基于USBL的USV-AUV协同多目标路径规划

米彦龙; 杨惠珍; 郭天阳

doi:10.11993/j.issn.2096-3920.2025-0113

基于USBL的USV-AUV协同多目标路径规划

doi: 10.11993/j.issn.2096-3920.2025-0113

米彦龙^{1, 2,},
杨惠珍^{1, 2, ,},
郭天阳^{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(2024-CXPT-GF-JJ-036-06).

详细信息

作者简介:
米彦龙(2000-), 男, 在读硕士, 主要研究方向为水下航行器路径规划

通讯作者:
杨惠珍(1974-), 女, 博士, 研究员, 主要研究方向为水下航行器控制与仿真.

中图分类号: TJ630; U663
计量
- 文章访问数: 162
- HTML全文浏览量: 65
- PDF下载量: 69
- 被引次数: 0
出版历程
- 收稿日期: 2025-08-25
- 修回日期: 2025-09-14
- 录用日期: 2025-09-26
- 网络出版日期: 2026-01-19

Multi-Objective Collaborative Path Planning for USV-AUV Based on USBL

MI Yanlong^{1, 2
,},
YANG Huizhen^{1, 2
, ,},
GUO Tianyang^{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: 面向无人水面艇(USV)辅助多自主水下航行器(AUV)作业应用背景, 针对水下通信易受干扰、有效距离有限及协同稳定性不足等问题, 提出一种基于超短基线 (USBL)定位系统的USV-AUV多目标协同路径规划方法。通过分析USBL工作原理与海洋水声信号传播特性, 融合USBL信号有效区、射线声学理论定义的声线传播边界及声呐方程计算的最大作用距离, 共同构建协同作业的稳定通信范围。在确保USV与AUV处于水声通信有效范围的前提下, 进一步优化路径长度、路径平滑度和USV-AUV的通信性能, 建立了USV-AUV协同路径规划的多目标优化模型, 采用融合遗传算法(GA)、粒子群优化(PSO)和教学优化(TLBO)算法的协同优化策略求解。仿真实验探究了通信距离、AUV作业深度等参数对USV规划路径影响, 结果表明, 所提方法在满足USBL通信约束的同时, 能够有效提升USV与AUV协同工作的稳定性和效率, 为多AUV执行复杂海洋任务提供可靠保障。
- 无人水面艇 /
- 自主水下航行器 /
- 超短基线定位系统 /
- 协同路径规划 /
- 多目标优化 /
- 水声通信 /
- GA-PSO-TLBO算法
Abstract: To address the problems of high susceptibility to interference, limited effective range, and insufficient collaborative stability in underwater communication within the context of unmanned surface vehicle (USV)-assisted multi-autonomous undersea vehicle(AUV) operations, this paper proposed a multi-objective collaborative path planning method for USV-AUV based on an ultra-short baseline(USBL) positioning system. By analyzing the working principle of USBL and the propagation characteristics of marine underwater acoustic signals, this paper constructed a stable communication range for collaborative operations by integrating the effective zone of USBL signals, the acoustic ray propagation boundary defined by ray acoustics theory, and the maximum operating distance calculated by the sonar equation. With the prerequisite of ensuring that the USV and AUVs are within the effective range of underwater acoustic communication, the paper established a multi-objective optimization model for USV-AUV collaborative path planning to further optimize path length, path smoothness, and USV-AUV communication performance. An improved collaborative optimization strategy that integrates the genetic algorithm(GA), particle swarm optimization(PSO) and teaching-learning-based optimization(TLBO) algorithm was employed for solution. Simulation experiments investigate the influence of parameters, such as communication distance and AUV operating depth, on the USV planned path. The results indicate that the proposed method can effectively enhance the stability and efficiency of collaborative operations between the USV and AUVs while satisfying USBL communication constraints, providing reliable support for multiple AUVs in executing complex marine missions.
- unmanned surface vessel /
- autonomous undersea vehicle /
- ultra-short baseline positioning system /
- collaborative path planning /
- multi-objective optimization /
- underwater acoustic communication /
- GA-PSO-TLBO algorithm

HTML全文

图 1 USV辅助多AUV工作场景示意图

Figure 1. Schematic diagram of USV assisting multi-AUV operation scenario

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图 2 USBL定位原理图

Figure 2. Schematic diagram of USBL positioning principle

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图 3 USV-AUV通信盲区相对位置图

Figure 3. Relative position of USV-AUV communication blind zones

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图 4 USV稳定通信区域二维平面示意图

Figure 4. 2D plane schematic of USV stable communication area

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图 5 GA-PSO-TLBO算法流程图

Figure 5. Flow chart of GA-PSO-TLBO algorithm

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图 6 不同算法在仿真场景1中的规划结果

Figure 6. Planning results of different algorithms in simulation scenario 1

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图 7 不同算法在仿真场景2中的规划结果

Figure 7. Planning results of different algorithms in simulation scenario 2

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图 8 权重系数变化对路径规划效果的影响

Figure 8. The impact of changes in weight coefficients on the effectiveness of path planning

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表 1 不同高度差下的路径规划结果

Table 1. Path planning results under different height differences

USV-AUV 高度差/m	GA			GA-PSO-TLBO
USV-AUV 高度差/m	路径长度/m	通信性能评估/%	USV 最大转角/(°)	路径长度/m	通信性能评估/%	USV 最大转角/(°)
100	1052	97.33	40.16	1052	97.33	40.40
150	1279	96.00	75.37	1130	96.67	75.20
200	1280	95.34	92.56	1203	96.33	66.07
250	1311	95.33	93.81	1211	95.50	103.60
300	1562	94.10	107.84	1431	94.40	103.53

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表 2 不同稳定通信距离下的路径规划结果

Table 2. Path planning results under different stable communication distances

稳定通信距离/m	GA			GA-PSO-TLBO
稳定通信距离/m	路径长度/m	通信性能评估/%	USV 最大转角/(°)	路径长度/m	通信性能评估/%	USV 最大转角/(°)
100	1050	79.74	40.24	1050	79.74	40.70
150	1280	82.98	72.79	1170	84.50	72.77
200	1280	89.29	93.44	1250	90.33	84.35
250	1310	93.13	90.98	1200	95.50	100.80
300	1660	94.99	111.10	1474	97.00	106.77

下载: 导出CSV

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