[1]王 猛,王道波,王博航,等.基于改进 NSGA-II 的多无人机三维空间协同航迹规划研究[J].机械与电子,2021,(11):73-80.
　WANG Meng,WANG Daobo,WANG Bohang,et al.Three-dimensional Multi-UAV Cooperative Path Planning Based on an Improved NSGA-II Algorithm[J].Machinery & Electronics,2021,(11):73-80.
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基于改进 NSGA-II 的多无人机三维空间协同航迹规划研究()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2021年11期
页码:
73-80
栏目:
智能工程
出版日期:
2021-11-24

文章信息/Info

Title:
Three-dimensional Multi-UAV Cooperative Path Planning Based on an Improved NSGA-II Algorithm
文章编号:
1001-2257 ( 2021 ) 11-0073-08
作者:
王 猛; 王道波; 王博航; 周晨昶; 姜 燕
南京航空航天大学自动化学院,江苏 南京 210016
Author(s):
WANG Meng ;  WANG Daobo ;  WANG Bohang ;  ZHOU Chenchang ;  JIANG Yan
( College of Automation Engineering , Nanjing University of Aeronautics and Astronautics , Nanjing 210016 , China )
关键词:
NSGA-II ; 航迹规划; 协同; 自适应进化
Keywords:
NSGA-II ;  path planning ;  cooperation ;  adaptive evolution
分类号:
V249.122.3
文献标志码:
A
摘要:
三维空间多无人机协同航迹规划问题属于多约束条件下的多目标多变量问题,采用 NSGA-II算法进行解决,并对其进行改进。鉴于已知航点的分配问题,利用双染色体编码方式表述航迹;综合考虑无人机自身性能约束以及时间空间协同约束,引入时间空间协同系数表示协同约束情况,并依此改进算法中的选择操作和精英保留策略;同时,提出自适应进化策略,将个体拥挤距离大小与平均个体拥挤距离大小进行对比,并使个体交叉变异概率与迭代次数关联,避免盲目进化,加强算法的搜索能力以及收敛方向的准确性;最后,以航迹长度和受雷达威胁程度作为目标编写代价函数。仿真结果验证了改良后算法的可行性,同时与原算法相比,寻优能力增强,收敛速度及稳定性都有明显提升。
Abstract:
The problem of cooperative path planning for multi-UAVs in three-dimensional space is a multi-variable , multi-objective and multi-constrained problem , this paper uses the improved NSGA-II algorithm to solve it.The improved algorithm uses double chromosome encoding which has different kind of genes to express paths , and modify crossover and mutation accordingly.Considering track distance , safety , performance constraints and spatial and space coordination constraints comprehensively , this paper also improves the selection operation.At the same time , the iterative factor is introduced to realize the adaptive evolution of the population based on the individual crowding distance , which strengthens the algorithm search ability and convergence.The simulation results verify the feasibility of the improved algorithm, and also prove that it has better rapidity and convergence than the original algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2021-07-28
作者简介:王 猛 ( 1997- ),男,江苏扬州人,硕士研究生,研究方向为智能算法、固定翼无人机控制;王道波 ( 1957- ),男,河北易县人,博士,教授,研究方向为无人机系统、智能算法、精密伺服控制和航空发动机控制等。
更新日期/Last Update: 2021-12-03