Improved Particle Swarm Algorithm for B-spline Curve Path Planning of Unmanned Aerial Vehicles

NAVIGATE

Table of Contents

STATISTICS

Viewed70

Downloads19

Improved Particle Swarm Algorithm for B-spline Curve Path Planning of Unmanned Aerial Vehicles

PDF下载 (19)

[1]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]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 04 Page number: 8-15 Column: Public date: 2025-07-10

Title:: Improved Particle Swarm Algorithm for B-spline Curve Path Planning of Unmanned Aerial Vehicles

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

Keywords:: UAV; B-spline curves; path planning; obstacle avoidance; improved particle swarm algorithm

CLC:: TP18V249V279

DOI:: 10.13705/j.issn.1671-6833.2025.04.014

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.

References:: [1] RADMANESH M, KUMAR M, GUENTERT P H, et al. Overview of path-planning and obstacle avoidance algorithms for UAVs: a comparative study [ J ] . Unmanned Systems, 2018, 6(2) : 95-118.
[2] PUENTE-CASTRO A, RIVERO D, PAZOS A, et al. A review of artificial intelligence applied to path planning in UAV swarms [ J ] . Neural Computing and Applications, 2022, 34(1) : 153-170.
[3] SUN J Y, TANG J, LAO S Y. Collision avoidance for cooperative UAVs with optimized artificial potential field algorithm[ J] . IEEE Access, 2017, 5: 18382-18390.
[4] 陈江义, 殷笑勇, 王婷婷, 等. 基于改进斥力模型的人工势场局部路径规划 [ J] . 郑州大学学报 ( 工学版) , 2023, 44(3) : 83-87, 101.
CHEN J Y, YIN X Y, WANG T T, et al. Local path planning of artificial potential field based on improved repulsive model[ J] . Journal of Zhengzhou University ( Engineering Science) , 2023, 44(3) : 83-87, 101.
[5] AIT-SAADI A, MERAIHI Y, SOUKANE A, et al. A novel hybrid chaotic Aquila optimization algorithm with simulated annealing for unmanned aerial vehicles path planning[ J] . Computers and Electrical Engineering, 2022, 104: 108461.
[6] JIN Q B, TANG C N, CAI W. Research on dynamic path planning based on the fusion algorithm of improved ant colony optimization and rolling window method[ J] . IEEE Access, 2021, 10: 28322-28332.
[7] XIONG T, LI H, DING K, et al. A hybrid improved symbiotic organisms search and sine-cosine particle swarm optimization method for drone 3D path planning [ J ] . Drones, 2023, 7(10) : 633.
[8] 高岳林, 武少华. 基于自适应粒子群算法的机器人路径规划[ J] . 郑州大学学报( 工学版) , 2020, 41( 4) : 46-51.
GAO Y L, WU S H. Robot path planning based on adaptive particle swarm optimization[J]. Journal of Zhengzhou University (Engineering Science), 2020, 41(4): 46-51.
[9] BAO Y Y, LIU Y, WANG J S, et al. Genetic algorithm based on grid maps for solving robot path planning problem [J]. Engineering letters, 2023, 31(4): 1635-1648.
[10] ROBERGE V, TARBOUCHI M, LABONTÉ G. Fast genetic algorithm path planner for fixed-wing military UAV using GPU[ J] . IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(5) : 2105-2117.
[11] 施法中. 计算机辅助几何设计与非均匀有理 B 样条 [M] . 2 版. 北京: 高等教育出版社, 2013.
SHI F Z. CAGD & NURBS[M] . 2nd ed. Beijing: Higher Education Press, 2013.
[12] HUO F C, ZHU S, DONG H L, et al. A new approach to smooth path planning of Ackerman mobile robot based on improved ACO algorithm and B-spline curve[ J] . Robotics and Autonomous Systems, 2024, 175: 104655.
[13] LIU M J, ZHANG H X, YANG J, et al. A path planning algorithm for three-dimensional collision avoidance basedon potential field and B-spline boundary curve[ J] . Aerospace Science and Technology, 2024, 144: 108763.
[14] YAN J F, DENG T, XU B H. Three dimensional path planning for flying car based on improved A algorithm and Bezier curve fusion[ J] . Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2025, 239(6) : 2205-2219.
[15] PHUNG M D, HA Q P. Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization[ J] . Applied Soft Computing, 2021, 107: 107376.
[16] QI Z, SHAO Z H, PING Y S, et al. An improved heuristic algorithm for UAV path planning in 3D environment [ C]∥2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics. Piscataway: IEEE, 2010: 258-261.
[17] 黄书召, 田军委, 乔路, 等. 基于改进遗传算法的无人机路径规划[J]. 计算机应用, 2021, 41(2): 390-397.
HUANG S Z, TIAN J W, QIAO L, et al. Unmanned aerial vehicle path planning based on improved genetic algorithm[ J] . Journal of Computer Applications, 2021, 41 (2) : 390-397.
[18] SHI Y, EBERHART R. A modified particle swarm optimizer[C]∥IEEE World Congress on Computational Intelligence. Piscataway: IEEE, 2002: 69-73.
[19] ARORA S, ANAND P. Chaotic grasshopper optimization algorithm for global optimization [ J] . Neural Computing and Applications, 2019, 31(8) : 4385-4405.
[20] SHI Y, EBERHART R C. Empirical study of particle swarm optimization[ C]∥Congress on Evolutionary Computation. Piscataway: IEEE, 1999: 1945-1950.
[21] ZHAO W G, WANG L Y, ZHANG Z X, et al. Electric eel foraging optimization: a new bio-inspired optimizer for engineering applications[ J] . Expert Systems with Applications, 2024, 238: 122200.
[22] 宋志强, 夏庆锋, 陈少博, 等. 基于改进球面向量粒子群优化的 UAV 航迹规划[ J] . 电光与控制, 2023, 30(4) : 56-60.
SONG Z Q, XIA Q F, CHEN S B, et al. UAV path planning with improved spherical vector based particle swarm optimization [ J ] . Electronics Optics & Control, 2023, 30(4) : 56-60.
[23] 李丁, 夏露. 改进的粒子群优化算法在气动设计中的应用[ J] . 航空学报, 2012, 33(10) : 1809-1816.
LI D, XIA L. Application of improved particle swarm optimization algorithm to aerodynamic design[ J]. Acta Aeronautica et Astronautica Sinica, 2012, 33 ( 10): 1809 - 1816.

Similar References:

Memo

Last Update: 2025-07-12