Autonomous Underwater Navigation

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Overview

The field studies how underwater vehicles estimate their position and move safely when visibility is poor, currents disturb motion, and acoustic communication is delayed or unavailable. Localization commonly fuses inertial sensors with Doppler velocity logs, sonar, cameras, depth sensors, and acoustic positioning, using Kalman filters, SLAM, and learned estimators. For planning and control, researchers combine methods such as model predictive control, line-of-sight guidance, mapping and search, reinforcement learning, and physics-based world models. Simulation benchmarks enable broad testing, while pool and sea trials assess real-world reliability; a central challenge is making methods robust to unfamiliar environments and sensor failures without excessive computation or expensive hardware.

Latest papers

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50 papers3 papers in the last four weeks, down 75% on the four weeks before. 0.0% of all new papers.

Jun 22, 2026

DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation

Autonomous Underwater Vehicles (AUVs) rely heavily on the fusion of inertial sensors and Doppler velocity logs (DVLs) for navigation. In standard autonomous navigation systems, the DVL measures four beam velocities, thereby enabling the estimation of the AUV velocity vector. However, during real-world missions, the DVL may receive noisy or incomplete beam measurements due to marine obstacles, seabed reflections, or environmental disturbances. Furthermore, some low-cost underwater platforms operate without inertial sensors to reduce system complexity and cost. In such cases, reliable estimation of the AUV velocity vector in real-world missing beam scenarios becomes challenging, leading to degraded navigation solutions. To circumvent these challenges and enable resilient underwater navigation, we propose DVL-DeepONet, a physics-guided deep neural operator framework along with three variants. The proposed models are designed to estimate DVL-based velocity information under multiple operational scenarios, including (i) noise-resilient estimation in coupled inertial/DVL measurements, (ii) DVL-only learning, and (iii) beam measurement recovery. By learning a nonlinear operator that maps temporal inertial/DVL observations directly to vehicle velocity while enforcing DVL measurement physics through a consistency constraint, the proposed approach enables robust velocity estimation even under degraded sensing conditions. The proposed framework is validated using real-world AUV experiments, comprising a cumulative path length of approximately 10,000 m. Experimental results demonstrate that the proposed DVL-DeepONet architectures outperform baseline model-based approaches and learning-based algorithms by 40%.

Arup Kumar Sahoo, Itzik Klein

The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, 3498838, Israel

Underwater RoboticsPhysics-Informed MLAutonomous Underwater NavigationOperator LearningMultimodal Sensor Fusion
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Jun 22, 2026

ISOPoT: Imaging Sonar Odometry by Point Tracking

Reliable navigation in underwater environments remains a key challenge in marine robotics. In such scenarios, forward-looking sonars are a natural choice for long-range perception, offering wide coverage even in turbid, low-visibility conditions. However, sonar images are inherently noisy, contain artifacts, and lack rich semantic structure, causing standard computer vision methods for keypoint detection and matching to perform poorly. In this paper, we introduce ISOPoT, an imaging sonar odometry method based on modern point tracking techniques. We propose a sonar odometry pipeline that uses multi-frame point tracks as its primary correspondence representation, augmented with lightweight optimizations to improve robustness. We evaluated the proposed method on the Aracati 2017 dataset, as well as on an internal sonar dataset collected in real-world underwater environments. Our results show that ISOPoT outperforms previous state-of-the-art methods consistently in both sonar-only scenarios and in multi-sensor settings.

Jaša Samec, Vid Rijavec, Marko Peljhan +4

Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia · OZON Research Group, TIMTEC, Goriska cesta 6c, Vipava, 5271, Slovenia · MAT Systemics, University of California, Santa Barbara, 3309 Phelps Hall, Santa Barbara, 93106-6065, California, United States of America +1

Point TrackingRelative Pose EstimationUnderwater ImagingUnderwater RoboticsAutonomous Underwater NavigationRobot State Estimation
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Jun 7, 2026

Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning

Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture splitting the problem into two Markov Decision Processes. A High-Level (HL) policy operating at 2Hz processes raw 84×8484 \times 84 pixel monocular camera frames, stacked 100×100100 \times 100 pixel forward-looking imaging sonar, and proprioceptive data to generate spatial subgoals. Simultaneously, a Low-Level (LL) policy operating at 10Hz converts these subgoals into thruster commands. The HL policy is trained using Reinforcement Learning from Prior Demonstrations (RLPD) within a modified Sample-Efficient Robotic Reinforcement Learning (SERL) framework, while the LL policy utilizes Soft Actor-Critic (SAC) combined with Hindsight Experience Replay (HER). Evaluated in the high-fidelity HoloOcean simulator, our method demonstrates successful obstacle avoidance, achieving trajectory lengths closely approximating (within 4% to 6% of) an RRT∗\text{RRT}^* planning baseline. Furthermore, the learned policy exhibits strong robustness to simulated sensor noise and decreased visibility. While the system navigates familiar geometries effectively, experiments reveal generalization limitations when encountering unvisited areas with novel obstacle shapes. Ultimately, this work demonstrates the promise of sample-efficient, end-to-end DRL for underwater navigation using minimal computational hardware.

Elisei Shafer, Oren Gal

Department of Marine Technologies, University of Haifa, Israel

Hierarchical RLVisuomotor Policy LearningUnderwater RoboticsAutonomous Underwater NavigationRobust RL
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Jun 4, 2026

3D Underwater Path Planning via Generative Flow Field Surrogates

Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensional propeller wake whose hydrodynamic structure cannot be characterised by a uniform current model. High-fidelity Reynolds-Averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) simulations resolve this structure with sufficient accuracy for path planning, but their computational cost renders them impractical for onboard use. We address this gap by integrating two conditional generative adversarial network (cGAN) architectures -- a regularised PatchGAN and a 2D3DGAN with self-attention -- as drop-in replacements for RANS CFD data within a three-dimensional, energy-weighted A* path planning framework. Both generators are driven by a hierarchical pipeline that synthesises full 1283128^3 voxel flow field volumes from scalar operating condition inputs alone, with end-to-end inference times of approximately 28-146 μμs, compared to hours for a single RANS computation. We benchmark all four environmental knowledge levels: uniform current, ground-truth CFD, PatchGAN, and 2D3DGAN~SA across 19,800 independently generated trajectories spanning 550 distinct flow conditions. Full CFD wake knowledge reduces energy expenditure by 5.7-12.5% and high-velocity wake-core encounters by up to 77.8% relative to uniform-current planning, with both benefits scaling with operating severity. The cGAN surrogates recover approximately 45-60% of the CFD energy benefit and high-velocity cell avoidance benefit while operating at inference speeds compatible with edge device use. These results provide the first systematic quantification of the downstream path planning value of cGAN-predicted hydrodynamic fields in a three-dimensional maritime robotics application.

Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth +1

Flinders University · PrioriAnalytica

Surrogate ModelingUnderwater RoboticsMotion PlanningAutonomous Underwater NavigationGenerative Adversarial Networks
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May 6, 2026

AI-Aided Advancements in Autonomous Underwater Vehicle Navigation

Autonomous underwater vehicles (AUVs) have become indispensable for deep-sea exploration, spanning critical scientific research and commercial applications. The rapid attenuation of electromagnetic waves renders satellite radio signals unavailable, while the dynamic unpredictability of the marine environment presents formidable navigation challenges. This chapter explores recent advancements in AI-aided AUV positioning, specifically focusing on advanced sensor fusion architectures that integrate inertial navigation systems with Doppler velocity logs and cameras. Beyond traditional model-based filtering, we examine the transformative emergence of AI-driven learning approaches in enhancing inertial dead-reckoning tasks and adaptive fusion algorithms. By addressing these recent milestones, this chapter provides a comprehensive roadmap for achieving the high-precision navigation essential for autonomous underwater missions.

Guy Damari, Zeev Yampolsky, Nadav Cohen +4

The Hatter Department of Marine Technologies, University of Haifa, Abba Khoushy Ave 199, Haifa, 3498838, Israel · Federal University of Lavras (UFLA), Campus Universitário, Lavras, 37200-900, MG, Brazil

Underwater RoboticsAutonomous Underwater NavigationInertial Navigation
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Apr 23, 2026

Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation

Autonomous underwater vehicles are required to perform multiple tasks adaptively and in an explainable manner under dynamic, uncertain conditions and limited sensing, challenges that classical controllers struggle to address. This demands robust, generalizable, and inherently interpretable control policies for reliable long-term monitoring. Reinforcement learning, particularly multi-task RL, overcomes these limitations by leveraging shared representations to enable efficient adaptation across tasks and environments. However, while such policies show promising results in simulation and controlled experiments, they yet remain opaque and offer limited insight into the agent's internal decision-making, creating gaps in transparency, trust, and safety that hinder real-world deployment. The internal policy structure and task-specific specialization remain poorly understood. To address these gaps, we analyze the internal structure of a pretrained multi-task reinforcement learning network in the HoloOcean simulator for underwater navigation by identifying and comparing task-specific subnetworks responsible for navigating toward different species. We find that in a contextual multi-task reinforcement learning setting with related tasks, the network uses only about 1.5% of its weights to differentiate between tasks. Of these, approximately 85% connect the context-variable nodes in the input layer to the next hidden layer, highlighting the importance of context variables in such settings. Our approach provides insights into shared and specialized network components, useful for efficient model editing, transfer learning, and continual learning for underwater monitoring through a contextual multi-task reinforcement learning method.

Yi-Ling Liu, Melvin Laux, Mariela De Lucas Alvarez +2

Robotics Innovation Center, German Research Center for Artificial Intelligence, Bremen, Germany · Robotics Research Group, University of Bremen, Bremen, Germany

Reinforcement LearningUnderwater RoboticsMulti-Task RLAutonomous Underwater NavigationInterpretability in RL
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Apr 13, 2026

BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams

Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. In the absence of external position fixes, AUVs rely on inertial dead-reckoning, which accumulates unbounded drift due to sensor bias and noise. This paper presents BIND-USBL, a cooperative localization framework in which a fleet of Autonomous Surface Vessels (ASVs) equipped with Ultra-Short Baseline (USBL) acoustic positioning systems provides intermittent fixes to bound AUV dead-reckoning error. The key insight is that long-duration navigation failure is driven not by the accuracy of individual USBL measurements, but by the temporal sparsity and geometric availability of those fixes. BIND-USBL combines a multi-ASV formation model linking survey scale and anchor placement to acoustic coverage, a conflict-graph-based TDMA uplink scheduler for shared-channel servicing, and delayed fusion of received USBL updates with drift-prone dead reckoning. The framework is evaluated in the HoloOcean simulator using heterogeneous ASV-AUV teams executing lawnmower coverage missions. The results show that localization performance is shaped by the interaction of survey scale, acoustic coverage, team composition, and ASV-formation geometry. Further, the spatial-reuse scheduler improves per-AUV fix delivery rate without violating the no-collision constraint, while maintaining low end-to-end fix latency.

Pranav Kedia, Rajini Makam, Heiko Hamann +1

Dept of Computer and Information Science, University of Konstanz, Germany · Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany · Dept of Aerospace Engineering, Indian Institute of Science, Bangalore, India

Autonomous Underwater NavigationCooperative LocalizationMaritime NavigationMulti-Robot CoordinationInertial Navigation
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Apr 7, 2026

Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces

Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging due to poor visibility, weak signal transmission, and dynamic water currents. These factors pose challenges in accurate global localization, reliable communication, and obstacle avoidance. Local sensing provides critical real time environmental data to enable online decision making. However, the inherent noise in underwater sensor measurements introduces uncertainty, complicating planning and control. To address these challenges, we propose an integrated planning and control framework that leverages real time sensor data to dynamically induce closed loop AUV trajectories, ensuring robust obstacle avoidance and enhanced maneuverability in tight spaces. By planning motion based on pre designed feedback controllers, the approach reduces the computational complexity needed for carrying out online optimizations and enhances operational safety in complex underwater spaces. The proposed method is validated through ROS Gazebo simulations on the RexRov AUV, demonstrating its efficacy. Its performance is evaluated by comparison against PID based tracking methods, and quantifying localization errors in dead reckoning as the AUV transitions into the target communication range.

