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Article

Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon

1
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Drones 2024, 8(12), 697; https://doi.org/10.3390/drones8120697
Submission received: 15 October 2024 / Revised: 5 November 2024 / Accepted: 19 November 2024 / Published: 21 November 2024

Abstract

Visual recognition and localization of underwater optical beacons are critical for AUV docking, but traditional beacons are limited by fixed directionality and light attenuation in water. To extend the range of optical docking, this study designs a novel omnidirectional rotating optical beacon that provides 360-degree light coverage over 45 m, improving beacon detection probability through synchronized scanning. Addressing the challenges of light centroid detection, we introduce a parallel deep learning detection algorithm based on an improved YOLOv8-pose model. Initially, an underwater optical beacon dataset encompassing various light patterns was constructed. Subsequently, the network was optimized by incorporating a small detection head, implementing dynamic convolution and receptive-field attention convolution for single-stage multi-scale localization. A post-processing method based on keypoint joint IoU matching was proposed to filter redundant detections. The algorithm achieved 93.9% AP at 36.5 FPS, with at least a 5.8% increase in detection accuracy over existing methods. Moreover, a light-source-based measurement method was developed to accurately detect the beacon’s orientation. Experimental results indicate that this scheme can achieve high-precision omnidirectional guidance with azimuth and pose estimation errors of -4.54° and 3.09°, respectively, providing a reliable solution for long-range and large-scale optical docking.
Keywords: underwater optical beacon; docking technology; pose detection; deep learning; underwater localization underwater optical beacon; docking technology; pose detection; deep learning; underwater localization

Share and Cite

MDPI and ACS Style

Li, Y.; Sun, K.; Han, Z.; Lang, J. Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon. Drones 2024, 8, 697. https://doi.org/10.3390/drones8120697

AMA Style

Li Y, Sun K, Han Z, Lang J. Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon. Drones. 2024; 8(12):697. https://doi.org/10.3390/drones8120697

Chicago/Turabian Style

Li, Yiyang, Kai Sun, Zekai Han, and Jichao Lang. 2024. "Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon" Drones 8, no. 12: 697. https://doi.org/10.3390/drones8120697

APA Style

Li, Y., Sun, K., Han, Z., & Lang, J. (2024). Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon. Drones, 8(12), 697. https://doi.org/10.3390/drones8120697

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