基于无碰撞轨迹的无人机路径规划方法研究

doi:10.16182/j.issn1004731x.joss.25-0028

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (3): 808-817.doi: 10.16182/j.issn1004731x.joss.25-0028

• 论文 • 上一篇

基于无碰撞轨迹的无人机路径规划方法研究

谢俊¹, 张琦², 彭延云², 石浩楠², 李东阳², 刘熙³^,⁴

^1.国家电网有限公司华中分部，湖北武汉 430077
^2.北京机械设备研究所，北京 100854
^3.南瑞集团(国网电力科学研究院)有限公司，江苏南京 210061
^4.国国网电力科学研究院武汉南瑞有限责任公司，湖北武汉 430074

收稿日期:2025-01-07 修回日期:2025-03-19 出版日期:2026-03-18 发布日期:2026-03-27
通讯作者: 张琦
第一作者简介:谢俊(1980-)，男，高工，博士，研究方向为电网设备原理及管理。
基金资助:
国家电网有限公司总部管理科技项目(5500-202304542A-3-2-ZN)

Research on UAV Path Planning Method Based on Collision Free Trajectory

Xie Jun¹, Zhang Qi², Peng Yanyun², Shi Haonan², Li Dongyang², Liu Xi³^,⁴

^1.Central China Branch of State Grid Corporation, Wuhan 430077, China
^2.Beijing Machinery and Equipment Research Institute, Beijing 100854, China
^3.Nanrui Group (State Grid Electric Power Research Institute) Co. , LTD. , Nanjing 210061, China
^4.State Grid Electric Power Research Institute Wuhan Nanrui Co. , LTD. , Wuhan 430074, China

Received:2025-01-07 Revised:2025-03-19 Online:2026-03-18 Published:2026-03-27
Contact: Zhang Qi

摘要/Abstract

摘要：

针对无人机的自主路径规划方法规划路径质量差、耗时长、效率低的问题，提出了一种基于无碰撞轨迹的无人机路径规划方法。在不确定性前提下，设置时间相关虚拟点和碰撞阈值，将障碍物建模为矩形，并定义矩形周围的兴趣点，建立无人机与障碍物的不确定性优化模型，得到无人机无碰撞轨迹的可允许边缘；采用改进的PRM方法求解边缘，得到无人机的无碰撞轨迹；采用仿真软件的UAV工具箱对无人机的路径规划方法进行仿真和实验。实验结果表明：所提方法更适合于无人机的实时自主路径规划，提高了无人机自主路径规划的性能。

关键词: 无碰撞轨迹, 无人机, 路径规划, 障碍物建模, PRM, 仿真

Abstract:

In view of the problems of poor quality, long time consumption, and low efficiency of the autonomous path planning method for unmanned aerial vehicles, a path planning method for unmanned aerial vehicles based on a collision-free trajectory was proposed. Under the premise of uncertainty, the time-related virtual points and collision threshold were set; the obstacle was modeled as a rectangle; the interest points around the rectangle were defined. The uncertainty optimization model between the unmanned aerial vehicles and the obstacle was established, so as to obtain the allowable edge of the collision-free trajectory of the unmanned aerial vehicles. The improved PRM method was adopted to solve the edge, so as to obtain the collision-free trajectory of unmanned aerial vehicles. The UAV toolbox of simulation software was used to simulate and experiment on the path planning method for unmanned aerial vehicles. The results show that the proposed method is more suitable for real-time autonomous path planning for unmanned aerial vehicles, thus improving the autonomous path planning performance of unmanned aerial vehicles.

Key words: collision free trajectory, UAV, path planning, model the obstacle as a rectangle, probabilistic roadmap method(PRM), simulation

中图分类号:

TP18

谢俊,张琦,彭延云等 . 基于无碰撞轨迹的无人机路径规划方法研究[J]. 系统仿真学报, 2026, 38(3): 808-817.

Xie Jun,Zhang Qi,Peng Yanyun,et al . Research on UAV Path Planning Method Based on Collision Free Trajectory[J]. Journal of System Simulation, 2026, 38(3): 808-817.

图/表 14

图1

图2

图3

图4

表1

仿真参数和软件环境

主要参数	参数值
操作系统	Windows 10
CPU	Intel(R) Core(TM) i5-6200U 2.30 GHz
地图尺寸/m	400×600~20 000×20 000
障碍物数量	$4 ~ 6$
障碍物尺寸/m	3×3~2 000×2 000
碰撞阈值 $ρ$ /m	10
无人机速度限制/(m/s)	$5 ~ 9$
模拟迭代最大数	300

