Autonomous Underwater Vehicle Research Articles

Sonar-based object detection for autonomous underwater vehicles in marine environments

Sonar-based object detection for autonomous underwater vehicles in marine environments

Optimal event‐triggered trajectory tracking control for under‐actuated autonomous underwater vehicles with adaptive dynamic programming

AbstractIn this study, the optimal event‐triggered trajectory tracking control problem for under‐actuated autonomous underwater vehicles (AUVs) is investigated. First, a virtual velocity control law is designed for the position and attitude tracking error system using the backstepping approach. Next, the velocity tracking error is derived. By integrating the position, attitude, and velocity‐tracking errors, a comprehensive tracking error system is constructed, and a corresponding cost function for the system is defined. Then, the optimal event‐triggered control law is designed using event‐triggered adaptive dynamic programming, employing only a single critic network. The approximation error of the critic network and the stability of the tracking error system are analyzed using Lyapunov theory. Finally, simulation results demonstrate the effectiveness of the proposed optimal event‐triggered control law.

Reducing AUV Energy Consumption Through Dynamic Sensor Directions Switching via Deep Reinforcement Learning

Autonomous underwater vehicle (AUV) is crucial for marine applications such as ocean data collection, pollution monitoring, and navigation. However, their limited energy resources constrain their operational duration, posing a significant challenge for long-term operations. Due to the complex and unpredictable nature of the underwater environment, AUVs allocate energy to their sensing systems to sense the surrounding environment and avoid obstacles. Existing methods focus on reducing energy consumption on AUV computing and movement, neglecting sensing energy consumption and few attempts have been made to balance the AUV energy and sensing ability with a flexible sensing system. Along these lines, we consider both AUV energy consumption and flexible sensing abilities, and propose a deep reinforcement learning-based method to Reduce Energy Consumption by AUV Sensing system (RECS). Specifically, we build an AUV sensing system in a 2-dimension space, with controllable 8-direction sensing abilities to collect the environment information dynamically. Then we divide the underwater environment into several areas and assign weights on the edges of areas based on the AUV planned path. Additionally, we dynamically switch the sensors in different directions and radii to sense the edges of the area where the AUV is located. The Artificial Potential Field (APF) method is employed to re-plan the AUV path to avoid obstacles and reach the target point effectively. Experimental results demonstrate that compared to full sensors on, our method reduces energy consumption by 53.48% and is capable of generalizing to varying environments and varying sensing system radii.

An Adaptive Navigation System for an Autonomous Underwater Vehicle Based on Data Transmitted via an Acoustic Channel from a Hydroacoustic Station

Currently, it is becoming relevant to use cheap autonomous underwater vehicles (AUVs) to perform various underwater operations (environmental monitoring, aquatic protection, search and tracking of underwater biological objects, etc.). At the same time, the main way to reduce the cost of AUVs is to reduce the number of expensive on-board acoustic sensors. But this leads to a decrease in the accuracy of determining the parameters of the movement of these AUVs and the difficulty of performing missions. To solve this problem, this paper proposes a new method for the synthesis of an AUV navigation system, which recovers unavailable information (due to the absence of an expensive sensor) based on the dynamic model of the AUV and its thruster control signals. At the same time, these estimates are corrected using AUV position information, which is generated by an external hydroacoustic station (HAS) and transmitted via acoustic communication channels. This approach does not require synchronization procedures between the AUV and the HAS, but its accuracy significantly depends on the accuracy of determining the AUV dynamic model and the parameters of underwater currents. Two new approaches are proposed to ensure the accuracy of the navigation system. The first approach is to use the Kalman filter to combine data obtained from different sources with different periods and to take into account delays in receiving AUV position information at the stage of correcting estimates made by the Kalman filter. The second approach is to more accurately estimate the parameters of the AUV model and underwater currents based on data on the trajectory of the AUV obtained from the HAS. The use of these refined parameters of the AUV dynamic model makes it possible to significantly increase the accuracy of the navigation system. The simulation results carried out take into account the characteristics of real on-board sensors of the AUV and the HAS, and acoustic data transmission channels showed the high accuracy of the proposed method of constructing a navigation system, which reduces the cost of creating AUVs. In addition, the proposed algorithm can also be used during the failure of a number of AUV on-board navigation sensors.

