Underwater Vehicle-manipulator Research Articles

Experimental investigation on wave characteristics due to interference of catamaran demi-hulls in waves

The presence of twin hulls of a catamaran has a strong impact on wave pattern in the whole inner region due to interference between waves generated by each demi-hull. Consequently, wave profiles as well as wave height become complicated and unpredictable in the inner region not only in still water, but also in the presence of waves. Determining wave characteristics between demi-hulls of the catamaran is essential for predicting the operation of an autonomous underwater vehicle (AUV) throughout the launch and recovery (LAR) process. This study performed an experiment to investigate wave characteristics between demi-hulls of the catamaran under regular and irregular waves at various wave directions and speeds. The experiment was designed to measure wave elevation in the inner region between demi-hulls in waves using ultrasonic sensors and capacitance wave probes. The experimental method and setting were verified exacting by comparing the motion response of the catamaran measured by the Qualisys system with numerical simulation using the 3D panel method. Then, wave elevation in the inner region was measured and analyzed to wave height which is a representative parameter of regular waves and to wave spectral density and significant wave height which are representative parameters for irregular waves. These parameters were compared to those of the ocean waves to understand influences of wavelengths, sea states, wave directions, and speeds on wave characteristics between demi-hulls of the catamaran. Plotting of difference from ocean waves indicated the discrepancy in wave characteristics among positions in the inner region, wavelengths, wave directions, sea states, and speeds compared to ocean waves. Results of wave characteristics in the inner region are valuable to analyze motion response and behavior of AUV during LAR process of unmanned surface vehicle (USV).

Review on the Hydro- and Thermo-Dynamic Wakes of Underwater Vehicles in Linearly Stratified Fluid

Wakes produced by underwater vehicles, particularly submarines, in density-stratified fluids play a pivotal role across military, academic, and engineering domains. In comparison to homogeneous fluid environments, wakes in stratified flows exhibit distinctive phenomena, including upstream blocking, pancake eddies, internal waves, and variations in hydrodynamic performance. These phenomena are crucial for optimizing the operation of underwater vehicles. This review critically assesses the hydrodynamic and thermodynamic aspects of these wakes through an integration of theoretical, experimental, and numerical approaches. The hydrodynamic wake evolution, comprising near-wake, non-equilibrium, and quasi-two-dimensional regimes, is scrutinized. The underlying physics, encompassing energy transformation, vertical motion suppression, and momentum dissipation, are analyzed in detail. Special emphasis is placed on numerical methods, encompassing diverse approaches and turbulence models and highlighting their differences in fidelity and computational cost. Numerical simulations not only provide insights into the intricate interplay among various factors but also emerge as a crucial focal point for future research directions. In the realm of thermodynamic wakes, we delve into the thermal wake induced by the discharge of high-temperature cooling water and the cold wake resulting from the stirring of seawater. The generation, evolution, and ascent to the free surface of these wakes are explored. Additionally, this review identifies and analyzes current research shortcomings in each aspect. By systematically addressing existing knowledge gaps, our study contributes novel insights that propel academic progress and bear significant implications for submarine engineering. This work not only enhances our understanding of the intricate dynamics involved but also provides a foundation for future research endeavors in this critical field.

Ultrasound-Powered Wireless Underwater Acoustic Identification Tags for Backscatter Communication.

Autonomous underwater vehicle (AUV) operations are limited by currently achievable underwater localization and navigation solutions; hence, the development of low-cost and passive (i.e., operable without an active power supply) acoustic underwater markers (or tags) can provide accurate localization information to AUVs improving their situational awareness, especially when operating in small scales or confined missions. This work presents an acoustic identification (AID) tag that can be powered wirelessly with ultrasonic power transfer from a remote acoustic source (e.g., mounted on an interrogating AUV) and provide localization information using backscatter communication. The AID tag harvests energy from the acoustic signal generated from the AUV and communicates by modulating the reflected signals from an embedded piezoelectric transducer. A scaled broadband AID tag prototype that achieves concurrent acoustic energy harvesting (tuned around 1.3 MHz) and backscatter communication (in wider frequency band 600 and 800 kHz) using frequency-domain multiplexing is implemented using a custom broadband impedance matching-based transducer design approach. During concurrent power and data operation, this prototype AID tag achieves data rates up to 200 kb/s using amplitude- and frequency-based modulation communication. The use of broadband schemes to achieve robust communications in low SNR (tested here down to -6 dB) is also demonstrated using linear frequency-modulated data carriers. Finally, the extension to full-scale devices of this AID tag concept and potential applications for short-range AUV routing and navigation such as homing and docking are discussed.

