[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]

弹箭与制导学报 >

2023 , Vol. 43 >Issue 5: 9 - 15

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.05.002

基于蚁群算法的巡飞弹药飞行路径规划研究

  • 张搏睿 1 ,
  • 刘帅 2 ,
  • 唐宏 3 ,
  • 张本康 1 ,
  • 闫祁晨 1 ,
  • 沈剑 1
展开
  • 1 中北大学机电工程学院,山西 太原 030051
  • 2 陆军装备部驻太原地区第四军事代表室,山西 太原 030027
  • 3 晋西工业集团有限责任公司,山西 太原 030027

张搏睿(1999—),男,硕士研究生,研究方向:智能弹药编队协同任务规划。

收稿日期: 2022-12-30

  网络出版日期: 2024-12-30

基金资助

国家自然科学基金(62003314)

国家自然科学基金(51909245)

航空科学基金(2019020U0002)

山西省自然科学基金(201901D211244)

收起

Research on Flight Path Planning of Loitering Munition Based on Ant Colony Algorithm

  • ZHANG Borui 1 ,
  • LIU Shuai 2 ,
  • TANG Hong 3 ,
  • ZHANG Benkang 1 ,
  • YAN Qichen 1 ,
  • SHEN Jian 1
Expand
  • 1 School of Mechanical and Electrical Engineering,North University of China,Taiyuan 030051,Shanxi,China
  • 2 Fourth Military Representative Office of Army Equipment Department in Taiyuan Area,Taiyuan 030027,Shanxi,China
  • 3 Jinxi Industrial Group Co. Ltd.,Taiyuan 030027,Shanxi,China

Received date: 2022-12-30

  Online published: 2024-12-30

Fold

摘要

针对巡飞弹药巡飞航迹规划算法设计以及作战任务想定,结合一种基于二维和三维数字地形的空间模型,提出基于自然界蚂蚁觅食的最短路径理论的航迹规划算法,实现巡飞弹药执行对既定作战区域定点威胁目标避障巡飞的航迹规划方案,对巡飞弹药已建立的环境中进行飞行模拟仿真和结果分析,验证了蚁群算法的正确性和有效性。

关键词： 巡飞弹药; 航迹规划; 蚁群算法; 定点威胁

本文引用格式

张搏睿 , 刘帅 , 唐宏 , 张本康 , 闫祁晨 , 沈剑 . 基于蚁群算法的巡飞弹药飞行路径规划研究[J]. 弹箭与制导学报, 2023 , 43(5) : 9 -15 . DOI: 10.15892/j.cnki.djzdxb.2023.05.002

Abstract

For loitering munition patrol flight trajectory path planning algorithm design and combat mission scenarios, combined with a space based on 2D and 3D digital terrain model, based on the nature of ants foraging path planning algorithm of the shortest path theory, implement cruise missile threat to the established operational area designated objectives cruise flight path planning of obstacle avoidance. The correctness and effectiveness of the ant colony algorithm are verified by the flight simulation and analysis of the patrolling missile in the established environment.

Key words： loitering munition; track planning; ant colony algorithm; fixed point threat

[an error occurred while processing this directive]

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
张从泾, 白文华, 郑红生. 智能化弹药巡飞弹最新发展概述及展望[J]. 国防制造技术, 2021(1): 3-7.

ZHANG C J, BAI W H, ZHENG H S. Overview and prospect of the latest development of intelligent ammunition patrol missile[J]. Defense Manufacturing Technology, 2021(1): 3-7.

[2]
LUO S, WEI Z H, MA Z Y, et al. Multi-loitering munitions cooperative interference resource allocation based on hybrid ant colony algorithm[C]// Proceedings of the 33rd Chinese Control and Decision Conference, May 22-24, 2021. Kunming: Kunming University of Science and Technology, 2021: 3908-3913.

[3]
SHEN J, ZHANG B K, ZHU Q Y, et al. A deep Q-network-based collaborative control research for smart ammunition formation[J]. International Journal of Aerospace Engineering, 2022(15): 2021693.

[4]
郝延军, 罗军, 陈治平, 等. 改进的蚁群算法在无人机航路规划中的应用[J]. 无线电工程, 2013, 43(6): 54-57.

HE Y J, LUO J, CHEN Z P, et al. Application of improved ant colony algorithm in path planning of unmanned aircraft system[J]. Radio Engineering, 2013, 43(6): 54-57.

[5]
谢凯利, 杨海涛, 谢海平. 智能航迹规划算法研究现状与展望[J]. 兵器装备工程学报, 2020, 41(21): 8-13.

XIE K L, YANG H T, XIE H P. Research status of intelligent track planning algorithm[J]. Journal of Ordnance Equipment Engineering, 2020, 41(21): 8-13.

