摘要
自主水下航行器(AUV)是“进入海洋、探测海洋、利用海洋”的重要工具,AUV水下回收光学导引技术一直以来都是国内外的研究热点。在总结分析国内外AUV水下回收单目视觉、双目视觉、位置探测器导引技术的基础上,重点讨论了各类AUV水下回收光学导引技术的实现原理、方法、发展现状与趋势,详细介绍了基于图像传感器和位置探测器的光学导引技术。未来AUV水下回收光学导引技术将从单一功能模块向智能系统生态演进,为构建涉水智能光电技术与装备体系提供核心技术基础,以满足海洋科考、资源勘探、水下安防等领域的重大应用需求。
Significance Autonomous Underwater Vehicles(AUVs)are pivotal tools for ocean exploration,resource utilization,and environmental monitoring.The underwater docking process,which enables AUVs to physically connect with recovery stations for energy replenishment and data transmission,is critical for enhancing operational efficiency and mission continuity.Traditional guidance methods,such as acoustic and electromagnetic systems,face limitations in precision,robustness,and adaptability to complex underwater environments.Acoustic guidance suffers from low resolution and susceptibility to multipath interference,while electromagnetic signals degrade rapidly in water.Optical guidance,leveraging high-resolution visual or photoelectric detection,has emerged as a promising solution for close-range docking due to its superior accuracy,real-time performance,and stealth advantages.This review highlights advancements in optical guidance technologies,focusing on monocular vision,binocular vision,and position detectors,and outlines their transformative potential in enabling reliable AUV underwater recovery.Progress 1)Monocular vision guidance.Monocular vision systems utilize a single camera to detect active or passive optical markers on docking stations.Active markers,such as LED arrays,offer long visibility ranges but require precise geometric configurations to avoid ambiguity in pose estimation.Passive markers(e.g.,ArUco codes or geometric patterns)provide unique identification but are limited by shorter detection distances.Recent studies have improved robustness through multi-marker fusion and deep learning.For instance,irregularly arranged four-light beacons[Figs.5(a)‒(c)]and hybrid markers combining LEDs with black-and-white codes[Fig.5(d)]enhance feature matching accuracy.Deep learning frameworks like YOLOV5 and CNN-based models[Fig.6(a)]further optimize marker recognition in turbid water.Currently,deep learning-enhanced monocular visual guidance achieves sub-3 cm localization accuracy by combining beacon recognition with PnP algorithms or fusing visual data with multi-sensor inputs,but faces challenges in underwater optical attenuation,high computational demands,and limited real-time pose estimation frequency.2)Binocular vision guidance.Binocular vision systems leverage stereo cameras to resolve depth through disparity analysis[Fig.7(b)].By correlating pixel coordinates of guide lights in dual images,3D coordinates are derived using triangulation.Key advancements include camera calibration and distortion correction.Traditional calibration methods(e.g.,Zhang’s checkerboard approach)ensure sub-pixelaccuracy,while neural networks address nonlinear distortions caused by underwater optical windows[Fig.7(a)].Binocular vision guidance enhances beacon recognition range and accuracy,enabling AUVs to achieve sub-centimeterlocalization precision(~10 mm error)and 30-meter docking range,though its computational speed(milliseconds to hundreds of milliseconds per cycle)requires further optimization despite low hardware demands.3)Position detector-basedguidance.Position detectors,such as quadrant photodetectors(QPDs),track light spots from docking station beacons.These systems excel in high-speedtracking and angular resolution but require precise optical alignment.Experimental validations demonstrate their robustness in turbulent flows,achieving angular accuracies within 0.1°.The sea trial demonstrated that the multi-branchnetwork optical guidance method,based on multi-quadrantphotoelectric detection and real-timeangle data processing,achieved an AUV position resolution speed of 5.650 ms/cycle and a mean coordinate error of 58.292 mm(best 7.107 mm at 2‒3 m),fulfilling precision and efficiency requirements for terminal docking with lower computational power and energy consumption compared to existing methods.Conclusions and Prospects Optical guidance for AUV underwater docking,a cornerstone technology enabling safe,continuous,and efficient marine operations,has garnered significant attention from researchers globally.This study systematically reviews two primary optical guidance paradigms:image sensor-basedmethods and position detector-basedmethods.Image sensor-basedapproaches,characterized by intuitive data acquisition and high positioning accuracy,dominate current practices by leveraging visual or photoelectric sensing to extract beacon features and resolve relative pose.Meanwhile,position detector-basedmethods,exemplified by multi-quadrantphotoelectric detectors,highlight advantages in detection speed and communication-integrationpotential.Despite progress,critical challenges persist:benchmark dataset limitations.While datasets have been developed,acquiring high-fidelityground truth data remains arduous due to dynamic underwater environments and system-inducednoise.Image sensor-basedmethods suffer from low frame rates,exacerbating latency and computational burdens during real-timeprocessing.Position detectors,though faster,lack sufficient modulation bandwidth for high-speedcommunication.To address these gaps,future advancements should focus on three synergistic directions:1)High-speed,stable,and intelligent guidance systems.The integration of deep learning architectures,particularly large-scalemodels,with edge-computingframeworks will enhance real-timedecision-makingcapabilities.Model quantization and lightweight design facilitate deployment on embedded devices,ensuring adaptive navigation in dynamic underwater scenarios.2)Integrated optical-acousticcommunication guidance.The development of multi-quadrantphotodetectors with high-frequencymodulation capabilities enables unify positioning and communication functions.Utilizing optical communication’s short-rangehigh-bandwidthadvantages while compensating for acoustic latency bridges the gap between near-fieldprecision and long-rangeconnectivity.3)Multi-sensorfusion perception.The fusion of heterogeneous sensor data(e.g.,GPS,INS,DVL)with optical guidance through advanced communication protocols and collaborative control algorithms enhances system performance.The incorporation of deep learning enables robust feature extraction and target perception,achieving centimeter-levelaccuracy and cross-domainsensor synergy.By synergizing these innovations,AUV underwater docking systems will evolve toward autonomous,resilient,and intelligent operation,unlocking new frontiers in marine exploration,infrastructure maintenance,and underwater robotics.
作者
李学龙
孙哲
吴国俊
Li Xuelong;Sun Zhe;Wu Guojun(School of Artificial Intelligence,Optics and Electronics(iOPEN),Northwestern Polytechnical University,Xi’an 710072,Shaanxi,China;Institute of Artificial Intelligence(TeleAI),China Telecom,Shanghai 200232,China;Xi’an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences,Xi’an 710119,Shaanxi,China)
出处
《光学学报》
北大核心
2025年第12期1-22,共22页
Acta Optica Sinica
基金
国家重点研发计划(2022YFC2808003)
陕西省自然科学基础研究计划面上项目(2024JC-YBMS-468)。
关键词
光学导引
自主水下航行器
图像信号处理
位置信号处理
涉水视觉
涉水光学
optical guidance
autonomous underwater vehicle
image signal processing
position signal processing
water-related vision
water-related optics
作者简介
通信作者:李学龙,xuelong_li@ieee.org;通信作者:孙哲,sunzhe@nwpu.edu.cn;通信作者:吴国俊,wuguojun@opt.ac.cn。