Model-free control of underwater vehicle-manipulator system interacting with unknown environments

The underwater vehicle-manipulator (UVM) interaction tasks require that UVM can adapt to different environments and have good stability properties. However, the complicated structures of UVM and uncertainties of the environment lead to poor interaction. In addition, transient performance related to overshoot and convergence is required during the interaction to ensure the safety of the UVM and environment. In this paper, an inner-loop/outer-loop control formulation is developed for underwater vehicle-manipulator interacting with environment, subject to unknown system and environment dynamic. For the inner-loop, an approximation-free state feedback controller is designed for UVM system without a prior knowledge of dynamics, capable of guaranteeing trajectory tracking with prescribed performance. A prescribed function is constructed, which allows the convergence time and convergence accuracy to be predetermined. Moreover, it is independent of the initial conditions. For the outer-loop, an off-policy reinforcement learning algorithm is presented to optimize the parameters of the admittance model without requiring the information of environment dynamic. The proposed admittance optimization is feasible for arbitrary desired trajectory. Finally, simulation and experimental results validate the effectiveness of the proposed model-free tracking controller.

Investigation of 3D Printed Underwater Thruster Propellers Using CFD and Structural Simulations

Unmanned Surface Vehicles (USV) and Autonomous or Remotely Operated Underwater vehicles (AUV, ROV) are developing and spreading rapidly in various industries. A common feature of these vehicles is that they are propelled by small plastic (or metal) propellers in most cases. Additive manufacturing can offer an excellent opportunity for rapid prototyping and the development of new models. This paper aims to investigate the fundamental aspects to be considered in the geometric design and manufacturing of small (diameter less than 100 mm) PLA (Polylactic acid) propellers 3D-printed using Fused Filament Fabrication (FFF) technology. In-service deformation of 3D-printed PLA ducted propellers with average geometry was investigated to determine the effect on the thrust and torque on the blades. For this purpose, one-directional FSI (Fluid Solid Interaction) simulations were performed using CFD (Computational Fluid Dynamics) and structural simulations. The propeller CAD geometries were generated using an in-house MATLAB script. The variable parameters of each version are the thickness, skew, and rake of the propeller blades. For the structural simulations, it was considered that the material properties of PLA parts printed with FFF technology depend on the print orientation. The results of the simulations show that except for extreme geometries (e.g., thin blades, skew, or rake more than 10°), the deformation of small PLA ducted propellers is not significant. CFD studies of the deformed geometries have shown that the resulting deformation has no significant effect on the thrust and torque of the propeller and thruster.

Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles

PurposeDocking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.Design/methodology/approachThis paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.FindingsThe authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.Originality/valueThis fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Experimental Analysis of Deep-Sea AUV Based on Multi-Sensor Integrated Navigation and Positioning

The operation of underwater vehicles in deep waters is a very challenging task. The use of AUVs (Autonomous Underwater Vehicles) is the preferred option for underwater exploration activities. They can be autonomously navigated and controlled in real time underwater, which is only possible with precise spatio-temporal information. Navigation and positioning systems based on LBL (Long-Baseline) or USBL (Ultra-Short-Baseline) systems have their own characteristics, so the choice of system is based on the specific application scenario. However, comparative experiments on AUV navigation and positioning under both systems are rarely conducted, especially in the deep sea. This study describes navigation and positioning experiments on AUVs in deep-sea scenarios and compares the accuracy of the USBL and LBL/SINS (Strap-Down Inertial Navigation System)/DVL (Doppler Velocity Log) modes. In practice, the accuracy of the USBL positioning mode is higher when the AUV is within a 60° observation range below the ship; when the AUV is far away from the ship, the positioning accuracy decreases with increasing range and observation angle, i.e., the positioning error reaches 80 m at 4000 m depth. The navigational accuracy inside and outside the datum array is high when using the LBL/SINS/DVL mode; if the AUV is far from the datum array when climbing to the surface, the LBL cannot provide accurate position calibration while the DVL fails, resulting in large deviations in the SINS results. In summary, the use of multi-sensor combination navigation schemes is beneficial, and accurate position information acquisition should be based on the demand and cost, while other factors should also be comprehensively considered; this paper proposes the use of the LBL/SINS/DVL system scheme.

