基于空海异构无人平台的水下目标搜索与跟踪

丁文俊; 柴亚军; 杨宇贤; 刘佳敏; 毛昭勇

doi:10.11993/j.issn.2096-3920.2024-0037

基于空海异构无人平台的水下目标搜索与跟踪

doi: 10.11993/j.issn.2096-3920.2024-0037

丁文俊^{1, 2, 3, 4,},
柴亚军^1,,
杨宇贤¹,
刘佳敏¹,
毛昭勇^{1, 3, 4}

1.
西北工业大学无人系统技术研究院, 陕西西安, 710072
2.
浙江西安交通大学研究院, 浙江杭州, 311200
3.
无人飞行器技术全国重点实验室, 陕西西安, 710072
4.
无人机技术集成攻关大平台, 陕西西安, 710072

基金项目: 国家自然科学基金项目(51909206); 中国博士后科学基金项目(2021M692616); 陕西省自然科学基础研究计划项目(2024JC-YBMS-300); 中央高校基本科研业务费专项资金项目(31020200QD044)

详细信息

作者简介:
丁文俊(1989-), 男, 博士, 副教授, 主要研究方向为水下无人系统、跨域协同及智能决策

中图分类号: TJ630.32; U674
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- 文章访问数: 923
- HTML全文浏览量: 55
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-10
- 修回日期: 2024-04-01
- 网络出版日期: 2024-05-06

Underwater Target Search and Tracking Based on Air-Sea Heterogeneous Unmanned Platform

DING Wenjun^{1, 2, 3, 4
,},
CHAI Yajun^1
,,
YANG Yuxian¹,
LIU Jiamin¹,
MAO Zhaoyong^{1, 3, 4}

1.
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
2.
Research Institute of Xi’an Jiaotong University, Hangzhou 311200, China
3.
National Key Laboratory of Unmanned Aerial Vehicle Technology, Xi’an 710072, China
4.
Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Xi’an 710072, China

摘要

摘要: 海上异构无人系统可有效提高复杂任务的完成效率。文中采用自主水下航行器(AUV)和无人机(UAV)来完成近海海域内未知水下目标的搜索与跟踪任务。首先, 描述了水下目标搜索跟踪任务, 将任务过程分为目标搜索和目标跟踪阶段, 2个阶段的目标分别是使AUV&UAV总搜索空间最大化以及AUV与水下目标的末端位置误差最小; 然后, 建立AUV&UAV跨域协同搜索模型, 并设定模型中AUV和UAV探测范围和通信距离等约束条件; 最后, 在跨域协同搜索与路径跟踪规划中, 基于传统粒子群算法, 加入自适应学习因子调控策略和精英保存策略, 生成搜索与跟踪路径。仿真实验表明, 采用改进粒子群优化算法的AUV&UAV异构无人系统能够更高效地完成水下目标搜索与跟踪任务。
- 跨域无人系统 /
- 自主水下航行器 /
- 无人机 /
- 改进粒子群优化算法
Abstract: The marine heterogeneous unmanned systems can effectively enhance the implementation efficiency of complex missions. In this paper, autonomous undersea vehicles(AUVs) and unmanned aerial vehicles(UAVs) were used for searching and tracking unknown underwater targets in offshore waters. First, the underwater target search and tracking mission was described, and the mission was divided into two stages: target search and target tracking, with the objectives of maximizing the total search space of the AUV&UAV system and minimizing the end position error between the AUV and the underwater target, respectively. Then, a cross-domain collaborative search model of the AUV&UAV system was established, and constraints such as detection range and communication distance for AUVs and UAVs in the model were set. Finally, based on the traditional particle swarm optimization algorithm, an adaptive learning factor regulation strategy and an elite preservation strategy were employed for cross-domain collaborative search and tracking path planning, and search and tracking paths were generated. The simulation experiment demonstrates that the heterogeneous AUV&UAV system based on an improved particle swarm optimization algorithm can more efficiently search and track underwater targets.
- cross-domain unmanned system /
- autonomous undersea vehicle /
- unmanned aerial vehicle /
- improved particle swarm optimization algorithm

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图 1 自由地图模型示意图

Figure 1. Diagram of free map model

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图 2 UAV与AUV模型示意图

Figure 2. Model of UAV and AUV

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图 3 IPSO算法流程图

Figure 3. IPSO algorithm flow chart

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图 4 基于PSO算法的任务全过程二维路径曲线

Figure 4. The two-dimensional path map of the whole process of a task based on PSO algorithm

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图 5 基于PSO算法的任务全过程三维路径图

Figure 5. 3D path map of the whole process of the task based on PSO algorithm

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图 6 基于PSO算法的全过程无人平台间距变化曲线

Figure 6. The whole process of unmanned platform spacing variation curve based on PSO algorithm

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图 7 基于IPSO算法的任务全过程的二维路径图

Figure 7. The two-dimensional path map of the whole process of a task based on IPSO algorithm

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图 8 基于IPSO算法的任务全过程三维路径图

Figure 8. 3D path map of the whole process of the task based on IPSO algorithm

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图 9 基于IPSO算法的全过程无人平台间距变化曲线

Figure 9. The whole process of unmanned platform spacing variation curves based on IPSO algorithm

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表 1 UAV、AUV及二维定深运动目标的初始状态信息

Table 1. Initial state information of UAV, AUV and two-dimensional depth-fixed moving target

运动物体	坐标/m			速度 /(m/s)	姿态角/rad
运动物体	x	y	z	速度 /(m/s)	$\varphi $	$\theta $
UAV	200	0	50	15	π/6	−
AUV	0	50	−50	8	0	0
目标	1 000	1 000	−50	5	0	0

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表 2 基于PSO算法任务的相关参数

Table 2. Other data of task based on PSO algorithm

参数	数值
T_U/s	41.00
T_A/s	241.00
T_A-T/s	400.00
L_A-T/m	1.86

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表 3 基于IPSO算法任务的相关参数

Table 3. Other data of UAV & AUV heterogeneous system task based on IPSO algorithm

参数	数值
T_U/s	35.00
T_A/s	180.00
T_A-T/s	344.00
L_A-T/m	0.69

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表 4 PSO算法和IPSO算法仿真结果

Table 4. Some experimental results of PSO algorithm

参数	PSO仿真结果					平均值
T_U/s	41	40	37	54	32	40.8
T_A/s	241	227	244	261	217	238
T_A-T/s	400	366	384	416	341	381.4
L_A-T/m	1.86	2.68	2.23	1.56	1.72	2.01

参数	IPSO仿真结果					平均值
T_U/s	35	30	25	30	42	34.4
T_A/s	180	217	203	175	210	197
T_A-T/s	344	347	253	230	361	307
L_A-T/m	1.69	1.83	0.83	1.53	1.62	1.5

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