一种基于数据驱动的AUV锋面跟踪算法

鲁杰阳; 文永鹏; 郭倩; 朱心科; 焦君圣

doi:10.11993/j.issn.2096-3920.2024-0151

一种基于数据驱动的AUV锋面跟踪算法

doi: 10.11993/j.issn.2096-3920.2024-0151

鲁杰阳^{1, 3,},
文永鹏²,
郭倩²,
朱心科^3,,
焦君圣¹

1.
中国计量大学计量测试与仪器学院, 浙江杭州, 310018
2.
国电电力浙江舟山海上风电开发有限公司, 浙江舟山, 316100
3.
自然资源部第二海洋研究所, 浙江杭州, 310012

基金项目: 东海实验室预研项目(20230125); 深海技术科学太湖实验室“揭榜挂帅”项目(T7004-2022JBGS04001).

详细信息

作者简介:
鲁杰阳(2000-), 男, 在读硕士, 主要研究方向为自主水下航行器

通讯作者:
朱心科(1980-), 男, 正高级工程师, 主要研究方向为海洋观测.

中图分类号: TJ630; U664.82
计量
- 文章访问数: 36
- HTML全文浏览量: 21
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2024-11-04
- 修回日期: 2025-01-08
- 录用日期: 2025-01-15
- 网络出版日期: 2025-05-26

A Data-Driven Front Tracking Algorithm for Autonomous Undersea Vehicles

1.
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China
2.
Guodian Power Zhejiang Zhoushan Offshore Wind Power Development Co., Ltd, Zhoushan 316100, China
3.
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

摘要

摘要: 针对自主水下航行器(AUV)自适应观测的需求, 设计了一种基于数据驱动的海洋锋面跟踪算法。该算法通过构建基于高斯过程回归(GPR)和粒子群优化(PSO)算法的混合温度场预测模型, 以预采集数据作为先验数据对模型进行训练, 利用PSO算法对核函数中的超参数进行迭代优化, 再将优化后的超参数代入GPR模型, 获得邻近温度场预测结果。计算AUV所在位置与预测区域的温度梯度值, 根据AUV在锋面中的不同位置, 来选择相应的温度梯度跟踪策略, 保持其沿梯度方向运动或是沿等温线跟踪, 实现AUV对锋面的快速跟踪。为了验证算法的有效性, 采用真实锋面数据对该算法进行仿真测试, 结果表明, 该算法相较于其他方法, 在跟踪锋面的准确性和快速性上均有更好的效果, 可以满足AUV高效自主观测的需求。
- 自主水下航行器 /
- 锋面跟踪 /
- 高斯过程回归 /
- 粒子群优化算法
Abstract: To meet the requirement for adaptive observation of autonomous undersea vehicles(AUVs), a data-driven ocean front tracking algorithm was designed. This algorithm constructed a hybrid temperature field prediction model based on Gaussian process regression(GPR) and particle swarm optimization(PSO). Pre-collected data was utilized as prior information to train the model. The PSO algorithm was employed to iteratively optimize the hyperparameters within the kernel function, which were then substituted back into the GPR model to obtain predictions of the adjacent temperature field. By calculating the temperature gradient values between the AUV’s current position and the predicted region, the algorithm selected corresponding temperature gradient tracking strategies based on the AUV’s different positions within the front. This allowed the AUV to maintain motion along the gradient direction or track along isotherms, enabling rapid tracking of the ocean front by the AUV. To validate the effectiveness of the algorithm, simulation tests were conducted using real ocean front data. The results indicate that compared to other methods, this algorithm exhibits superior accuracy and speed in tracking ocean fronts, thereby satisfying the demand for efficient autonomous observation by AUV.
- autonomous undersea vehicle /
- front tracking /
- Gaussian process regression /
- particle swarm optimization algorithm

HTML全文

图 1 PSO-GPR算法伪代码

Figure 1. Pseudo code of PSO-GPR algorithm

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图 2 PSO-GPR算法流程

Figure 2. Flow chart of PSO-GPR algorithm

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图 3 锋面跟踪流程图

Figure 3. Flow chart of front tracking

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图 4 不同预采集区域

Figure 4. Different pre-collection areas

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图 5 温度梯度跟踪示意图

Figure 5. Schematic diagram of temperature gradient tracking

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图 6 路径点选取示意图

Figure 6. Schematic diagram of path pointselection

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图 7 插值后温度场

Figure 7. Temperature field after interpolation

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图 8 预采集范围

Figure 8. Pre-acquisition range

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图 9 不同预测半径下的MSE

Figure 9. MSE under different prediction radii

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图 10 不同等温线跟踪距离下效果图

Figure 10. Effect diagram under different isotherm tracking distances

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图 11 不同算法仿真结果对比

Figure 11. Simulation results comparison of different algorithms

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表 1 性能指标

Table 1. Performance indicators

算法总耗时/h MSE

“之”字形 7.70 —

GPR 24.65 0.0105

PSO-GPR 18.63 0.0037

下载: 导出CSV

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