Design of Software for Underwater Vehicles Operating in a Swarm

Design of Software for Underwater Vehicles Operating in a Swarm

A Control Architecture for Developing Reactive Hybrid Remotely Operated Underwater Vehicles

This article introduces a control architecture designed for the development of Hybrid Remotely Operated Underwater Vehicles. The term ”Hybrid” characterizes Remotely Operated systems capable of autonomously executing specific operations. The presented architecture maintains teleoperation capabilities while enabling two fully autonomous applications. The approach emphasizes the implementation of reactive navigation by exclusively utilizing data from a Mechanical Scanned Imaging Sonar for control decisions. This mandates the control system to solely react to data derived from the vehicle’s environment, without considering other positioning information or state estimation. The study involves transforming a small-scale commercial Remotely Operated Underwater Vehicle into a hybrid system without structural modifications, and details the development of an intermediate Operational Control Layer responsible for sensor data processing and task execution control. Two practical applications, inspired by tasks common in natural or open-water aquaculture farms, are explored: one for conducting transects, facilitating monitoring and maintenance operations, and another for navigating toward an object for inspection purposes. Experimental results validate the feasibility and effectiveness of the authors’ hypotheses. This approach expands the potential applications of underwater vehicles and facilitates the development of Hybrid Remotely Operated Underwater Vehicles, enabling the execution of autonomous reactive tasks.

Numerical Investigation on Hydrodynamic Characteristics and Drag Influence of an Open-Frame Remotely Operated Underwater Vehicle

Remotely operated underwater vehicles (ROVs) have been widely used in deep-sea resource exploitation and industrial engineering operations. To perform these tasks accurately in the deep-sea environment, stable motion control has become a key area of research on ROV systems, which has led to the importance of analyzing the hydrodynamic characteristic of ROVs. But a systematic methodology for analyzing the hydrodynamic characteristics of ROVs is still lacking nowadays. In this paper, systematic numerical simulation methods for analyzing hydrodynamic characteristics and shape optimization of a work-class ROV are conducted, and details of simulation procedures based on computational fluid dynamics are studied, which can be a foundation for robust controller design.

Unmotorize ROV gripper to catch profiling floats

This paper proposes an ROV (Remotely Operated underwater Vehicle) gripper for retrieving cylindrical profiling floats. In order to fit several types of ROVs and to be easily installed, the gripper is not motorized, using only the robot’s movements to catch the floats by simply moving forward on them. Then, the gripper uses the turnstile and freewheel concept to grasp and hold the float, with a safety release system to free an unwanted catch. The effectiveness of the clamp was tested in the pool and in the lake, with two actual rescues of lost floats. The limits of the methods are discussed.

Risk Analysis of Autonomous Underwater Vehicle Operation in a Polar Environment Based on Fuzzy Fault Tree Analysis

Autonomous underwater vehicles have long been used in marine explorations, and their application in recent polar expeditions is particularly noteworthy. However, the complexity and extreme conditions of the polar environment pose risks to the stable operation of autonomous underwater vehicles. This study adopted the methodology of fuzzy fault tree analysis to deeply analyze the operational risks of autonomous underwater vehicles in polar environments. While traditional fault tree analysis maps the causal relationships and probabilities between basic and intermediate events, fuzzy fault tree analysis models the uncertainty of data and determines the failure probability by integrating expert opinions. This study revealed that polar environment-induced failures play a more substantial role in autonomous underwater vehicle loss in polar regions than inherent system failures. The study identified ‘recovery failure’ and ‘poor communication’ as the major risk factors facing autonomous underwater vehicles in polar environments, exhibiting the highest failure probabilities. Specifically, among various polar environmental factors, ‘large ice concentration’, ‘ice thickness’, and ‘roughness of ice underside’ under ‘bad’ conditions were found to have a significant impact on the autonomous underwater vehicle’s failure probability. The fuzzy fault tree analysis method in this study successfully filled the gap created by the absence of historical data by effectively incorporating expert opinions, enabling a quantitative presentation of the impact of polar environments, which has been previously difficult to convey in qualitative terms.

