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Spatial-temporal features are extracted from X-band marine radar backscatter image sequences via deep neural networks to estimate sea surface significant wave heights (SWHs). A convolutional neural network (CNN) is first constructed based on the pretrained GoogLeNet to estimate SWH using multiscale deep spatial features extracted from each radar image. Since the CNN-based model cannot analyze the temporal behavior of wave signatures in radar image sequences, a gated recurrent unit (GRU) network is concatenated after the deep convolutional layers from the CNN to build a convolutional GRU (CGRU)-based model, which generates spatial-temporal features for SWH estimation. Both the CNN and CGRU-based models are trained and tested using shipborne marine radar data collected during a sea trial off the East Coast of Canada, while simultaneous SWHs measured by nearby buoys are used as ground truths for model training and reference. Experimental results show that compared to the classic signal-to-noise ratio (SNR)-based method, both models improve estimation accuracy and computational efficiency significantly, with a reduction of RMSD by 0.32 m (CNN) and 0.35 m (CGRU), respectively. It is also found that under rainy conditions, CGRU outperforms SNR and CNN-based models by reducing the RMSD from around 0.90 to 0.54 m.

Photometric stereo is a widely used optical 3-D reconstruction method, which has advantages for high-resolution and well-detailed 3-D reconstruction even in weak texture regions. However, in underwater photometric stereo, the light refraction and attenuation using the camera and point light sources usually result in significant errors in shape recovery. In this article, we present a novel solution to address these challenges for improving the performance of underwater photometric stereo by combining it with underwater laser triangulation. First, we employ underwater laser triangulation as references to correct the global surface-shape distortion caused by nonuniform close-range illumination. Second, we propose to use an underwater camera refractive model to eliminate nonlinear refraction distortion. Third, we build a device implementing the proposed method for 3-D seabed reconstruction in on-site sea trials. Experimental results demonstrate that the proposed approach is able to produce accurate 3-D reconstruction results in the underwater environment.

Underwater acoustic localization (UWAL) is widely applied in ocean exploration and a key issue for the high rate time of arrival (TOA) based UWAL method is to select the direct signal in the multi-path acoustic channel. However, the existing direct signal selection methods suffer from a deteriorate of the selection accuracy since the received signals from adjacent periods are mixed with each other. Addressing the problem, we propose a high rate direct signal selection algorithm in this paper. First, we choose the amplitude, TOA, bandwidth, and Doppler frequency of the detected pulses as the input features. Secondly, a direct signal classifier is established utilizing the decision tree. Finally, the localization model is built based on the TDOA information of the direct signal, and the localization precision analysis evaluates the performance of the UWAL method. Sufficient experiments including simulation, tank experiment, and sea trial are implemented to verify the effectiveness of the proposed method.

Underwater autonomous navigation technology is a key link for AUV to complete various underwater missions successfully. In this paper, we have proposed a position correction model based on hybrid gated RNN, which does not need to establish a motion model like typical navigation algorithms, to avoid modeling errors in the navigation process. According to the low update rate of GPS and the slip data caused by the coverage of GPS antenna during the process of floating and diving, it does not meet the actual requirements of navigation and control. We proposed to use improved EKF and iSAM as the output of the training dataset to filter out GPS slips, then the position error is corrected at the same time. First, the normalized input sequence is fed to the two-layer hybrid gated RNN with BiLSTM output mode as sequence and LSTM output mode as last, and then the processed data is fed to the two fully connected layers with a dropout layer added in the middle. After that, the output of the position correction model can be obtained through the regression layer. To confirm the validity of the proposed model, we conducted sea trials to compare the proposed model with EKF and iSAM. The experimental results prove that the proposed position correction model performs better than EKF and iSAM in all aspects, and the correction model trained with iSAM is more effective than that trained with EKF.

The article discusses the main methodologies and key technologies behind the ROBUST project, funded by the European Union within the Horizon 2020 framework, whose goal is the development of an autonomous underwater vehicle manipulator system for deep sea mining exploration. This article focuses on the developed control and perception framework and related mechatronics, and presents the experimental results from the sea trials in Sardinia, Italy, supporting the proposed methodologies.