Veejay Karthik, Udit Ekansh, Tejal Bedmutha +3

Centre for Systems and Control, Indian Institute of Technology Bombay, Mumbai · Institute for Control, Optimization, and Networks, Purdue University, West Lafayette, USA

Autonomous Underwater NavigationSafe Motion PlanningMotion PlanningRobot Collision Avoidance
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Apr 6, 2026

A Survey on Sensor-based Planning and Control for Unmanned Underwater Vehicles

This survey examines recent sensor-based planning and control methods for Unmanned Underwater Vehicles (UUVs). In complex, uncertain underwater environments, UUVs require advanced planning and control strategies for effective navigation. These vehicles face significant challenges including drifting and noisy sensor measurements, absence of Global Navigation Satellite System (GNSS) signals, and low-bandwidth, high-latency underwater acoustic communications. The focus is on reactive local planning layers that adapt to real-time sensor inputs such as SONAR and Inertial Measurement Units (IMU) to improve localization accuracy and autonomy in dynamic ocean conditions, enabling dynamic obstacle avoidance and on-the-fly re-planning. The survey categorizes the existing literature into decoupled and coupled architectures for sensor-based planning and control. The decoupled architecture sequentially addresses planning and control stages, whereas coupled architectures offer tighter feedback loops for more immediate responsiveness. A comparative analysis of coupled planning and control methods reveals that while PID controllers are simple, they lack predictive capability for complex maneuvers. Model Predictive Control (MPC) offers superior path optimization but can be computationally intensive, and invariant-set controllers provide strong safety guarantees at the potential cost of agility in confined environments. Key contributions include a taxonomy of architectures combining planning and control, a focus on adaptive local planning, and an analysis of controller roles in integrated planning frameworks for autonomous navigation of UUVs.

Shivam Vishwakarma, Tejal Bedmutha, Dharmendra Kumar Patel +2

Underwater RoboticsAutonomous Underwater NavigationRobotic Control
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Apr 3, 2026

An Asynchronous Two-Speed Kalman Filter for Real-Time UUV Cooperative Navigation Under Acoustic Delays

In Global Navigation Satellite System (GNSS)-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded dead-reckoning drift, making collaborative navigation (CN) crucial for accurate state estimation. However, the severe communication delay inherent in underwater acoustic channels poses serious challenges to real-time state estimation. Traditional filters, such as Extended Kalman Filters (EKFs) or Unscented Kalman Filters (UKFs), usually block the main control loop while waiting for delayed data, or effectively discard Out-of-Sequence Measurements (OOSMs), resulting in serious drift. To address this, we propose an Asynchronous Two-Speed Kalman Filter (TSKF) enhanced by a novel projection mechanism, which we term Variational History Distillation (VHD). The proposed architecture decouples the estimation process into two parallel threads: a fast-rate thread that utilizes Gaussian Process (GP) compensated dead reckoning to guarantee high-frequency real-time control, and a slow-rate thread dedicated to processing asynchronously delayed collaborative information. By introducing a Finite-Length Circular State Buffer (FLCSB), the algorithm applies delayed measurements to their corresponding historical states, and utilizes a VHD-based projection to fast-forward the correction to the current time without computationally heavy recalculations. Simulation results demonstrate that the proposed TSKF maintains a trajectory error comparable to computationally intensive batch-optimization methods under severe delays (up to 30,s). Executing in sub-millisecond time, it significantly outperforms standard EKF/UKF. The results demonstrate an effective control, communication, and computing (3C) co-design that significantly enhances the resilience of autonomous marine automation systems.

Shuyue Li, Miguel López-Benítez, Eng Gee Lim +5

School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China · School of Computer Science and Informatics, University of Liverpool, Liverpool, UK · ARIES Research Centre, Universidad Antonio de Nebrija, Madrid, Spain +3

Kalman FilteringGNSS-Denied LocalizationCooperative LocalizationUnderwater RoboticsAutonomous Underwater NavigationRobot State Estimation+1
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Mar 31, 2026

Communication Outage-Resistant UUV State Estimation: A Variational History Distillation Approach

The reliable operation of Unmanned Underwater Vehicle (UUV) clusters is highly dependent on continuous acoustic communication. However, this communication method is highly susceptible to intermittent interruptions. When communication outages occur, standard state estimators such as the Unscented Kalman Filter (UKF) will be forced to make open-loop predictions. If the environment contains unmodeled dynamic factors, such as unknown ocean currents, this estimation error will grow rapidly, which may eventually lead to mission failure. To address this critical issue, this paper proposes a Variational History Distillation (VHD) approach. VHD regards trajectory prediction as an approximate Bayesian reasoning process, which links a standard motion model based on physics with a pattern extracted directly from the past trajectory of the UUV. This is achieved by synthesizing ``virtual measurements'' distilled from historical trajectories. Recognizing that the reliability of extrapolated historical trends degrades over extended prediction horizons, an adaptive confidence mechanism is introduced. This mechanism allows the filter to gradually reduce the trust of virtual measurements as the communication outage time is extended. Extensive Monte Carlo simulations in a high-fidelity environment demonstrate that the proposed method achieves a 91% reduction in prediction Root Mean Square Error (RMSE), reducing the error from approximately 170 m to 15 m during a 40-second communication outage. These results demonstrate that VHD can maintain robust state estimation performance even under complete communication loss.