表1

图5

图6

表2

图7

图8

图9

表3

图10

图11

参考文献 18

[1]	Jayarajan Niresh, Ganesan Tamilselvan, Naganathan Archana. Optimal Trajectories for UAV Three-dimensional Path Planning Using a Hybrid ABC-RRT* Algorithm[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2025, 239(3): 930-943.
[2]	Sikora Toma, Papić Vladan. Survey of Path Planning for Aerial Drone Inspection of Multiple Moving Objects[J]. Drones, 2024, 8(12): 705.
[3]	Kevin B de Carvalho, Hiago de O B Batista, Fagundes-Junior Leonardo A,et al. Q-learning Global Path Planning for UAV Navigation with Pondered Priorities[J]. Intelligent Systems with Applications, 2025, 25: 200485.
[4]	Samir Moataz, Ebrahimi Dariush, Assi Chadi, et al. Leveraging UAVs for Coverage in Cell-free Vehicular Networks: A Deep Reinforcement Learning Approach[J]. IEEE Transactions on Mobile Computing, 2021, 20(9): 2835-2847.
[5]	Miao Fahui, Li Hangyu, Yan Guanjie, et al. Optimizing UAV Path Planning in Maritime Emergency Transportation: A Novel Multi-strategy White Shark Optimizer[J]. Journal of Marine Science and Engineering, 2024, 12(7): 1207.
[6]	Jiang Wei, Yongxi Lü, Li Yongfeng, et al. UAV Path Planning and Collision Avoidance in 3D Environments Based on POMPD and Improved Grey Wolf Optimizer[J]. Aerospace Science and Technology, 2022, 121: 107314.
[7]	Zammit Christian, van Kampen Erik-Jan. Real-time 3D UAV Path Planning in Dynamic Environments with Uncertainty[J]. Unmanned Systems, 2023, 11(3): 203-219.
[8]	Liu Haiying, Chen Jie, Feng Jianxin, et al. An Improved RRT* UAV Formation Path Planning Algorithm Based on Goal Bias and Node Rejection Strategy[J]. Unmanned Systems, 2023, 11(4): 317-326.
[9]	Zhang Nan, Yue Linkai, Zhang Qiang, et al. A UAV Coverage Path Planning Method Based on a Diameter-height Model for Mountainous Terrain[J]. Applied Sciences, 2025, 15(4): 1988.
[10]	Xu Hong, Niu Zijing, Jiang Bo, et al. ERRT-GA: Expert Genetic Algorithm with Rapidly Exploring Random Tree Initialization for Multi-UAV Path Planning[J]. Drones, 2024, 8(8): 367.
[11]	Zou Zixuan, Wang Guoshuai, Li Zhenshuo, et al. MFO-Fusion: A Multi-frame Residual-based Factor Graph Optimization for GNSS/INS/LiDAR Fusion in Challenging GNSS Environments[J]. Remote Sensing, 2024, 16(17): 3114.
[12]	Li Shengyu, Li Xingxing, Wang Huidan, et al. Multi-GNSS PPP/INS/Vision/LiDAR Tightly Integrated System for Precise Navigation in Urban Environments[J]. Information Fusion, 2023, 90: 218-232.
[13]	Anıl Gökhan Göktaş, Sezer Semih. A New Local Path Planning Approach by Synthesis of PRM and RRT* Algorithms for an Autonomous Mobile Robot[J]. Journal of Control, Automation and Electrical Systems, 2025, 36(1): 72-85.
[14]	黄志锋, 刘媛华. 基于改进哈里斯鹰和B-spline曲线的无人机路径规划研究[J]. 系统仿真学报, 2024, 36(7): 1509-1524.
	Huang Zhifeng, Liu Yuanhua. UAV Path Planning Based on Improved Harris Hawk Algorithm and B-spline Curve[J]. Journal of System Simulation, 2024, 36(7): 1509-1524.
[15]	Chen Guojun, Cheng Danguo, Chen Wei, et al. Path Planning for AUVs Based on Improved APF-AC Algorithm[J]. Computers, Materials and Continua, 2024, 78(3): 3721-3741.
[16]	Li Yanchu, Dong Xinzhou, Ding Qingqing, et al. Improved A-STAR Algorithm for Power Line Inspection UAV Path Planning[J]. Energies, 2024, 17(21): 5364.
[17]	Shen Junyi, Fan Jianglong, Chan Tszho. Microplastic Sampling Path Planning for Unmanned Surface Vehicle (USV) Based on RRT* and Dijkstra Fusion Algorithm[J]. Journal of Physics: Conference Series, 2024, 2897(1): 012023.
[18]	林俊强, 王红军, 邹湘军, 等. 基于DPPO的移动采摘机器人避障路径规划及仿真[J]. 系统仿真学报, 2023, 35(8): 1692-1704.
	Lin Junqiang, Wang Hongjun, Zou Xiangjun, et al. Obstacle Avoidance Path Planning and Simulation of Mobile Picking Robot Based on DPPO[J]. Journal of System Simulation, 2023, 35(8): 1692-1704.

场景	路径总距离		起点到终点实际距离
场景	本文	改进的APF	起点到终点实际距离
I	528.85	567.29	500
II	543.24	561.71	500
III	4 911.52	5 146.65	4 500

算法	迭代数	t/s	路径距离/m
本文	4	2.23	598.74
改进A*	13	7.30	627.65
RRT*&Dijkstra	17	5.90	635.86
PPO	26	9.70	597.27

基于无碰撞轨迹的无人机路径规划方法研究

Research on UAV Path Planning Method Based on Collision Free Trajectory

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