AoI-MDP: An AoI Optimized Markov Decision Process Dedicated in the Underwater Task (Student Abstract)

Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process (AoI-MDP) to enhance underwater tasks by modeling observation delay as signal delay and including it in the state space. AoI-MDP also introduces wait time in the action space and integrates AoI with reward functions, optimizing information freshness and decision-making using reinforcement learning. Simulations show AoI-MDP outperforms the standard MDP, demonstrating superior performance, feasibility, and generalization in underwater tasks. To accelerate relevant research, we have made the codes available as open-source at https://github.com/Xiboxtg/AoI-MDP.

UACOF: A USV-AUV Collaboration Framework for Underwater Tasks Under Extreme Sea Conditions (Student Abstract)

Ocean exploration requires effective collaboration between the unmanned surface vehicle (USV) and autonomous underwater vehicles (AUVs). We propose UACOF, a USV-AUV collaboration framework that enhances multi-AUV performance under extreme sea conditions. The framework includes high-precision multi-AUV location via USV path planning with Fisher information matrix optimization and reinforcement learning training for cooperative tasks. Experimental results show UACOF's superior feasibility, performance, coordination and robustness in extreme conditions.

A Review of Path Following, Trajectory Tracking, and Formation Control for Autonomous Underwater Vehicles

This paper summarizes the latest research progress in the field of motion control of autonomous underwater vehicles (AUVs), focusing on three core technologies: path following, trajectory tracking and multi-AUV formation control. Aiming at the external disturbances faced by AUVs performing tasks in complex marine environments as well as the system’s own inherent nonlinearities, model uncertainties, and physical constraints, it analyzes the advantages and shortcomings of the traditional control methods and intelligent control strategies in terms of improving the tracking accuracy, enhancing the robustness of the system, and realizing the cooperative operation. Recent advances in distributed control and multi-AUV cooperative operations, including leader–follower, consistency control, virtual structure and behavior control, and other formation control strategies, are also discussed. Finally, the future development trend of AUV control technology is outlooked, pointing out that intelligent control, multi-sensor fusion navigation, and distributed synergy will become an important direction to enhance the operational capability and adaptability of AUVs. This review aims to provide theoretical references and technical support for AUV applications in the fields of marine resource exploration and environmental monitoring.

Assessment of Maneuvering Influence on the Fine Alignment of Autonomous Underwater Vehicle

ABSTRACTAutonomous underwater vehicles (AUVs) are specialized robots used to accomplish important field operations such as inspection of oil and gas pipelines, marine wildlife monitoring, imaging of river and sea beds, nondestructive testing of ship hulls, and so on. Before the start of an AUV mission, its navigation system, which is generally comprised of a doppler velocity log (DVL)/pressure sensor (PS)‐aided inertial navigation system (INS) needs to be initialized. After a brief coarse stage of initialization, the AUV attitude is generally refined (as well as some inertial measurement unit (IMU)/aiding sensor systematic error parameters are corrected for) in a Kalman filter (KF)‐based estimation process known as fine alignment, which is usually performed in open sea conditions. When the latter is conducted before the submerged phase of the AUV, a Global Navigation Satellite System (GNSS) receiver may provide additional aiding information to the refinement process. As the excitation of the degrees of freedom of the AUV is known to interfere with the performance of the KF fine alignment, this study exploits Baram and Kailath's concept of estimability to assess what kind of deliberate AUV maneuver is able to deliver the best estimation results. As the main contribution, we show that among the tested AUV motion profiles, the lawn mower is the maneuver that, except for the IMU/DVL misalignment around the AUV longitudinal axis, decreases the estimation uncertainties of all remaining INS/GNSS/DVL/PS fine alignment states. Results from simulated and experimental tests confirm the adequacy of the outlined verifications.