Multi-Branch Dilation Convolution CenterNet for Object Detection of Underwater Vehicles

Object detection occupies a very important position in the fishing operation and autonomous navigation of underwater vehicles. At present, most deep-learning object detection approaches, such as R-CNN, SPPNet, R-FCN, etc., have two stages and are based on anchors. However, the previous methods generally have the problems of weak generalization ability and not high enough computational efficiency due to the generation of anchors. As a well-known one-stage anchor-free method, CenterNet can accelerate the inference speed by omitting the step of generating anchors, whereas it is difficult to extract sufficient global information because of the residual structure at the bottom layer, which leads to low detection precision for the overlapping targets. Dilation convolution makes the kernel obtain a larger reception field and access more information. Multi-branch structure can not only preserve the whole area information, but also efficiently separate foreground and background. By combining the dilation convolution and multi-branch structure, multi-branch dilation convolution is proposed and applied to the Hourglass backbone network in CenterNet, then an improved CenterNet named multi-branch dilation convolution CenterNet (MDC-CenterNet) is built, which has a stronger ability of object detection. The proposed method is successfully utilized for detection of underwater organisms including holothurian, scallop, echinus and starfish, and the comparison result shows that it outperforms the original CenterNet and the classical object detection network. Moreover, with the MS-COCO and PASCAL VOC datasets, a number of comparative experiments are performed for showing the advancement of our method compared to other best methods.

Machine learning transmission loss simulations in complex undersea environments with range-dependent bathymetry

Range-dependent bathymetric variability introduces diverse scattering and multipath that can impact optimal acoustic communication ranges, and will impact autonomous underwater vehicle (AUV) operations. We are working towards building a sound-aware framework in which the AUV has knowledge of the transmission environment and can exploit or react with intended outcomes. Prior knowledge of the environment coupled with acoustic propagation modeling on-board AUVs can improve sound-awareness of a vehicle but is computationally intensive, and costly from a power-budget perspective. This work addresses the computational burden by training a machine learning (ML) model to interpret range-dependent acoustic propagation through environmental inputs to predict transmission loss (TL) from the ray tracing model, BELLHOP. A decision tree model is trained to predict locations in range and depth of acceptable TL using feature representations to reduce bathymetric information within a region. Models are trained and tested with TL field realizations for acoustic communications at 10 and 25 kHz for environments with varying bathymetry at ranges up to 3 km through BELLHOP. We present simulations and results from field experiments performed off the coast of Southern California. The communication paths are between a surface vehicle (Liquid Robotics Waveglider, Herdon, VA) and an AUV (REMUS UUV, Huntington Ingalls, Falmouth, MA). Results for model predictions and collected field data will be discussed.

An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters

Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.

Design and Fabrication of a Low-Cost Multi-Purpose Underwater Remotely Operated Vehicle

Remotely Operated Underwater Vehicles (ROVs) are emerging potential technology for ocean research, environmental and geochemical studies, mine hunting, surveillance, and commercial usage. Due to their affordability, simplicity in handling and deployment, appropriateness for deep-sea diving, increased mobility, and ability to operate in harsh settings, underwater vehicles—both autonomous and remotely controlled—have grown in popularity. The goal is to develop and test a remotely operated underwater vehicle (ROV) that is lightweight, affordable, and capable of conducting surveys in shallow waters. The design, production, and experimental findings of such an ROV are the focus of this paper. The mechanical design, thruster characteristics, electrical systems, and software architecture of the graphical user interface (GUI) for the proposed vehicle are all thoroughly explained. In contrast to autonomous underwater vehicles, the planned ROV is totally controlled from the base station using a live camera feed and other sensors (AUV). In contrast to the majority of AUVs, it provides real-time results from its various sensors and takes human commands via two-way communication. Comprehensively addressed, the results and answers are provided. Constraints on the mechanical design’s weight and portability, electronics’ power requirements, and the length of the umbilical cord are all addressed. It is also explored how to strengthen the paradigm and make it a for-profit enterprise with more improvements.

A novel RBFNN-UKF-based SOC estimator for automatic underwater vehicles considering a temperature compensation strategy

Accurate state of charge (SOC) estimation of batteries is a prerequisite for the reliable operation of automatic underwater vehicles. Currently, the accuracy of traditional SOC evaluation algorithms deteriorates significantly at low temperatures and low SOCs. Hence, a novel SOC estimator is proposed in this study, consisting of three efforts. Firstly, a new radial basis function neural network (RBFNN) battery model is built to replace the equivalent circuit model (ECM) for SOC estimation. Then, based on the relation between SOC and terminal voltage at a different temperature, a temperature compensation strategy is developed, which is an effortless operation and does not increase the computational burden. Finally, incorporating the new battery model, the temperature compensation strategy, and the unscented Kalman filter (UKF), a novel SOC estimation frame expressed as RBFNN-UKF is designed.The performance of the proposed method, including accuracy, generalization ability, and low-temperature adaptation, is evaluated systematically based on a publicly available dataset, where the inaccurate initial value and the current errors are added in each case. The results show that: (1) The SOC estimation curve of RBFNN-UKF can converge quickly to the reference curve and maintain good consistency even at low SOCs; (2) The proposed method exhibits excellent generalization capability for different dynamic cycles; (3) At low temperatures, the SOC estimation error of the RBFNN-UKF is reduced to 17 % of traditional ECM-UKF algorithm with the recursive least squares parameter identification method. The above results indicate that the proposed RBFNN-UKF-based SOC estimator has a high application value for AUVs and other vehicles working in complex environments.

