基于改进蜘蛛蜂优化算法的三维多机避障航迹规划方法

王麒权; 肖作林; 程东青; 向凯

doi:10.12382/bgxb.2025.0475

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基于改进蜘蛛蜂优化算法的三维多机避障航迹规划方法

更新时间：2026-05-06

- 基于改进蜘蛛蜂优化算法的三维多机避障航迹规划方法
- 3D Multi-UAV Obstacle Avoidance Path Planning Method Based on Improved Spider Wasp Optimization Algorithm
- 兵工学报 2026年47卷第4期页码：250475
- 作者机构：
  
  北京机电工程研究所，北京 100074
- 作者简介：
  
  通信作者邮箱：wangqiquan2022@163.com
- 基金信息：
- DOI：10.12382/bgxb.2025.0475
  中图分类号： V249
- 收稿：2025-06-09，
  
  网络首发：2026-02-10，
  
  纸质出版：2026-04
- 稿件说明：
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王麒权, 肖作林, 程东青, 等. 基于改进蜘蛛蜂优化算法的三维多机避障航迹规划方法[J]. 兵工学报, 2026,47(4):250475.

WANG Qiquan, XIAO Zuolin, CHENG Dongqing, et al. 3D Multi-UAV Obstacle Avoidance Path Planning Method Based on Improved Spider Wasp Optimization Algorithm[J]. Acta Armamentarii, 2026, 47(4): 250475.
王麒权, 肖作林, 程东青, 等. 基于改进蜘蛛蜂优化算法的三维多机避障航迹规划方法[J]. 兵工学报, 2026,47(4):250475. DOI： 10.12382/bgxb.2025.0475.

WANG Qiquan, XIAO Zuolin, CHENG Dongqing, et al. 3D Multi-UAV Obstacle Avoidance Path Planning Method Based on Improved Spider Wasp Optimization Algorithm[J]. Acta Armamentarii, 2026, 47(4): 250475. DOI： 10.12382/bgxb.2025.0475.

摘要

针对复杂地形下的多机避障航迹规划问题，提出一种基于改进蜘蛛蜂优化（Improvement Spider Wasp Optimization，ISWO）算法的三维多机避障航迹规划方法。考虑环境及自身性能约束建立多机航迹规划的数学模型，在标准SWO算法的基础上优化航迹点划分方式；以立体几何的方式替代随机生成，提升航迹点生成效率，并引入动态反向学习，拓展搜索能力；采用非均匀有理B样条曲线对离散航迹点进行拟合，并通过仿真实验对改进后算法的初始化效率与整体效率进行对比验证。仿真结果表明，相比于SWO和粒子群优化算法，ISWO算法在收敛速度与优化效果上具有明显优势。

Abstract

In response to the obstacle avoidance path planning for multi-unmanned aerial vehicles (multi-UAVs) in complex terrains

a three-dimensional multi-UAV obstacle avoidance path planning method based on improved spider wasp optimization (SWO) algorithm is proposed. A mathematical model for multi-UAV path planning is established by considering the environmental constraints and performance limitations

and the path points division method is optimized based on standard SWO algorithm. The efficiency of generating the path points is improved by replacing random generation with a solid geometry method

and a dynamic reverse learning is introduced to expand the search capability. The discrete path points are fitted using the non-uniform rational B-spline (NURBS) curve

and the initialization and overall efficiencies of the improved algorithm are verified through simulation experiment. The simulated results demonstrate that the improved algorithm exhibits significant advantages in terms of convergence speed and optimization effectiveness compared with SWO and PSO algorithms.

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