Physical simulations of interior trajectories for an AUV free swimming out from a moving underwater platform

ABSTRACT This study investigates strategies to enhance launch safety of Autonomous Underwater Vehicles (AUVs) by improving launch velocity and reducing deployment time. The multi-block dynamic hybrid mesh technology is used to perform unsteady numerical simulations of AUV free swimming-out from a moving underwater platform. The numerical results investigated the characteristics of the propeller thrust, resistance of AUV, and the transient flow field in the interior trajectory. Results identify that the blocking effect during AUV passing through the exit of the grid tube is the crucial reason for restricting the AUV’s launch velocity. To address that, the effects of different forms of water replenishment on the launch velocity are simulated. Simulation data demonstrates that optimized water compensation effectively mitigates flow obstruction: The most effective replenishment configuration reduces launch duration by 15.09% and achieves a 24.83% maximum velocity increase.

Deep Learning based Mine Detection using Side-Scan Sonar Image

This paper compares and analyzes the performance of deep learning-based object detection models for mine detection, particularly Faster R-CNN and YOLOv5, using side-scan sonar images. Additionally, it proposes effective data augmentation method to enhance the generalization performance of mine detection models. Performance evaluation based on the structure and size of each model indicates that the two-stage model, Faster R-CNN, is more suitable for precise search tasks, while the one-stage model, YOLOv5, offers faster processing speed, making it advantageous for rapid mine detection in large maritime areas. This contributes significantly to improving the efficiency of maritime boundary missions and operations utilizing autonomous underwater vehicles, thereby making a substantial impact on naval operations and mine counter measures strategies.

Safe and Optimal Motion Planning for Autonomous Underwater Vehicles: A Robust Model Predictive Control Framework Integrating Fast Marching Time Objectives and Adaptive Control Barrier Functions

Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods oversimplify obstacle geometries, and both fail to ensure safety and satisfy turning radius constraints for under-actuated AUVs in intricate environments. This paper proposes a robust Model Predictive Control (MPC) framework integrating an enhanced fast marching control barrier function, specifically designed for AUVs equipped with fully directional sonar systems. The framework introduces a novel improvement for moving obstacles by extending the control barrier function field propagation along the obstacle’s movement direction. This enhancement generates precise motion plans that ensure safety, satisfy kinematic constraints, and effectively handle static and dynamic obstacles. Simulation results demonstrate superior obstacle avoidance and motion planning performance in complex scenarios, with key outcomes including a minimum safety margin of 1.86 m in cluttered environments (vs. 0 m for A* and FMM) and 1.76 m in dynamic obstacle scenarios (vs. 0.13 m for MPC-APFCBF), highlighting the framework’s ability to enhance navigation safety and efficiency for real-world AUV deployments in unpredictable marine environments.

Optimal Trajectory Tracking for Underactuated Systems via the Takagi–Sugeno Framework: An Autonomous Underwater Vehicle Mission Case Study

Autonomy of underwater vehicles has become an imperative feature due to increasingly challenging deep sea mission scenarios. In particular, for trajectory-tracking problems of Autonomous Underwater Vehicles (AUVs), the use of Lyapunov theory tools in state-of-the-art methods is common practice. These often require special assumptions, according to the application considered, and ‘intuition’ for the choice of a control law, which often leads to conservative results. This article suggests a systematic analysis for the horizontal motion of an AUV which ensures global asymptotic stability for the closed loop system. A nonlinear underactuated AUV system is considered with linear and angular velocity constraints. The Takagi–Sugeno (TS) framework design is adopted for the representation of the original nonlinear system. The control law is synthesised using the standard parallel distributed compensation (PDC) control law structure and stability is guaranteed for the closed loop system. The design criteria are posed as linear matrix inequalities (LMIs) where sufficient conditions for the design of the control law are shown. The proposed approach can be easily adopted for different types of autonomous vehicles with minor modifications.

