[1]杨火根,王 艳,骆 伟.改进粒子群算法的无人机 B 样条曲线路径规划[J].郑州大学学报(工学版),2025,46(04):8-15.[doi:10.13705/j.issn.1671-6833.2025.04.014]
　YANG Huogen,WANG Yan,et al.Improved Particle Swarm Algorithm for B-spline Curve Path Planning of Unmanned Aerial Vehicles[J].Journal of Zhengzhou University (Engineering Science),2025,46(04):8-15.[doi:10.13705/j.issn.1671-6833.2025.04.014]
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改进粒子群算法的无人机 B 样条曲线路径规划()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年04期
页码:
8-15
栏目:
出版日期:
2025-07-10

文章信息/Info

Title:
Improved Particle Swarm Algorithm for B-spline Curve Path Planning of Unmanned Aerial Vehicles
文章编号:
1671-6833(2025)04-0008-08
作者:
杨火根1; 2;  王 艳1;  骆 伟3
1. 江西理工大学 理学院,江西 赣州 341000;2. 江西理工大学 多维智能感知与控制江西省重点实验室,江西 赣州341000;3. 江西理工大学 体育与艺术学院,江西 赣州 341000
Author(s):
YANG Huogen1;  2;  WANG Yan1;  LUO Wei3
1. School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China; 2. Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control, Jiangxi University of Science and Technology, Ganzhou 341000, China; 3. School of Sports and Arts, Jiangxi University of Science and Technology, Ganzhou 341000, China
关键词:
无人机;  B 样条曲线;  路径规划;  避障;  改进粒子群算法
Keywords:
UAV;  B-spline curves;  path planning;  obstacle avoidance;  improved particle swarm algorithm
分类号:
TP18V249V279
DOI:
10.13705/j.issn.1671-6833.2025.04.014
文献标志码:
A
摘要:
针对粒子群算法在无人机路径规划中易陷入局部最优解,且在离散路径点光滑处理后对避障考虑不足的问题,提出一种基于改进粒子群算法的无人机三维 B 样条曲线路径规划方法。 首先,综合考虑无人机路径长度、安全避障、飞行高度及平稳性等飞行性能要求,利用 B 样条曲线的几何性质构建路径规划模型; 其次,采用改进的粒子群算法对模型进行求解,算法改进主要通过优化粒子初始化策略、惯性权重因子和学习因子更新策略、增加粒子扰动策略来实现;最后,在 CEC2017 标准测试函数集上进行测试。 结果表明:改进的粒子群算法在对比算法中表现出更强的寻优能力,稳定性也更好。 两个场景的仿真结果表明:所规划的路径代价可减少 2%,稳定性可提高 65%,路径安全避障且 C2 连续,能满足无人机飞行综合性能要求。
Abstract:
To address the problem of getting stuck in local optimal solutions in UAV path planning with particle swarm algorithm and insufficient consideration for obstacle avoidance after smoothing discrete path points, a threedimensional B-spline curve path planning method for unmanned aerial vehicles based on an improved particle swarm algorithm was proposed. Firstly, considering the flight performance requirements such as UAV path length, safe obstacle avoidance, flight altitude, and smoothness, a path planning model was constructed using the geometric properties of B-spline curves. Then, an improved particle swarm algorithm was used to solve the model. The algorithm improvement was mainly achieved by optimizing the particle initialization strategy, updating the inertia weight factor and learning factor strategy, and increasing the particle perturbation strategy. The test results on the CEC2017 standard test function set showed that the improved particle swarm algorithm exhibited stronger optimization ability and better stability compared to other algorithms. The simulation results of two scenarios showed that the planned path cost was reduced by 2%, stability was improved by 65%, path safety avoided obstacles and C 2 was continuous, which could meet the comprehensive performance requirements of UAV flight.

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更新日期/Last Update: 2025-07-12