Docking and Persistent Operations for a Resident Underwater Vehicle

• URL: http://arxiv.org/abs/2602.16360v1
• Date: Wed, 18 Feb 2026 10:50:04 GMT
• Title: Docking and Persistent Operations for a Resident Underwater Vehicle
• Abstract summary: This work presents the development, deployment, and operation of a resident infrastructure using a mini-class Remotely Operated Vehicle (ROV) at 90m depth.<n>The ROVis equipped with enhanced onboard processing and perception, allowing it to autonomously navigate using USBL signals.<n>The system demonstrated a 90% autonomous docking success rate and completed full inspection missions within four minutes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our understanding of the oceans remains limited by sparse and infrequent observations, primarily because current methods are constrained by the high cost and logistical effort of underwater monitoring, relying either on sporadic surveys across broad areas or on long-term measurements at fixed locations. To overcome these limitations, monitoring systems must enable persistent and autonomous operations without the need for continuous surface support. Despite recent advances, resident underwater vehicles remain uncommon due to persistent challenges in autonomy, robotic resilience, and mechanical robustness, particularly under long-term deployment in harsh and remote environments. This work addresses these problems by presenting the development, deployment, and operation of a resident infrastructure using a docking station with a mini-class Remotely Operated Vehicle (ROV) at 90m depth. The ROVis equipped with enhanced onboard processing and perception, allowing it to autonomously navigate using USBL signals, dock via ArUco marker-based visual localisation fused through an Extended Kalman Filter, and carry out local inspection routines. The system demonstrated a 90% autonomous docking success rate and completed full inspection missions within four minutes, validating the integration of acoustic and visual navigation in real-world conditions. These results show that reliable, untethered operations at depth are feasible, highlighting the potential of resident ROV systems for scalable, cost-effective underwater monitoring.

Related papers

• A Vision-based Control Framework for Real-time Autonomous UUV Operations [1.293050392312921]
  This paper presents a vision-based framework for real-time and robust localization, autonomous navigation, and mapping for unmanned underwater vehicles (UUVs)<n>The proposed pipeline enables both net-relative and global localization while generating continuous 3D maps of the surroundings in real-time.<n>Results demonstrate real-time performance and enhanced robustness, supporting vision-driven autonomous navigation.
  arXiv  Detail & Related papers  (2026-08-05T11:43:30Z)
• Underwater Embodied Intelligence for Autonomous Robots: A Constraint-Coupled Perspective on Planning, Control, and Deployment [9.865783767797874]
  Review argues that planning and control must be understood within tightly coupled sensing, communication, coordination, and resource constraints in real ocean environments.<n>We show how environmental monitoring, inspection, exploration, and cooperative missions expose distinct stress profiles of cross-layer coupling.
  arXiv  Detail & Related papers  (2026-03-08T00:45:06Z)
• HUNT: High-Speed UAV Navigation and Tracking in Unstructured Environments via Instantaneous Relative Frames [50.83645076723809]
  HUNT (High-speed UAV Navigation and Tracking) is a real-time framework that unifies, acquisition, and tracking within a single relative formulation.<n>Trials in dense forests, container compounds, and search-and-rescue operations with vehicles and mannequins demonstrate robust autonomy where global methods fail.
  arXiv  Detail & Related papers  (2025-09-23T18:07:10Z)
• DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring [14.347123589187964]
  We present DREAM, a Vision Language Model (VLM)-guided autonomy framework for long-term underwater exploration and habitat monitoring.<n>In oyster-monitoring task, our framework takes 31.5% less time than the previous baseline with the same amount of oysters.<n>In shipwreck scenes, our framework successfully explores and maps the wreck without collisions, requiring 27.5% fewer steps than the vanilla model.
  arXiv  Detail & Related papers  (2025-09-17T03:35:52Z)
• DUViN: Diffusion-Based Underwater Visual Navigation via Knowledge-Transferred Depth Features [47.88998580611257]
  We propose a Diffusion-based Underwater Visual Navigation policy via knowledge-transferred depth features, named DUViN.<n>DuViN guides the vehicle to avoid obstacles and maintain a safe and perception awareness altitude relative to the terrain without relying on pre-built maps.<n> Experiments in both simulated and real-world underwater environments demonstrate the effectiveness and generalization of our approach.
  arXiv  Detail & Related papers  (2025-09-03T03:43:12Z)
• Continuous Marine Tracking via Autonomous UAV Handoff [0.4935992163749761]
  This paper introduces an autonomous UAV vision system for continuous, real-time tracking of marine animals, specifically sharks.<n>The system integrates an onboard computer with a stabilised RGB-D camera and a custom-trained OSTrack pipeline.<n>A key innovation is the inter-UAV handoff protocol, which enables seamless transfer of tracking responsibilities between drones.
  arXiv  Detail & Related papers  (2025-07-17T03:35:53Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• Benchmarking Online Object Trackers for Underwater Robot Position Locking Applications [0.9642500063568189]
  Vision-based underwater robot navigation and control has recently gained increasing attention to counter the numerous challenges faced in underwater conditions.<n>In this paper, we propose a first rigorous unified benchmarking of more than seven Machine Learning (ML)-based one-shot object tracking algorithms for vision-based position locking of ROV platforms.<n>Our proposed system uses the output result of different object tracking algorithms to automatically correct the position of the ROV against external disturbances.
  arXiv  Detail & Related papers  (2025-02-23T13:27:34Z)
• Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time [1.3387004254920498]
  AUVs are becoming standard tools for underwater exploration and seabed mapping in both scientific and industrial applications. Their capacity to dive untethered allows them to reach areas inaccessible to surface vessels and to collect data more closely to the seafloor. navigation autonomy remains bounded by the accuracy of their dead reckoning (DR) estimate of their global position, severely limited in the absence of a priori maps of the area and GPS signal.
  arXiv  Detail & Related papers  (2022-11-10T14:21:48Z)
• ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI [55.41644538483948]
  Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response operations. We have developed the free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS. We demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide timely predictions of catastrophic flooding events.
  arXiv  Detail & Related papers  (2022-01-25T14:51:19Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.