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

doi:10.13700/j.bh.1001-5965.2020.0203

基于改进人工鱼群算法的无人直升机编队航迹规划

doi: 10.13700/j.bh.1001-5965.2020.0203

南京航空航天大学自动化学院, 南京 211100

基金项目:

中央高校基本科研业务费专项资金 NZ2019008

详细信息

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

通讯作者:
龚华军. E-mail: ghj301@nuaa.edu.cn

中图分类号: V279
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出版历程
- 收稿日期: 2020-05-22
- 录用日期: 2020-06-18
- 网络出版日期: 2021-02-20

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

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China

Funds:

the Fundamental Research Funds for the Central Universities NZ2019008

More Information

Corresponding author: GONG Huajun. E-mail: ghj301@nuaa.edu.cn

摘要

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。
- 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.
- Artificial Fish Swarm Algorithm (AFSA) /
- Unmanned Helicopter (UH) /
- formation /
- trajectory planning /
- clustering strategy

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图 1 人工鱼群进化策略改进示意图

Figure 1. Schematic diagram of improvement of evolution strategy of artificial fish swarm algorithm

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图 2 航迹连接示意图

Figure 2. Schematic diagram of trajectory connection

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图 3 基于改进人工鱼群算法的航迹规划算法流程

Figure 3. Flowchart of trajectory planning algorithm based on improved AFSA

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图 4 水平面规划航迹

Figure 4. Horizontal trajectory planning

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图 5 三维空间规划航迹

Figure 5. 3D space trajectory planning

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图 6 搜索算法性能对比

Figure 6. Performance comparison of search algorithms

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表 1 无人直升机与目标信息

Table 1. UH and target information

指令到达时间/s	无人直升机	x/km	y/km		目标	x/km	y/km
	UH1	0	30	→	T1	1 050	1 020
7 800	UH2	30	0	→	T2	1 020	1 050
	UH3	0	0	→	T3	1 050	1 050

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表 2 威胁参数设置

Table 2. Threat parameter setting

威胁编号	类型	(x, y)/km
1	气象	(140, 140)
2	地空导弹	(240, 180)
3	雷达	(310, 270)
4	气象	(350, 480)
5	地空导弹	(500, 480)
6	地空导弹	(600, 680)
7	雷达	(710, 770)

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表 3 无人直升机实际到达时间

Table 3. Actual arrival time of UH

无人直升机	实际到达时间/s	与指令到达时间差值/s
UH1	7 784.6	-16.4
UH2	7 818.83	18.83
UH3	7 769.47	-30.53

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