Shuyue Li, Miguel López-Benítez, Eng Gee Lim +5

School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China · School of Engineering, University of Liverpool, Liverpool, UK · Jiangsu JITRI Tsingunited Intelligent Control Technology Co., Ltd., Wuxi, China +4

Robot State EstimationRobust EstimationAutonomous Underwater NavigationUnscented Kalman Filtering
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Mar 28, 2026

Predictive Modeling in AUV Navigation: A Perspective from Kalman Filtering

We present a safety-oriented framework for autonomous underwater vehicles (AUVs) that improves localization accuracy, enhances trajectory prediction, and supports efficient search operations during communication loss. Acoustic signals emitted by the AUV are detected by a network of fixed buoys, which compute Time-Difference-of-Arrival (TDOA) range-difference measurements serving as position observations. These observations are subsequently fused with a Kalman-based prediction model to obtain continuous, noise-robust state estimates. The combined method achieves significantly better localization precision and trajectory stability than TDOA-only baselines. Beyond real-time tracking, our framework offers targeted search-and-recovery capability by predicting post-disconnection motion and explicitly modeling uncertainty growth. The search module differentiates between continued navigation and propulsion failure, allowing search resources to be deployed toward the most probable recovery region. Our framework fuses multi-buoy acoustic data with Kalman filtering and uncertainty propagation to maintain navigation accuracy and yield robust search-region definitions during communication loss.

Zizhan Tang, Yao Liu, Jessica Liu

Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA · Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA

Autonomous Underwater NavigationRobot State EstimationTrajectory PredictionKalman Filtering
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Mar 17, 2026

Neural Aided Adaptive Innovation-Based Invariant Kalman Filter

Autonomous platforms require accurate positioning to complete their tasks. To this end, a Kalman filter-based algorithms, such as the extended Kalman filter or invariant Kalman filter, utilizing inertial and external sensor fusion are applied. To cope with real-world scenarios, adaptive noise estimation methods have been developed primarily for classical Euclidean formulations. However, these methods remain largely unexplored in the tangent Lie space, despite it provides a principled geometric framework with favorable error dynamics on Lie groups. To fill this gap, we combine invariant filtering theory with neural-aided adaptive noise estimation in real-world settings. To this end, we derive a novel theoretical extension of classical innovation-based process noise adaptation formulated directly within the Lie-group framework. We further propose a lightweight neural network that estimates the process noise covariance parameters directly from raw inertial data. Trained entirely in a sim2real framework via domain adaptation, the network captures motion-dependent and sensor-dependent noise characteristics without requiring labeled real-world data. To examine our proposed neural-aided adaptive invariant Kalman filter, we focus on the challenging real-world scenario of autonomous underwater navigation. Experimental results demonstrate superior performance compared to existing methods in terms of position root mean square error. These results validate our sim2real pipeline and further confirm that geometric invariance significantly enhances learning-based adaptation and that adaptive noise estimation in the tangent Lie space offers a powerful mechanism for improving navigation accuracy in nonlinear autonomous platforms.

Barak Diker, Itzik Klein

Kalman FilteringInvariant EKFAutonomous Underwater Navigation
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Mar 16, 2026

CORAL: COntextual Reasoning And Local Planning in A Hierarchical VLM Framework for Underwater Monitoring

Oyster reefs are critical ecosystem species that sustain biodiversity, filter water, and protect coastlines, yet they continue to decline globally. Restoring these ecosystems requires regular underwater monitoring to assess reef health, a task that remains costly, hazardous, and limited when performed by human divers. Autonomous underwater vehicles (AUVs) offer a promising alternative, but existing AUVs rely on geometry-based navigation that cannot interpret scene semantics. Recent vision-language models (VLMs) enable semantic reasoning for intelligent exploration, but existing VLM-driven systems adopt an end-to-end paradigm, introducing three key limitations. First, these systems require the VLM to generate every navigation decision, forcing frequent waits for inference. Second, VLMs cannot model robot dynamics, causing collisions in cluttered environments. Third, limited self-correction allows small deviations to accumulate into large path errors. To address these limitations, we propose CORAL, a framework that decouples high-level semantic reasoning from low-level reactive control. The VLM provides high-level exploration guidance by selecting waypoints, while a dynamics-based planner handles low-level collision-free execution. A geometric verification module validates waypoints and triggers replanning when needed. Compared with the previous state-of-the-art, CORAL improves coverage by 14.28% percentage points, or 17.85% relatively, reduces collisions by 100%, and requires 57% fewer VLM calls.