New Capability in Autonomous Ocean Carbon Observations Using the Autosub Long-Range AUV Equipped with Novel pH and Total Alkalinity Sensors.

The development of marine autonomous platforms has improved our capability to gather ocean observations at fine spatial scales and high temporal frequency, which can be used to better measure, characterize, and model ocean carbon. As part of the OCEANIDS program, novel carbonate sensors were integrated into the Autosub Long-Range (ALR) autonomous underwater vehicle (AUV) and deployed in the Celtic Sea. Autonomous Lab-On-Chip (LOC) sensors measured pH and total alkalinity (TA) while onboard the ALR. Using interpolation, the ALR-sensor data set is compared against CTD co-samples. The average differences between the LOC sensor and co-sample pH range from -0.011 to -0.015. The TA sensor data agrees with co-samples within 1-2 μmol kg-1 on average. Biogeochemical water properties differing between CTD and ALR observations reveal correlations to carbonate parameter variations. The LOC sensors enabled the characterization of the marine carbonate system from autonomous subsurface measurements for the first time. Sensor pH and TA data were used to calculate dissolved inorganic carbon (DIC), partial pressure of CO2 (pCO2), and aragonite saturation state (ΩAr) and are compared with CTD co-samples with mean residuals of 4-7 μmol kg-1, 10-17 μatm, and -0.03 to -0.06, respectively. Future perspectives on sensor deployment and analysis are discussed.

Design and experiments of an adaptive disturbance observer for tracking control of autonomous underwater vehicles

Design and experiments of an adaptive disturbance observer for tracking control of autonomous underwater vehicles

A Portable Autonomous Underwater Vehicle With Multi-Thruster Propulsion: Design, Development, and Vision-Based Tracking Control

A Portable Autonomous Underwater Vehicle With Multi-Thruster Propulsion: Design, Development, and Vision-Based Tracking Control

Low-Voltage and High-Current Wireless Power Transfer Systems for Autonomous Underwater Vehicles

Low-Voltage and High-Current Wireless Power Transfer Systems for Autonomous Underwater Vehicles

Position-based acoustic visual servo control for docking of autonomous underwater vehicle using deep reinforcement learning

Position-based acoustic visual servo control for docking of autonomous underwater vehicle using deep reinforcement learning

Collaborative Search Approach for Autonomous Underwater Vehicle Swarm-Based Distributed Radar in Communication Denied Environments

Collaborative Search Approach for Autonomous Underwater Vehicle Swarm-Based Distributed Radar in Communication Denied Environments

Sliding Optimal Tracking Control of Autonomous Underwater Vehicles With Adaptive Dynamic Programming

Sliding Optimal Tracking Control of Autonomous Underwater Vehicles With Adaptive Dynamic Programming

A sparse adaptive decision feedback equalization equipped with digital phase-locked loop on feedback filter for underwater acoustic communication with a fast-moving platform

Abstract Ocean survey systems using underwater robots, such as autonomous underwater vehicles, for exploring vast ocean areas have gained attention. However, mobile underwater acoustic communication (UWAC) channels present highly challenging doubly selective environments, necessitating advanced signal processing techniques. Recently, methods leveraging the physical characteristics of UWAC channels, particularly channel response sparsity, have garnered interest. This study proposes a signal processing method that applies a sparsity-constrained adaptive algorithm to a decision feedback equalizer, with digital phase-locked loops on the feedback filter taps to track Doppler shifts in multipaths. The method's performance was evaluated through simulations in a high-speed mobile communication environment in shallow water, showing improved communication performance under severe conditions, such as low-SNR and high relative speed (15 kt) with significant delay-Doppler spread. Additionally, the communication performance bounds were analyzed parametrically based on channel characteristics, confirming near-optimal gains relative to SNR or signal-to-interference ratio.