Monocular visual guidance method for terminal docking of an autonomous underwater vehicle

Qiusheng WANG , Xiangdong QI , Chenyang XUE , Dan LIU , Yonghua WANG , Jingnan WANG

Journal of Measurement Science and Instrumentation ›› 2024, Vol. 15 ›› Issue (2) : 216 -223.

PDF (2455KB)
Journal of Measurement Science and Instrumentation ›› 2024, Vol. 15 ›› Issue (2) :216 -223. DOI: 10.62756/jmsi.1674-8042.2024022
Control theory and technology
research-article

Monocular visual guidance method for terminal docking of an autonomous underwater vehicle

Author information +
History +
PDF (2455KB)

Abstract

Efficient recovery of the autonomous underwater vehicle (AUV) in complex underwater environments is a major technological challenge. A terminal docking method using monocular vision was presented to detect and track a circular light source marker at the docking station. To improve the recognition of the light source feature, the Canny edge detection algorithm was improved, the adaptive threshold method was used to dynamically adjust the light source contour, and the minimum enclosing circle method was used to determine the light source center when the threshold was optimal. In addition, geometric position and area constraints were used to eliminate interference from water surface reflections. For visual localization and tracking, Zhang’s calibration method was used to obtain the internal and distortion parameters of the camera, yaw and pitch errors of the AUV were estimated by comparing the light source center with the camera image center, then the position-attitude PID controller was used to achieve rapid attitude adjustment. The pool experimental results showed that the approach was simple, practical, and robust, providing a technical reference for future reliable recovery of underwater robots.

Keywords

autonomous underwater vehicle (AUV) / terminal docking / monocular visual / position estimation / image processing

Cite this article

Download citation ▾
Qiusheng WANG, Xiangdong QI, Chenyang XUE, Dan LIU, Yonghua WANG, Jingnan WANG. Monocular visual guidance method for terminal docking of an autonomous underwater vehicle. Journal of Measurement Science and Instrumentation, 2024, 15(2): 216-223 DOI:10.62756/jmsi.1674-8042.2024022

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

BOGUE R. Underwater robots: a review of technologies and applications. Industrial Robot, 2015, 42(3): 186-191.

[2]

BOROVIK A I, RYBAKOVA E I, GALKIN S V, et al. Experience of using the autonomous underwater vehicle MMT-3000 for research on benthic communities in antartica. Oceanology, 2022, 62(5): 709-720.

[3]

RUMSON A G. The application of fully unmanned robotic systems for inspection of subsea pipelines. Ocean Engineering, 2021, 235: 109214.

[4]

WIBISONO A, PIRAN M J, SONG H K, et al. A survey on unmanned underwater vehicles: challenges, enabling technologies, and future research directions. Sensors, 2023, 23(17): 7321.

[5]

LIN M W, LIN R, YANG C J, et al. Docking to an underwater suspended charging station: systematic design and experimental tests. Ocean Engineering, 2022, 249: 110766.

[6]

VANDAVASI B N J, VENKATARAMAN H, GIDUGU A R. Machine learning-based electro-magnetic field guided localization technique for autonomous underwater vehicle homing. Ocean Engineering, 2023, 280: 114692.

[7]

CONG Y, GU C, ZHANG T, et al. Underwater robot sensing technology: a survey. Fundamental Research, 2021, 1(3): 337-345.

[8]

YAZDANI A M, SAMMUT K, YAKIMENKO O, et al. A survey of underwater docking guidance systems. Robotics and Autonomous Systems, 2020, 124: 103382.

[9]

SONG L, HOU Y P, ZHANG J P, et al. Monocular visual 3D cuboid measurement method based on Mask-RCNN and SFM. Journal of Measurement Science and Instrumentation, 2023, 14(2): 127-136.

[10]

PARK J Y, JUN B H, LEE P M, et al. Experiments on vision guided docking of an autonomous underwater vehicle using one camera. Ocean Engineering, 2009, 36(1): 48-61.

[11]

BIANCHI FIGUEIREDO A, COIMBRA MATOS A. MViDO: A high performance monocular vision-based system for docking a hovering AUV. Applied Sciences, 2020, 10(9): 2991.

[12]

PALOMERAS N, VALLICROSA G, MALLIOS A, et al. AUV homing and docking for remote operations. Ocean Engineering, 2018, 154: 106-120.

[13]

LI D J, ZHANG T, YANG C. Terminal underwater docking of an autonomous underwater vehicle using one camera and one light. Marine Technology Society Journal, 2016, 50(6): 58-68.

[14]

LIU S, OZAY M, OKATANI T, et al. Detection and pose estimation for short-range vision-based underwater docking. IEEE Access, 2019, 7: 2720-2749.

[15]

ZHAO L J, ZHU R S. Research on image contour edge analysis based on canny edge detector. Academic Journal of Computing & Information Science, 2022, 5(1): 70-75.

[16]

FENG Y, HAN B, WANG X, et al. Self-supervised transformers for unsupervised sar complex interference detection using canny edge detector. Remote Sensing, 2024, 16(2): 306.

[17]

RUHELA R, GUPTA B, LAMBA S S. An efficient approach for texture smoothing by adaptive joint bilateral filtering. The Visual Computer: International Journal of Computer Graphics, 2023, 39(5): 2035-2049.

[18]

LI Q, MENG H, LI Y. Texture-based fast QTMT partition algorithm in VVC intra coding. Signal, Image and Video Processing, 2023, 17(4): 1581-1589.

[19]

FU L H, WANG C Y, HE J J, et al. Camera pose measurement method based on feature matching. Journal of Measurement Science and Instrumentation, 2023, 14(1): 1-8.

[20]

ZHANG Z Y. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.

PDF (2455KB)

47

Accesses

0

Citation

Detail

Sections
Recommended

/