Zhenqi Wu, Yuanjie Lu, Xuesu Xiao +1

University of South Florida · George Mason University

Autonomous Underwater NavigationVision-Language NavigationVLMs for RoboticsLarge Language Model-Based Robot Planning
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Mar 9, 2026

Hierarchical Multi-Modal Planning for Fixed-Altitude Sparse Target Search and Sampling

Efficient monitoring of sparse benthic phenomena, such as coral colonies, presents a great challenge for Autonomous Underwater Vehicles. Traditional exhaustive coverage strategies are energy-inefficient, while recent adaptive sampling approaches rely on costly vertical maneuvers. To address these limitations, we propose HIMoS (Hierarchical Informative Multi-Modal Search), a fixed-altitude framework for sparse coral search-and-sample missions. The system integrates a heterogeneous sensor suite within a two-layer planning architecture. At the strategic level, a Global Planner optimizes topological routes to maximize potential discovery. At the tactical level, a receding-horizon Local Planner leverages differentiable belief propagation to generate kinematically feasible trajectories that balance acoustic substrate exploration, visual coral search, and close-range sampling. Validated in high-fidelity simulations derived from real-world coral reef benthic surveys, our approach demonstrates superior mission efficiency compared to state-of-the-art baselines.

Lingpeng Chen, Yuchen Zheng, Apple Pui-Yi Chui +2

Informative Path PlanningUnderwater RoboticsMotion PlanningAutonomous Underwater NavigationMulti-Objective Motion Planning
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Mar 8, 2026

Underwater Embodied Intelligence for Autonomous Robots: A Constraint-Coupled Perspective on Planning, Control, and Deployment

Autonomous underwater robots are increasingly deployed for environmental monitoring, infrastructure inspection, subsea resource exploration, and long-horizon exploration. Yet, despite rapid advances in learning-based planning and control, reliable autonomy in real ocean environments remains fundamentally constrained by tightly coupled physical limits. Hydrodynamic uncertainty, partial observability, bandwidth-limited communication, and energy scarcity are not independent challenges; they interact within the closed perception-planning-control loop and often amplify one another over time. This Review develops a constraint-coupled perspective on underwater embodied intelligence, arguing that planning and control must be understood within tightly coupled sensing, communication, coordination, and resource constraints in real ocean environments. We synthesize recent progress in reinforcement learning, belief-aware planning, hybrid control, multi-robot coordination, and foundation-model integration through this embodied perspective. Across representative application domains, we show how environmental monitoring, inspection, exploration, and cooperative missions expose distinct stress profiles of cross-layer coupling. To unify these observations, we introduce a cross-layer failure taxonomy spanning epistemic, dynamic, and coordination breakdowns, and analyze how errors cascade across autonomy layers under uncertainty. Building on this structure, we outline research directions toward physics-grounded world models, certifiable learning-enabled control, communication-aware coordination, and deployment-aware system design. By internalizing constraint coupling rather than treating it as an external disturbance, underwater embodied intelligence may evolve from performance-driven adaptation toward resilient, scalable, and verifiable autonomy under real ocean conditions.

Jingzehua Xu, Guanwen Xie, Jiwei Tang +2

Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China · Department of Data and Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong +1

Underwater RoboticsAutonomous Underwater Navigation
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Feb 24, 2026

