	4.136E+03	2.554E+03	5.596E+03	5.747E+03	4.010E+03	7.850E+03	4.251E+03	2.754E+03
F10	B	2.882E+03	2.600E+03	2.657E+03	2.633E+03	2.650E+03	2.654E+03	2.614E+03	3.073E+03	2.600E+03
	W	3.356E+03	3.310E+03	4.056E+03	3.300E+03	3.240E+03	3.179E+03	3.056E+03	6.467E+03	3.024E+03
	M	2.992E+03	2.956E+03	3.027E+03	2.951E+03	2.972E+03	2.937E+03	2.934E+03	4.307E+03	2.930E+03
	V	1.874E+03	2.961E+03	9.400E+03	2.028E+03	3.124E+03	1.649E+03	1.576E+03	2.929E+05	1.178E+03

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表 2 算法参数

Table 2 Algorithm parameters

算法	参数	取值
PSO	惯性权重	1
	学习因子$ c_1 $和$ c_2 $	1.5, 1.5
	速度范围	[−10, 10]
LPSO	惯性权重范围	[0.3, 1.2]
	学习因子$ c_1 $和$ c_2 $	1.5, 1.5
	速度范围	[−10, 10]
PSO-SA	惯性权重	1
	学习因子$ c_1 $和$ c_2 $	1.5, 1.5
	速度范围	[−10, 10]
ACVDEPSO	惯性权重范围	[0.3, 1.2]
	个体因子范围	[0.1, 1.0]
	群体因子范围	[0.1, 1.0]
	速度范围	[−10, 10]
MSEPSO	惯性权重范围	[0.3, 1.2]
	学习因子$ c_1 $和$ c_2 $	取决于$ \omega(t) $
	记忆衰减系数$ \mu $	0.5
	速度范围	[−10, 10]

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表 3 适应度函数参数

Table 3 Fitness function parameters

参数	取值
$ \omega_1 $	1
$ \omega_2 $	0.1
$ \omega_3 $	10000
$ \omega_4 $	1000

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表 4 不同场景下的实验数据

Table 4 Experimental data in various scenarios

场景	算法	最优解	最劣解	均值	方差
简单山地	PSO	169.646	179.254	173.620	12.406
	LPSO	168.709	177.567	174.373	14.606
	PSO-SA	168.436	179.876	172.464	12.885
	ACVDEPSO	169.797	176.635	173.958	7.560
	MSEPSO	164.126	166.190	165.019	0.562
复杂山地	PSO	173.037	194.175	178.897	79.749
	LPSO	174.499	185.884	178.400	21.195
	PSO-SA	169.102	187.409	179.600	56.882
	ACVDEPSO	166.905	192.133	178.073	89.173
	MSEPSO	164.785	169.644	166.695	6.522
城市场景1	PSO	185.967	195.121	189.353	12.045
	LPSO	324.035	487.195	420.122	5183.551
	PSO-SA	183.576	190.903	187.306	7.711
	ACVDEPSO	185.522	215.841	194.130	168.304
	MSEPSO	182.906	187.127	184.020	3.124
城市场景2	PSO	165.621	175.897	169.139	17.071
	LPSO	163.833	173.548	167.518	15.471
	PSO-SA	163.906	168.039	165.526	2.675
	ACVDEPSO	165.009	177.137	170.999	30.845
	MSEPSO	161.070	162.970	162.491	0.640

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