[6]
SHEN J, ZHU Q Y, WANG X G, et al. Typical fault estimation and dynamic analysis of a leader-follower unmanned aerial vehicle formation[J]. International Journal of Aerospace Engineering, 2021, 2021: 6656422.

[7]
王辉. 巡飞弹药航迹规划算法优化研究[D]. 沈阳: 沈阳理工大学, 2021.

WANG H. Research on optimization of track planning algorithm for loitering munition[D]. Shenyang: Shenyang Ligong University, 2021.

[8]
刘永兰, 李为民, 吴虎胜, 等. 基于狼群算法的无人机航迹规划[J]. 系统仿真学报, 2015, 27(8): 1838-1843.

LIU Y L, LI W M, W H S, et al. Track planning for unmanned aerial vehicles based on wolf pack algorithm[J]. Journal of System Simulation, 2015, 27(8): 1838-1843.

[9]
于霜, 丁力, 吴洪涛. 基于改进人工蜂群算法的无人机的航迹规划[J]. 电光与控制, 2017, 24(1): 19-23.

YU S, DING L, WU H T. Path planning for UAVs based on improved artificial bee colony algorithm[J]. Electronics Optics & Control, 2017, 24(1): 19-23.

[10]
陈礼琪. 基于改进蚁群算法的无人机路径规划[J]. 计算机与数字工程, 2022, 50(3): 538-543.

CHEN L Q. UAV path planning based on improved ant colony algorithm[J]. Computer & Digital Engineering, 2022, 50(3): 538-543.

[11]
艾洪舟, 何鑫, 刘全义, 等. 通用航空山区救援安全航迹规划模型[J]. 安全与环境学报, 2023(2): 506-514.

AI H Z, HE X, LIU Q Y, et al. Safe flight path planning model for general aviation mountain rescue[J]. Journal of Safety and Environment, 2023(2): 506-514.

[12]
丁明跃, 郑昌文, 周成平, 等. 无人飞行器航迹规划[M]. 北京: 电子工业出版社, 2009: 39-40.

DING M Y, ZHENG C W, ZHOU C P, et al. Flight path planning for unmanned aerial vehicles[M]. Beijing: Publishing House of Electronics Industry, 2009: 39-40.

[13]
李怀涛. 巡飞弹导引头及制导控制技术研究[D]. 北京: 北京理工大学, 2016.

LI H T. Research on seeker, guidance and control technology for loitering munition[D]. Beijing: Beijing Institute of Technology, 2016.

[14]
陈忠俊, 赵军, 叶方舟. 面向空地协同任务的航路规划约束条件研究[C]// 中国航空工业技术装备工程协会. 2020中国航空工业技术装备工程协会年会论文集. 西安: 《测控技术》杂志社, 2020: 19-23.

CHEN Z J, ZHAO J, YE F Z. Research on constraints of route planning for airground collaborative tasks[C]// China Aviation Industry Technology and Equipment Engineering Association. Proceedings of the Annual Meeting of China Aviation Industry Technology and Equipment Engineering Association. Xi’an: Measurement and Control Technology Publishing House, 2020: 19-23.

[15]
王景杰, 吴文启, 梁朝阳, 等. 无人飞行器航迹规划评价方法研究[J]. 计算机测量与控制, 2012, 20(8): 2193-2196.

WANG J J, WU W H, LIANG C Y, et al. Study on path planning evaluation method for UAV[J]. Computer Measurement & Control, 2012, 20(8): 2193-2196.

[16]
李眩, 吴晓兵, 刘琼. 基于带变异的自适应精英改进蚁群算法的物流配送中心选址问题求解[J]. 成都大学学报(自然科学版), 2022, 41(1): 46-51.

LI X, WU X B, LIU Q. Solution to location problem of logistics distribution center based on adaptive elite ant colony algorithm with mutation[J]. Journal of Chengdu University(Natural Science Edition), 2022, 41(1): 46-51.

[17]
刘之滨, 赵欣然. 基于蚁群算法的移动自组网AODV路由协议动态优化[J]. 电子设计工程, 2022, 30(2): 151-155.

LIU Z B, ZHAO X R. Dynamic optimization of AODV routing protocol in mobile ad hoc network based on ant colony algorithm[J]. Electronic Design Engineering, 2022, 30(2): 151-155.

[18]
ZHOU Y L, HUANG N N. Airport AGV path optimization model based on ant colony algorithm to optimize Dijkstra algorithm in urban systems[J]. Sustainable Computing: Informatics and Systems, 2022, 35: 100716.

[19]
李宪强, 马戎, 张伸, 等. 蚁群算法的改进设计及在航迹规划中的应用[J]. 航空学报, 2020, 41(增刊2): 213-219.

LI X Q, MA R, ZHANG K, et al. Improved design of ant colony algorithm and its application in path planning[J]. Acta Aeronautica ET Astronautica Sinica, 2020, 41(S2): 213-219.

Options
文章导航

/

〈 〉

[an error occurred while processing this directive]