Autonomous Sea Turtle Robot for Marine Fieldwork

Autonomous robots can transform how we observe marine ecosystems, but close-range operation in reefs and other cluttered habitats remains difficult. Vehicles must maneuver safely near animals and fragile structures while coping with currents, variable illumination and limited sensing. Previous approaches simplify these problems by leveraging soft materials and bioinspired swimming designs, but such platforms remain limited in terms of deployable autonomy. Here we present a sea turtle-inspired autonomous underwater robot that closed the gap between bioinspired locomotion and field-ready autonomy through a tightly integrated, vision-driven control stack. The robot combines robust depth-heading stabilization with obstacle avoidance and target-centric control, enabling it to track and interact with moving objects in complex terrain. We validate the robot in controlled pool experiments and in a live coral reef exhibit at the New England Aquarium, demonstrating stable operation and reliable tracking of fast-moving marine animals and human divers. To the best of our knowledge, this is the first integrated biomimetic robotic system, combining novel hardware, control, and field experiments, deployed to track and monitor real marine animals in their natural environment. During off-tether experiments, we demonstrate safe navigation around obstacles (91% success rate in the aquarium exhibit) and introduce a low-compute onboard tracking mode. Together, these results establish a practical route toward soft-rigid hybrid, bioinspired underwater robots capable of minimally disruptive exploration and close-range monitoring in sensitive ecosystems.

Zach J. Patterson, Emily Sologuren, Levi Cai +4

Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, 44106, USA. · Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, 02139, USA. · Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, 02543, USA. +1

Underwater RoboticsAutonomous Underwater NavigationVision-Based Robot Control
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Feb 22, 2026

Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders

Underwater glider robots have become indispensable for ocean sampling, yet fully autonomous long-term operation remains rare in practice. Although stakeholders are calling for tools to manage increasingly large fleets of gliders, existing methods have seen limited adoption due to their inability to account for environmental uncertainty and operational constraints. In this work, we demonstrate that uncertainty-aware online navigation planning can be deployed in real-world glider missions at scale. We formulate the problem as a stochastic shortest-path Markov Decision Process and propose a sample-based online planner based on Monte Carlo Tree Search. Samples are generated by a physics-informed simulator calibrated on real-world glider data that captures uncertain execution of controls and ocean current forecasts while remaining computationally tractable. Our methodology is integrated into an autonomous system for Slocum gliders that performs closed-loop replanning at each surfacing. The system was validated in two North Sea deployments totalling approximately 3 months and 1000 km, representing the longest fully autonomous glider campaigns in the literature to date. Results demonstrate improvements of up to 9.88% in dive duration and 16.51% in path length compared to standard straight-to-goal navigation, including a statistically significant path length reduction of 9.55% in a field deployment.

Victor-Alexandru Darvariu, Charlotte Z. Reed, Jan Stratmann +21

Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, UK · Optiver, Amsterdam, The Netherlands · Stateful Robotics, Oxford, UK +3

Autonomous Underwater Navigation
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Feb 18, 2026

Docking and Persistent Operations for a Resident Underwater Vehicle

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 90 m depth. The ROV is 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.

Leonard Günzel, Gabrielė Kasparavičiūtė, Ambjørn Grimsrud Waldum +6

Applied Underwater Robotics Lab NTNU Trondheim, Norway

Underwater RoboticsAutonomous Underwater NavigationRobotic InspectionRobot Localization
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Oct 21, 2025

SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar

Autonomous navigation in 3D is a fundamental problem for autonomy. Despite major advancements in terrestrial and aerial settings due to improved range sensors including LiDAR, compact sensors with similar capabilities for underwater robots have only recently become available, in the form of 3D sonars. This paper introduces a novel underwater 3D navigation pipeline, called SHRUMS (Sensor Hallucination for Robust Underwater Motion planning with 3D Sonar). To the best of the authors' knowledge, SHRUMS is the first underwater autonomous navigation stack to integrate a 3D sonar and demonstrate safe operation in complex 3D environments without reliance on visual input, thereby remaining independent of visibility conditions. To accommodate the intricacies of the novel sensor data stream while achieving real-time locally optimal performance, SHRUMS introduces the concept of hallucinating sensor measurements from non-existent sensors with convenient arbitrary parameters, tailored to application specific requirements. The proposed concepts are validated in simulations informed with real 3D sonar sensor data, in-pool clean-water trials, and field deployments in real conditions with significantly degraded visibility conditions.

Susheel Vadakkekuruppath, Lauritz R. Fosso, Michail Chatzispyrou +4

Susheel Vadakkekuruppath and Jason M. O’Kane are with the Department of Computer Science and Engineering, Texas A&M University, USA · Herman B. Amundsen and Marios Xanthidis are with the Aquaculture Robotics and Automation, SINTEF Ocean

Autonomous Underwater NavigationMotion Planning
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