一种基于目标声信息改进概率图的多UUV协同搜索方法

杨惠珍; 周卓彧; 李源

doi:10.11993/j.issn.2096-3920.2022-0036

一种基于目标声信息改进概率图的多UUV协同搜索方法

doi: 10.11993/j.issn.2096-3920.2022-0036

杨惠珍^{1, 2},
周卓彧^{1, 2},
李源^{1, 2}

1.
西北工业大学航海学院, 陕西西安, 710072
2.
水下信息与控制全国重点实验室, 陕西西安, 710072

基金项目: 水下信息与控制全国重点实验室基金项目资助(2021-JCJQ-LB-030-03).

详细信息

作者简介:
杨惠珍(1974-), 女, 博士, 副教授, 主要研究方向为水下机器人控制与仿真、多机器人系统

中图分类号: TJ630.1; U666
计量
- 文章访问数: 65
- HTML全文浏览量: 6
- PDF下载量: 31
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-02
- 修回日期: 2022-09-10
- 录用日期: 2022-09-26
- 网络出版日期: 2023-09-25

A Cooperative Search Algorithm Based on Improved Probability Map of Target Acoustic Information for Multiple UUVs

YANG Huizhen^{1, 2},
ZHOU Zhuoyu^{1, 2},
LI Yuan^{1, 2}

1.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2.
Underwater Information and Control Laboratory, Xi’an 710072, China

摘要

摘要: 针对未知环境中多无人水下航行器(UUV)协同目标搜索问题, 提出一种基于目标声信息改进概率图的多UUV协同搜索方法。建立了包含目标声场信息、UUV占用信息、目标存在概率及环境确定度的改进概率图, 使UUV对动态搜索环境及目标信息的感知更加准确、全面; 提出了一种基于学习机制和自适应参数调节机制的改进粒子群优化(PSO)算法, 将基于线性种群规模减小和广泛学习机制的自适应差分进化算法的突变策略引入PSO算法, 通过生成自适应调整参数的突变粒子, 增加粒子多样性, 在多UUV目标搜索应用中, 减少了局部最优, 提高搜索效率; 设计开发了仿真程序, 应用蒙特卡洛仿真方法验证分析了多UUV搜索效能。仿真结果表明, 所提出的多UUV协同搜索方法与基于传统概率图的PSO搜索算法相比, 同样条件下找到目标所花费的时间更少、找到的目标数量更多, 对动态目标搜索具有较明显的优势。
- 多无人水下航行器 /
- 目标搜索 /
- 概率图 /
- 未知环境
Abstract: In view of the cooperative target search of multiple unmanned undersea vehicles (UUVs) in unknown environments, a cooperative search algorithm based on an improved probability map of target acoustic information for multiple UUVs was proposed. An improved probability map based on target acoustic information, UUV occupancy information, target existence probability, and environment certainty was established, making the UUV’s perception of dynamic search environment and target information more accurate and comprehensive. In addition, an improved particle swarm optimization(PSO) algorithm based on a learning mechanism and adaptive parameter adjustment mechanism was put forward, which introduced the mutation strategy of adaptive differential evolution algorithm based on linear population size reduction and extensive learning mechanism into the PSO algorithm. By generating mutated particles with adaptive adjustment parameters and increasing particle diversity, local optimality was reduced, and search efficiency was improved in multi-UUV target search applications. The simulation program was developed, and the Monte Carlo method was employed to analyze the multi-UUV search efficiency. Simulation results show that compared with the PSO search algorithm based on traditional probability maps, the proposed multi-UUV collaborative search method takes less time and finds more targets under the same conditions, so it has obvious advantages in dynamic target search.
- multiple unmanned undersea vehicles /
- target search /
- probability map /
- uncertain environment

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图 1 UUV水平面运动坐标系

Figure 1. Motion coordinate system of UUV

下载: 全尺寸图片幻灯片

图 2 PSO-LSHADE-CLM算法流程框图

Figure 2. Flow chart of PSO-LSHADE-CLM algorithm

下载: 全尺寸图片幻灯片

图 3 多UUV系统目标搜索仿真界面

Figure 3. Simulation interface of multi-UUV target search

下载: 全尺寸图片幻灯片

图 4 TPM搜索轨迹(第800步时)

Figure 4. Search trajectories of TPM at step 800

下载: 全尺寸图片幻灯片

图 5 IPM搜索轨迹(第800步时)

Figure 5. Search trajectories of IPM at step 800

下载: 全尺寸图片幻灯片

图 6 PSO搜索轨迹(第600步时)

Figure 6. Search trajectories of PSO at step 600

下载: 全尺寸图片幻灯片

图 7 PSO-LPSR-SHADE-CLM搜索轨迹(第600步时)

Figure 7. Search trajectories of PSO-LPSR-SHADE-CLMat step 600

下载: 全尺寸图片幻灯片

表 1 UUV初始状态及参数表

Table 1. Initial states and parameters of UUVs

$ {\rm{UUV}} $	初始位置/km	航行速度/kn	最大转向角/(°)	避碰距离/m
$ {{\rm{UUV}}_1} $	$ (0.5,0.5) $	4	45	100
$ {{\rm{UUV}}_{\text{2}}} $	$ (0.5,19.5) $
$ {{\rm{UUV}}_{\text{3}}} $	$ (19.5,0.5) $
$ {{\rm{UUV}}_{\text{4}}} $	$ (19.5,19.5) $

下载: 导出CSV

表 2 声呐参数表

Table 2. Parameters of sonar

参数	探测距离/km	扇面角 /(°)	检测概率	虚警概率
数值	0.65	120	0.95	0.95

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表 3 PSO-LSHADE-CLM算法参数表

Table 3. Parameters of PSO-LSHADE-CLM algorithm

粒子数目	$ {G_{\max }} $	$ {c_1} $	$ {c_2} $	$ w1 $	$ w2 $	$ w3 $
100	100	2	2	0.375	0.125	0.5

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表 4 不同组合算法搜索到的目标数量

Table 4. Number of searched targets by different combination algorithms

移动目标数量	4 UUVs			6 UUVs
移动目标数量	IPM+PSO-LPSR- SHADE-CLM	IPM+PSO	TPM+PSO-LPSR- SHADE-CLM	IPM+PSO-LPSR- SHADE-CLM	IPM+PSO	IPM+PSO-LPSR- SHADE-CLM
2	1.85	1.4	1.2	1.87	1.6	1.4
4	3.60	3.3	3.1	3.80	3.6	3.3
6	5.40	4.9	4.7	5.70	5.2	4.9
合计	10.85	9.6	9	11.37	10.4	9.6

下载: 导出CSV

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