This work indicates two novel ocean current observation methods that based on a glider type mobile platform. Glider with current meters are ideal current observation solutions for marine scientists since they have merits of large dynamic area and low power consumption, but their measurement accuracy is insufficient now. The proposed 1st solution, single point current meter compensated inversion method (SPCMCIM), is a revolution of the conventional inverse method by solving the acoustic Doppler to current profiler (AD2CP) induced turbulence problem. It’s a cooperated AD2CP and single point current meter (SPCM) algorithm that compensates the contaminated first AD2CP layer data. The physical position of the SPCM is also optimized according to a finite element analysis (FEA) study to minimize the interference generated by the SPCM. The 2nd method is solving the area restriction problems that AD2CP can just apply to non-pure water environment namely single point and inertial measurement inversion method (SPIMIM). It utilizes the SPCM as the only current observation source and takes advantages of physical properties of ocean currents. The currents are constitute of stable layers and each of them has a stable velocity, so the glider’s velocity variation can be obtained by reading the SPCM. The velocity variation in the transition process between layers can be acquired by the low cost inertial sensors. Finally, these theoretical studies were verified in an onsite sea trial. The SPCM and AD2CP was mounted to an underwater glider to measure the currents and a ship equipped AD2CP was used to provide the arbitrary data. Both of the proposed method can effectively measure the current velocity and induced significant enhancement than the classical inversion method. In particular, the first method conducted improvement of 70% accuracy by average. The 2nd one also shows an obvious improvement and has the advantage to deploy to high purity water area like deep lakes or north pole.

Doppler Velocity Logs (DVL) is an important information source for underwater vehicle navigation because it can provide accurate velocity information. As the main system, SINS/DVL integrated navigation is widely used in the underwater vehicle. However, DVL scale factor error, and misalignment with inertial measurement unit (IMU) are the two sources of integration inaccuracy. A novel calibration method aided GNSS is proposed in this paper, which is designed based on velocity observation and position observation respectively. The proposed method combines the Quasi-Newton method and Quaternions method, and we name it Quasi-Newton Quaternion (QNQ) calibration method. In order to verify the performance of the proposed method, another calibration method based on the Kalman filter is introduced in simulation, vehicle tests, and Sea trial. The test results indicate that the proposed calibration method can perform a good estimation of DVL error.

One of the challenges of Internet of Underwater Things (IoUT) is the design of nodes for collecting information from underwater, with features of low cost, long-term, long range, voyage routing, and real-time communications. Wave glider, has shown great potential acting as IoUT nodes through its persistent, long range travelling, and flexibility underwater. In this paper, we propose an architecture of Internet of Underwater Things, involving multiple wave gliders as nodes for acoustical observation. We present target localisation method via acoustical observation of nodes with multiple wave gliders deployed underwater, by which precision of bearing estimation of each node is required to achieve high precision of localisation. With the data collected, we apply a compensation method of bearing estimate when the hydrophone array is rotating during the observation. The feasibility of acoustical observation of wave gliders has been validated through both simulation and sea trial data, which is of great potential to be nodes for constructing IoUT.

A fundamental property of most sea conditions is that sets of large waves alternate with sets of smaller waves, known as quiescent periods. Exploiting the quiescent periods can expand the sea states under which the execution of many maritime operations can be considered safe. Building upon the work of Belmont et al. , we extend the methodology to fully describe the statistical distribution of the quiescent periods in terms of their time duration using sea spectra derived from current ship readings as well as metocean forecast. This provides the basis for a short- or long-term planning tool that complements deterministic wave prediction technology for executing wave-height-limited maritime operations. The method is evaluated using spectra calculated from field measurements in a dedicated sea trial and from standard parameterized oceanographic forms (JONSWAP and Pearson–Moskowitz).

