Fusion guiding technology solution and algorithm for underwater docking of autonomous underwater vehicles

ZHAO Rui; XU Jian

doi:10.19693/j.issn.1673-3185.02318

Volume 17 Issue 1

Mar. 2022

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Article Navigation > Chinese Journal of Ship Research > 2022 > 17(1): 212-220

ZHAO R, XU J. Fusion guiding technology solution and algorithm for underwater docking of autonomous underwater vehicles[J]. Chinese Journal of Ship Research, 2022, 17(1): 212–220 doi: 10.19693/j.issn.1673-3185.02318

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Fusion guiding technology solution and algorithm for underwater docking of autonomous underwater vehicles

doi: 10.19693/j.issn.1673-3185.02318

ZHAO Rui,
XU Jian

China Ship Development and Design Center, Wuhan 430064, China

Received Date: 2021-03-17
Rev Recd Date: 2021-04-02

Available Online: 2022-02-23

Publish Date: 2022-03-02

Abstract

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective To address the problem of underwater autonomous vehicles (AUVs) docking with parent vessels in recovery operations, a mobile platform-oriented AUV underwater guiding solution is proposed on the basis of multi-sources information fusion, including inertial, acoustic and optical signals. Methods A federated extended Kalman filter integrating multi-sensors information is designed to improve filtering accuracy through decentralized filtering and information fusion. Motion equations are also established by combining the five AUV docking stages, in which the signals detected by the inertial navigation system (INS), acoustic ultra-short baseline (USBL) and optical guiding system are applied separately as inputs of sub-filters, resulting in a fusion guiding algorithm adapted to AUV underwater docking systems. Results Simulation experiments demonstrate that the guiding process based on multi-source information fusion is feasible and possesses robust performance and adequate control and steering accuracy. Conclusions The proposed fusion guiding solution meets the engineering requirements of underwater docking operations. The results of this study can provide technical references for the underwater docking of AUVs.
- autonomous underwater vehicle,
- underwater docking,
- multi-sources information fusion algorithm,
- acoustic localization,
- optical localization

