Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/12/697 |
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| author | Yiyang Li Kai Sun Zekai Han Jichao Lang |
| author_facet | Yiyang Li Kai Sun Zekai Han Jichao Lang |
| author_sort | Yiyang Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e593db1784004b3ea6bb63f2be06f23e |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e593db1784004b3ea6bb63f2be06f23e2024-12-27T14:21:42ZengMDPI AGDrones2504-446X2024-11-0181269710.3390/drones8120697Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical BeaconYiyang Li0Kai Sun1Zekai Han2Jichao Lang3State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaVisual 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.https://www.mdpi.com/2504-446X/8/12/697underwater optical beacondocking technologypose detectiondeep learningunderwater localization |
| spellingShingle | Yiyang Li Kai Sun Zekai Han Jichao Lang Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon Drones underwater optical beacon docking technology pose detection deep learning underwater localization |
| title | Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon |
| title_full | Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon |
| title_fullStr | Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon |
| title_full_unstemmed | Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon |
| title_short | Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon |
| title_sort | deep learning based docking scheme for autonomous underwater vehicles with an omnidirectional rotating optical beacon |
| topic | underwater optical beacon docking technology pose detection deep learning underwater localization |
| url | https://www.mdpi.com/2504-446X/8/12/697 |
| work_keys_str_mv | AT yiyangli deeplearningbaseddockingschemeforautonomousunderwatervehicleswithanomnidirectionalrotatingopticalbeacon AT kaisun deeplearningbaseddockingschemeforautonomousunderwatervehicleswithanomnidirectionalrotatingopticalbeacon AT zekaihan deeplearningbaseddockingschemeforautonomousunderwatervehicleswithanomnidirectionalrotatingopticalbeacon AT jichaolang deeplearningbaseddockingschemeforautonomousunderwatervehicleswithanomnidirectionalrotatingopticalbeacon |