一种面向AUV动态对接的声学补偿无迹粒子滤波算法

付少波; 关夏威; 王嘉; 张昊

doi:10.19693/j.issn.1673-3185.03366

一种面向AUV动态对接的声学补偿无迹粒子滤波算法

doi: 10.19693/j.issn.1673-3185.03366

武汉第二船舶设计研究所，湖北武汉 430205

基金项目: 湖北省青年拔尖人才资助项目

详细信息

作者简介:
付少波，男，2000年生，硕士生

关夏威，男，1991年生，博士，高级工程师

王嘉，男，1996年生，硕士，助理工程师

张昊，男，1983年生，博士，研究员

通信作者:
张昊

中图分类号: U674.941；P733.23
计量
- 文章访问数: 61
- HTML全文浏览量: 12
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2023-05-17
- 修回日期: 2023-08-20
- 网络出版日期: 2023-11-24

An acoustic compensated unscented particle filter for AUV dynamic docking

Wuhan Second Ship Design and Research Institute, Wuhan 430205, China

一种面向AUV动态对接的声学补偿无迹粒子滤波算法由付少波，等创作，采用知识共享署名4.0国际许可协议进行许可。

摘要

摘要: 目的针对浅水环境下自主水下航行器（AUV）动态对接过程中超短基线定位系统（USBL）声学相对导航问题，提出一种基于扰动观测器的补偿无迹粒子滤波算法（CUPF）。方法该算法依据航位推算模型补偿USBL数据中的缺失值，利用观测器技术估计AUV动态对接过程中未知扰动，结合无迹粒子滤波过滤USBL数据中的野值，并实现AUV状态的预估。结果湖试数据表明，所提方法能够有效地剔除USBL定位中的野值并填补缺失值，速度估计误差不超过15%，计算时间相较于传统算法减少了57%。结论所提方法可充分利用相对量测信息，在平滑AUV运动轨迹的同时提高了USBL定位精度，准确估计了AUV运动状态，有利于AUV顺利对接。
- 动态对接 /
- 超短基线定位系统 /
- 扰动观测器 /
- 补偿无迹粒子滤波
Abstract: Objective To address the problem of ultra-short baseline (USBL) relative navigation and positioning during autonomous underwater vehicles (AUVs) dynamic docking in shallow water, a compensated unscented particle filter (CUPF) based on disturbance observer is proposed. Methods CUPF compensates missing values in USBL data based on dead reckoning model, estimates the unknown disturbances in dynamic docking of AUV with observer technology, filters outliers in USBL data combined with unscented particle filter, and realizes estimation of AUV state. Results The lake trial data shows that the proposed CUPF algorithm can effectively remove outliers and fill in missing values for positioning of USBL, with the estimation error of speed less than 15% and the computation time is reduced by 57% with traditional method. Conclusions The CUPF algorithm can improve the positioning accuracy of USBL while smoothing the motion trajectory and estimating state of AUV by fully utilize relative measurement information for AUV docking.
- dynamic docking /
- ultra-short baseline /
- disturbance observer /
- compensated unscented particle filter (CUPF)

HTML全文

图 1 AUV对接试验系统

Figure 1. The docking trial system of AUV

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图 2 AUV对接试验流程图

Figure 2. The docking process of AUV

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图 3 滤波算法框架

Figure 3. The framework of the CUPEF algorithm

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图 4 北东地坐标系下的AUV和坞站

Figure 4. AUV and docking station in the North-East-Down coordinate system

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图 5 CUPF流程图

Figure 5. Flowchart of CUPF algorithm

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图 6 使用不同方法的定位轨迹

Figure 6. Positioning trajectories of AUV using different methods

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图 7 UPF和CUPF的速度估计误差

Figure 7. The estimated error of speed using UPF and CUPF

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图 8 观测器估计扰动和实际扰动

Figure 8. The estimated disturbance and real disturbance

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图 9 UPF和CUPF的计算时间

Figure 9. Time-consuming of calculation using UPF and CUPF

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参考文献(13)