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References

[1]	封锡盛. 从有缆遥控水下机器人到自治水下机器人[J]. 中国工程科学, 2000, 2(12): 29–33,58. doi: 10.3969/j.issn.1009-1742.2000.12.005 FENG X S. From remotely operated vehicles to autonomous undersea vehicles[J]. Engineering Science, 2000, 2(12): 29–33,58 (in Chinese). doi: 10.3969/j.issn.1009-1742.2000.12.005
[2]	LEE P M, JEON B H, KIM S M. Visual servoing for underwater docking of an autonomous underwater vehicle with one camera[C]//Oceans 2003. Celebrating the Past. Teaming Toward the Future (IEEE Cat. No. 03CH37492). San Diego, CA, USA: IEEE, 2003, 2: 677-682.
[3]	赵蕊, 许建, 王淼, 等. 基于遗传算法和分数阶技术的水下机器人航向控制[J]. 中国舰船研究, 2018, 13(6): 87–93. doi: 10.19693/j.issn.1673-3185.01185 ZHAO R, XU J, WANG M, et al. Heading control of AUV based on GA and fractional order technology[J]. Chinese Journal of Ship Research, 2018, 13(6): 87–93 (in Chinese). doi: 10.19693/j.issn.1673-3185.01185
[4]	孙叶义, 武皓微, 李晔, 等. 智能无人水下航行器水下回收对接技术综述[J]. 哈尔滨工程大学学报, 2019, 40(1): 1–11. SUN Y Y, WU H W, LI Y, et al. Summary of AUV underwater recycle docking technology[J]. Journal of Harbin Engineering University, 2019, 40(1): 1–11 (in Chinese).
[5]	WIRTZ M, HILDEBRANDT M, GAUDIG C. Design and test of a robust docking system for hovering AUVs[C]//2012 Oceans. Hampton Roads, VA, USA: IEEE, 2012.
[6]	KAWASAKI T, FUKASAWA T, NOGUCHI T, et al. Development of AUV “Marine Bird” with underwater docking and recharging system[C]//2003 International Conference Physics and Control. Proceedings (Cat. No. 03EX708). Tokyo, Japan: IEEE, 2003: 166-170.
[7]	SINGH H, BELLINGHAM J G, HOVER F, et al. Docking for an autonomous ocean sampling network[J]. IEEE Journal of Oceanic Engineering, 2001, 26(4): 489–514.
[8]	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 (in Chinese). doi: 10.19693/j.issn.1673-3185.01182
[9]	燕奎臣, 吴利红. AUV水下对接关键技术研究[J]. 机器人, 2007, 29(3): 267–273. doi: 10.3321/j.issn:1002-0446.2007.03.014 YAN K C, WU L H. A survey on the key technologies for underwater AUV docking[J]. Robot, 2007, 29(3): 267–273 (in Chinese). doi: 10.3321/j.issn:1002-0446.2007.03.014
[10]	闫鹏. 水下机器人回收对接水动力仿真[D]. 大连: 大连海事大学, 2013. YAN P. Hydrodynamic simulation of autonomous underwater vehicle in docking reclaim [D]. Dalian: Dalian Maritime University, 2013 (in Chinese).
[11]	SATO Y, MAKI T, MATSUDA T, et al. Detailed 3D seafloor imaging of Kagoshima bay by AUV Tri-TON2[C]//2015 IEEE Underwater Technology (UT). Chennai, India: IEEE, 2015: 1-6.
[12]	张波. 自治式水下机器人水下对接装置研究[D]. 哈尔滨: 哈尔滨工程大学, 2013. ZHANG B. Research on AUV underwater docking device[D]. Harbin: Harbin Engineering University, 2013 (in Chinese).
[13]	SATO Y, MAKI T, MASUDA K, et al. Autonomous docking of hovering type AUV to seafloor charging station based on acoustic and visual sensing[C]//2017 Underwater Technology (UT). Busan, Korea (South): IEEE, 2017: 1-6.
[14]	赵蕊, 许建, 向先波, 等. 多自主式水下机器人的路径规划和控制技术研究综述[J]. 中国舰船研究, 2018, 13(6): 58–65. doi: 10.19693/j.issn.1673-3185.01028 ZHAO R, XU J, XIANG X B, et al. A review of path planning and cooperative control for MAUV systems[J]. Chinese Journal of Ship Research, 2018, 13(6): 58–65 (in Chinese). doi: 10.19693/j.issn.1673-3185.01028
[15]	JUN B H, PARK J Y, LEE F Y, et al. Development of the AUV ‘ISiMI’ and a free running test in an ocean engineering basin[J]. Ocean Engineering, 2009, 36(1): 2–14. doi: 10.1016/j.oceaneng.2008.07.009
[16]	PARK J Y, JUN B H, LEE P M, et al. Experiments on vision guided docking of an autonomous underwater vehicle using one camera[J]. Ocean Engineering, 2009, 36(1): 48–61. doi: 10.1016/j.oceaneng.2008.10.001
