基于GOTDBO算法的复杂约束条件下无人机航迹规划

张越; 张宁; 徐熙平; 潘越

doi:10.12382/bgxb.2024.0997

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基于GOTDBO算法的复杂约束条件下无人机航迹规划

更新时间：2025-09-25

- 基于GOTDBO算法的复杂约束条件下无人机航迹规划
- UAV Trajectory Planning under Complex Constraints Based on GOTDBO Algorithm
- 兵工学报 2025年46卷第8期页码：240997
- 作者机构：
  
  长春理工大学光电工程学院, 吉林长春 130022
- 作者简介：
  
  *zhangning@cust.edu.cn
  **xxp@cust.edu.cn
- 基金信息：
  
  吉林省科技发展计划项目(20230201052GX)
- DOI：10.12382/bgxb.2024.0997
  中图分类号：
- 收稿：2024-10-28，
  
  网络出版：2025-08-28，
  
  纸质出版：2025-08-31
- 稿件说明：
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张越, 张宁, 徐熙平, 等. 基于GOTDBO算法的复杂约束条件下无人机航迹规划[J]. 兵工学报, 2025,46(8):240997.

Yue ZHANG, Ning ZHANG, Xiping XU, et al. UAV Trajectory Planning under Complex Constraints Based on GOTDBO Algorithm[J]. Acta Armamentarii, 2025, 46(8): 240997.
张越, 张宁, 徐熙平, 等. 基于GOTDBO算法的复杂约束条件下无人机航迹规划[J]. 兵工学报, 2025,46(8):240997. DOI： 10.12382/bgxb.2024.0997.

Yue ZHANG, Ning ZHANG, Xiping XU, et al. UAV Trajectory Planning under Complex Constraints Based on GOTDBO Algorithm[J]. Acta Armamentarii, 2025, 46(8): 240997. DOI： 10.12382/bgxb.2024.0997.

摘要

针对传统蜣螂优化(Dung Beetle Optimization

DBO)算法在复杂环境下无人机航迹规划中表现出的稳定性差、寻优能力不足问题

提出一种融合复合种群策略与自适应

分布扰动的DBO算法(Group-based Optimization and adaptive

-Distribution DBO optimization algorithm

GOTDBO)。GOTDBO在DBO算法的基础上

结合复合种群初始化策略、自适应扰动全局勘探策略和自适应

分布扰动策略

可有效提升算法的全局探索和局部开发能力

提高算法的收敛速度。通过构建综合考虑总飞行长度、转角弯度和最大飞行方向变化的目标函数

并引入惩罚函数法处理路径中的禁飞区和其他约束

进一步优化航迹的平滑性与安全性。实验结果表明

在航程上

在不同复杂环境的场景中应用GOTDBO算法规划航程时

该算法能规划出紧凑高效的航迹

在最大航程指标上表现出色

可有效提升续航经济性;在威胁规避方面

采用GOTDBO算法规划的航迹接近威胁区域的次数最少

飞行安全性更高;在高度控制上

高度偏离程度低

能稳定精准控高。虽在航迹平滑度上与其他算法相当

但GOTDBO算法在多核心指标上优势显著

在无人机航迹规划中节能高效、安全可靠

具有高应用价值与广阔前景。

Abstract

The traditional dung beetle optimization algorithm (DBO) exhibits the poor stability and insufficient optimization ability in the trajectory planning of unmanned aerial vehicles (UAVs) in complex environments

DBO Optimization Algorithm with Group-based Optimization and Adaptive

-Distribution (GOTDBO) is proposed.Based on the DBO algorithm

the GOTDBO algorithm combines the composite population initialization strategy

the adaptive disturbance global exploration strategy and the adaptive

-distribution disturbance strategy

effectively enhancing the global exploration and local exploitation capabilities of the algorithm and improving the convergence speed of the algorithm.The smoothness and safety of the trajectory are further optimized by constructing an objective function that comprehensively considers the total flight length

corner curvature and maximum flight direction change

and introducing the penalty function method to handle no-fly zones and other constraints in the path

the smoothness and safety of the trajectory are further optimized.Experimental results show that

in terms of the flight range

When the GOTDBO algorithm is applied to route planning in scenarios with different complex environments

it can plan compact and efficient routes

performs excellently in terms of maximum range

and effectively improves the economy of endurance.In terms of threat avoidance

the trajectory planned by the GOTDBO algorithm has the least number of approaches to threat areas

thus ensuring higher flight safety.In terms of altitude control

the degree of altitude deviation i

s low

enabling stable and accurate altitude control.Although the GOTDBO algorithm is comparable to other algorithms in the trajectory smoothness

it has significant advantages in multiple core indicators.It is energy-saving and efficient

safe

and reliable in UAV trajectory planning

and has high application value and broad prospects.

关键词

Keywords

references

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