Evaluation of autonomous underwater vehicle motion trajectory optimization algorithms

The operation of autonomous underwater vehicles (AUVs) relies on three major motions: yaw, theta, and depth, each requiring its own set of proportional integral derivative (PID) controller gain criteria. Thus, different issues arise, including the availability of multiple criteria for optimization algorithm evaluation, the importance of these criteria, the trade-off between criterion performance, and criterion critical values. These issues make the evaluation of optimization algorithms for AUV motion control a complex multicriteria decision-making (MCDM) problem. This research proposes a novel selection-integrated approach for AUV optimization algorithms in different motions using two MCDM methods: fuzzy-weighted zero-inconsistency (FWZIC) for criteria weighting and fuzzy decision by opinion score method (FDOSM) for optimization algorithm selection. The approach comprises three main phases: development of PID, FWZIC-based criteria weighting, and FDOSM-based optimization algorithm selection. In all three motion types – “depth”, “yaw”, and “theta” – Kp_θ had the highest weight value, with respective weights of 0.143, 0.149, and 0.142. In contrast, Ki_depth consistently received the lowest weight value across all three motion types, with respective weights of 0.057, 0.0598, and 0.057. Regarding the “depth” motion, the Archimedes optimization algorithm (AOA) was the highest-performing alternative with a score of 0.077, while the eagle strategy–particle swarm optimization algorithm was the worst alternative with a score of 0.022. The Cuckoo optimization algorithm was identified as the best alternative for the “yaw” motion with a score of 0.072, whereas black hole optimization had the lowest score of 0.036. The best alternative for the “theta” motion was AOA with a score of 0.067, and the worst algorithm was sunflower optimization with a score of 0.028. This developed approach was evaluated based on systematic ranking and sensitivity analysis, which confirmed the validity of the proposed work.

Underwater Object Detection Algorithm Based on Adding Channel and Spatial Fusion Attention Mechanism

Underwater target detection is the foundation and guarantee for the autonomous operation of underwater vehicles and is one of the key technologies in marine exploration. Due to the complex and special underwater environment, the detection effect is poor, and the detection precision is not high. In this paper, YOLOv5 (You Only Look Once v5) is used as the overall structural framework of the target detection algorithm, and improvement is made on the basis of its detection precision in the underwater environment. Specifically, an attention mechanism (Channel and Spatial Fusion Attention, CSFA) that fuses the channel attention and spatial attention is proposed and added to the YOLOv5 network framework, enabling the network to focus on both the prominent features of the detected object and the spatial information of the detected object. The proposed method was tested on the underwater target detection dataset provided by the China Underwater Robot Professional Competition. The experimental detection precision (P) reached 85%, the recall (R) reached 82.2%, and the mean average precision (mAP) reached 87.5%. The effectiveness of the proposed method was verified, and its underwater target detection performance was better than that of ordinary models.

Online risk modeling of autonomous marine systems: Case study of autonomous operations under sea ice

Since few or no human operators are directly involved in the operation of an autonomous marine system (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision making. The current study combines the system-theoretic process analysis (STPA) with Bayesian belief networks (BBNs) to develop online risk models for an AMS. Furthermore, fuzzy discretization is introduced to deal with evidence uncertainty. The proposed risk model can update the risk level as the operating conditions change, providing a basis for AMS supervisory risk control (SRC). A two-level SRC is proposed in this study. Using the operation of an autonomous underwater vehicle (AUV) under sea ice as an example, the current work presents an online risk model and a corresponding SRC system, focusing on the navigation hazards to the AUV and its potential loss. The results of simulation studies show that the model enables the AUV to be informed of the risk level and to make risk-based decisions accordingly, thereby improving its intelligence. The importance of the evidence uncertainty in online risk models and SRC is analyzed and discussed. The results and conclusions of this analysis can be adapted to other AMSs.