[17]	PARK J Y, JUN B H, LEE P M, et al. Modified linear terminal guidance for docking and a time-varying ocean current observer[C]//2011 IEEE Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies. Tokyo, Japan: IEEE, 2011: 1-6.
[18]	WU L H, LI Y P, SU S J, et al. Hydrodynamic analysis of AUV underwater docking with a cone-shaped dock under ocean currents[J]. Ocean Engineering, 2014, 85: 110–126. doi: 10.1016/j.oceaneng.2014.04.022
[19]	刘准, 陈哲. INS/GPS/TRCOM组合制导系统中的信息融合方法研究[J]. 宇航学报, 2001, 22(3): 26–32. doi: 10.3321/j.issn:1000-1328.2001.03.006 LIU Z, CHEN Z. Research on information fusion method in INS/GPS/TRCOM system[J]. Journal of Astronautics, 2001, 22(3): 26–32 (in Chinese). doi: 10.3321/j.issn:1000-1328.2001.03.006
[20]	卢海曦, 周百令. 自适应联邦滤波器及其在组合导航系统中的应用[J]. 中国惯性技术学报, 2007, 15(6): 678–681. doi: 10.3969/j.issn.1005-6734.2007.06.010 LU H X, ZHOU B L. Self-adaptive federated filter and its application in integrated navigation system[J]. Journal of Chinese Inertial Technology, 2007, 15(6): 678–681 (in Chinese). doi: 10.3969/j.issn.1005-6734.2007.06.010
[21]	邱恺, 吴训忠, 张宗麟, 等. 联邦滤波器容错信息分配方法研究[J]. 控制与决策, 2006, 21(3): 343–346,360. doi: 10.3321/j.issn:1001-0920.2006.03.024 QIU K, WU X Z, ZHANG Z L, et al. On fault-tolerant information sharing scheme for federated filters[J]. Control and Decision, 2006, 21(3): 343–346,360 (in Chinese). doi: 10.3321/j.issn:1001-0920.2006.03.024
[22]	胡志强, 张英, 邱恺. 基于故障概率的联邦滤波鲁棒信息分配方法[J]. 系统工程与电子技术, 2008, 30(9): 1801–1804. doi: 10.3321/j.issn:1001-506X.2008.09.048 HU Z Q, ZHANG Y, QIU K. Robust information sharing scheme for federated filter based on fault probability[J]. Systems Engineering and Electronics, 2008, 30(9): 1801–1804 (in Chinese). doi: 10.3321/j.issn:1001-506X.2008.09.048
[23]	FELTER S C, WU N E. A relative navigation system for formation flight[J]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(3): 958–967. doi: 10.1109/7.599319
[24]	陈天如, 邱恺, 潘泉. 无反馈最优联邦信息滤波算法研究[J]. 传感技术学报, 2007, 20(5): 1064-1067. CHEN T R, QIU K, PAN Q. Optimal information sharing algorithm for the federated information filter without feedback [J]. Chinese Journal of Sensors and Actuators, 2007, 20(5): 1064-1067 (in Chinese).
[25]	李金梁, 吴训忠, 张宗麟. 基于矩阵摄动理论的联邦滤波信息分配方法[J]. 系统工程与电子技术, 2007, 29(11): 1940-1944. LI J L, WU X Z, ZHANG Z L. Information-sharing approach to federated filtering based on matrix perturbation theory [J]. Systems Engineering and Electronics, 2007, 29(11): 1940-1944 (in Chinese).
[26]	陈兵舫, 张育林, 赵华丽. INS/GPS/Odometer组合系统初始对准及自适应联合滤波[J]. 宇航学报, 2001, 22(6): 57–63,83. doi: 10.3321/j.issn:1000-1328.2001.06.009 CHEN B F, ZHANG Y L, ZHAO L H. Initial alignment of INS/GPS/Odometer integrated system and adaptive federated Kalman filter[J]. Journal of Astronautics, 2001, 22(6): 57–63,83 (in Chinese). doi: 10.3321/j.issn:1000-1328.2001.06.009
[27]	李丹, 刘建业, 熊智, 等. 应用联邦自适应UKF的卫星多传感器数据融合[J]. 应用科学学报, 2009, 27(4): 359–364. doi: 10.3969/j.issn.0255-8297.2009.04.006 LI D, LIU J Y, XIONG Z, et al. Multi-sensor data fusion for satellite based on federate adaptive unscented Kalman filter[J]. Journal of Applied Sciences, 2009, 27(4): 359–364 (in Chinese). doi: 10.3969/j.issn.0255-8297.2009.04.006
[28]	赖际舟, 周翟和, 刘建业, 等. 混合粒子联邦滤波在多信息组合导航系统中的应用[J]. 中国惯性技术学报, 2011, 19(1): 59–63. LAI J Z, ZHOU Z H, LIU J Y, et al. Mixture particle federated filter and its application in multi-sensor integrated navigation system[J]. Journal of Chinese Inertial Technology, 2011, 19(1): 59–63 (in Chinese).
[29]	张梦辉. 基于机器视觉的自主式水下航行器末端导引系统关键技术研究[D]. 杭州: 浙江大学, 2018. ZHANG M H. Research on key technologies of machine vision-based AUV terminal guidance system [D]. Hangzhou: Zhejiang University, 2018 (in Chinese).