北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (2): 406-413.doi: 10.13700/j.bh.1001-5965.2020.0203

• 论文 • 上一篇    下一篇

基于改进人工鱼群算法的无人直升机编队航迹规划

马梓元, 龚华军, 王新华   

  1. 南京航空航天大学 自动化学院, 南京 211100
  • 收稿日期:2020-05-22 发布日期:2021-03-08
  • 通讯作者: 龚华军 E-mail:ghj301@nuaa.edu.cn
  • 作者简介:马梓元,男,硕士研究生。主要研究方向:先进飞行控制技术;龚华军,男,博士,教授,博士生导师。主要研究方向:先进飞行控制技术、飞行综合控制、系统建模与仿真;王新华,男,博士,副教授,硕士生导师。主要研究方向:舰载机着舰引导与控制、直升机飞行控制、无人机飞行控制。
  • 基金资助:
    中央高校基本科研业务费专项资金(NZ2019008)

Trajectory planning of unmanned helicopter formation based on improved artificial fish swarm algorithm

MA Ziyuan, GONG Huajun, WANG Xinhua   

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Received:2020-05-22 Published:2021-03-08

摘要: 针对无人直升机(UH)编队的航迹规划问题,提出了一种基于改进人工鱼群算法(AFSA)的航迹规划算法。从邻域学习和算法特性2个角度出发,针对人工鱼群算法中的人工鱼视野模型提出了一种人工鱼自适应视野模型,并对其鱼群的进化策略在无性繁殖方式的基础上进行了改进;从规划原理、代价函数、约束条件3个方面建立了无人直升机编队航迹规划模型;针对航迹规划中普遍存在的搜索效率低、精度差等特有问题改进了所提算法的编码方式和聚类策略。利用三机编队航迹规划的算例对所提算法进行了验证,仿真结果证明,通过对人工鱼群算法的改进、航迹规划模型的建立等措施实现了良好的无人直升机编队航迹规划,同时在搜索效率、收敛速度及求解精度上都有了显著提高。

关键词: 人工鱼群算法(AFSA), 无人直升机(UH), 编队, 航迹规划, 聚类策略

Abstract: To solve formation path planning problem of the Unmanned Helicopter (UH), a path planning algorithm is proposed based on improved Artificial Fish Swarm Algorithm (AFSA). An adaptive vision model of artificial fish for artificial fish swarm algorithm was put forward from two aspects of neighborhood learning and algorithm characteristics. The evolutionary strategy of fish swarm was improved on the basis of asexual reproduction. The trajectory planning model of unmanned helicopter formation was established from three aspects of planning principle, cost function and constraint conditions. The coding method and clustering strategy were improved in order to solve low searching efficiency and poor accuracy problems in route planning. An example of three-aircraft formation path planning was used to verify the proposed method. Simulation results indicate that, through the improvement of AFSA, the establishment of trajectory planning model and other measures, good unmanned helicopter formation path planning can be achieved, and meanwhile the search efficiency, convergence velocity and solution accuracy are improved significantly.

Key words: Artificial Fish Swarm Algorithm (AFSA), Unmanned Helicopter (UH), formation, trajectory planning, clustering strategy

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