[1]	PAN W, ZHANG Y. Research on dynamic docking process and collision problems of AUV based on joint control simulation[C]//Proceedings of the 7th International Conference on Computer and Communications (ICCC). Chengdu, China: IEEE, 2021: 1057−1061.
[2]	CHEN G Y, WANG J Q, HU H Y. An integrated GNSS/INS/DR positioning strategy considering nonholonomic constraints for intelligent vehicle[C]//Proceedings of the 6th CAA International Conference on Vehicular Control and Intelligence (CVCI). Nanjing, China: IEEE, 2022: 1−6.
[3]	刘贤俊, 叶鹏, 张同伟, 等. 基于DR/USBL的HOV组合导航算法[J]. 水下无人系统学报, 2022, 30(4): 494–499. doi: 10.11993/j.issn.2096-3920.202105003 LIU X J, YE P, ZHANG T W, et al. DR/USBL integrated navigation algorithm for HOV[J]. Journal of Unmann-ed Undersea Systems, 2022, 30(4): 494–499 (in Chinese). doi: 10.11993/j.issn.2096-3920.202105003
[4]	裘天佑. 基于超短基线定位的AUV对接导航方法研究[D]. 中国科学院大学, 2019.
[5]	CAIATI A, DAVIDE F, BENEDETTO A, et al. Experimental results with a mixed USBL/LBL system for AUV navigation[C]//Proceedings of 2014 Underwater Communications and Networking (UComms). Sestri Levante, Italy: IEEE, 2014: 1−4.
[6]	PHUONG T T, OHISHI K, MITSANTISUK C, et al. Disturbance observer and Kalman filter based motion control realization[J]. IEEJ Journal of Industry Applications, 2018, 7(1): 1–14. doi: 10.1541/ieejjia.7.1
[7]	YAN H F, ZHAO X H, GAO S S, et al. Adaptive robust unscented particle filter and its application in SINS/SAR integration navigation system[C]//Proceedings of the 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Chongqing, China: IEEE, 2017: 2364−2368.
[8]	王磊. 深海AUV多源导航信息融合方法研究[D]. 南京: 东南大学, 2015. WANG Lei. Research on multi-source navigation information fusion method for deep-sea AUV[D]. Nanjing: Southeast University, 2015
[9]	FAN S, LIU C, LI B, et al. AUV docking based on USBL navigation and vision guidance[J]. Journal of Marine Science and Technology, 2019, 24: 673–685. doi: 10.1007/s00773-018-0577-8
[10]	李春雨, 张东升, 张延顺, 等. 一种基于声学定位/航位推算的水下导航定位方法[J]. 船舶工程, 2015, 37(8): 60–63,85. doi: 10.13788/j.cnki.cbgc.2015.08.057 LI C Y, ZHANG D S, ZHANG Y S, et al. Underwater navigation and location method based on acoustic positioning and dead reckoning[J]. Ship Engineering, 2015, 37(8): 60–63,85 (in Chinese). doi: 10.13788/j.cnki.cbgc.2015.08.057
[11]	MITSANTISUK C, OHISHI K, URUSHIHARA S, et al. Kalman filter-based disturbance observer and its applications to sensorless force control[J]. Advanced Robotics, 2011, 25(3–4): 335–353. doi: 10.1163/016918610X552141
[12]	RADKE A, GAO Z. A survey of state and disturbance observers for practitioners[C]//Proceedings of 2006 American Control Conference, Minneapolis, MN, USA : IEEE, 2006: 6−10.
[13]	郑荣, 宋涛, 孙庆刚, 等. 自主式水下机器人水下对接技术综述[J]. 中国舰船研究, 2018, 13(6): 43–49,65. doi: 10.19693/j.issn.1673-3185.01182 ZHENG R, SONG T, SUN Q G, et al. Review on underwater docking technology of AUV[J]. Chinese Journal of Ship Research, 2018, 13(6): 43–49,65. doi: 10.19693/j.issn.1673-3185.01182