Fronts between Arctic- and Atlantic-origin waters are characterized by strong lateral gradients in temperature and salinity. Ocean processes associated with fronts are complex with considerable space and time variability. Therefore, resolving the processes in frontal zones by observation is challenging but important for understanding the associated physical–biological interactions and their impact on the marine ecosystem. The use of autonomous robotic vehicles and in situ data-driven sampling can help improve and augment the traditional sampling practices, such as ships and profiling instruments. Here, we present the development and results of using an autonomous agent for detection and sampling of an Arctic front, integrated on board an autonomous underwater vehicle. The agent is based on a subsumption architecture implemented as behaviors in a finite-state machine. Once a front is detected, the front tracking behavior uses observations to continuously adapt the path of the vehicle to perform transects across the front interface. Following successful sea trials in the Trondheimsfjord, the front-tracking agent was deployed to perform a full-scale mission near 82 $^{\circ}$ N north of Svalbard, close to the sea ice edge. The agent was able to detect and track an Arctic frontal feature, performing a total of six crossings while collecting vertical profiles in the upper 90 m of the water column. Measurements yield a detailed volumetric description of the frontal feature with high resolution along the frontal zone, augmenting ship-based sampling that was run in parallel.

Small-scale ocean observations play an important role in oceanographic research. The station-keeping method based on the underwater mobile platform has been proposed as a means of small-scale ocean observation with the advantages of low cost and high flexibility. However, few studies have carried out complete modeling of the small-scale ocean observation process of mobile platforms and proposed a corresponding station-keeping method based on this. In this article, a hybrid underwater profiler (HUP) was tested under two working conditions for its ability of maintaining position during designated area persistent monitoring (DAPM) within a specific area of the sea over a long period. The DAPM problem is modeled and the cost function that evaluates the performance of DAPM is defined. If the underwater profiler cannot measure data about underwater currents, it can maintain its position by setting constant gliding and yaw angles based on the depth-averaged current measured each time it surfaces. If it has instruments to observe ocean currents at each depth during a dive, the profiler can maintain its position using a strategy based on a quasi-Newton method. Simulation results show that the gliding strategies effectively assist the HUP in monitoring a designated area. Additionally, sea trials were performed without instrumentation to measure ocean currents and the effectiveness of the simpler gliding strategy was clearly demonstrated.

Various methods have been developed and used for monitoring marine benthic habitats, such as coral reefs and seagrass meadows. However, the efficiency of general survey methods [e.g., line intercept transects and autonomous underwater vehicles (AUVs)] still is not high. In this article, we propose a practical coral-coverage estimation method combining an effective survey system [Speedy Sea Scanner (SSS)] and a deep-learning-based estimation method. The SSS is a towed-type system with six cameras arrayed on the platform. The depth rating of the system in our trial was 50 m. The length of the array baseline was 4.4 m, and six cameras were placed on the platform with equal spacing. The sea trial was conducted at Kujuku-Shima, Japan, on September 30, 2017. We successfully generated 3-D models and high-quality orthophotos of the seafloor with high resolution of about 1.5 mm/pixel. The survey efficiency of the SSS was about 7000 m2/h. In addition, the experimental results of coral-coverage estimation showed that the corals can be distinguished with accuracy of about 80% in places with relatively high transparency, and the error of coverage estimation was 10% or less. The proposed coral-coverage estimation method is more efficient than other survey techniques and costs less than AUV surveying; therefore, it is expected to become a promising tool for marine environmental surveying.

The prediction of water current (WC) is key in understanding oceanographic phenomena, and serves as a corner stone for maritime applications. While numerical models are capable to characterize the main phenomenon effecting the current, a good estimate of the current’s velocity requires accurate environmental information such as topographic maps, temperature, and wind velocity. Without such information, common practice involves deploying a large number of drifting devices to measure the WC in good resolution. In this paper, we purpose a statistical approach to predict the WC’s velocity field, namely, the relation between the drifters’ locations and their drifting velocity to estimate the velocity of the WC across a given environment. We offer two statistical approaches. Assuming the above relation is linear, the first uses a weighted least squares to estimate the parameters of the velocity field. The second uses a support vector regression to predict the WC by a classifier. The first approach is more traceable, while the second can manage a non-linear space-speed relation of the WC. Results are studied numerically using an WC model, and experimentally from a sea trial performed across the shores of San Diego including 13 acoustically localized submerged drifters. In both cases, performances show a good agreement between the ground truth of the WC and the predicted one.

In this paper, a sliding mode-based guidance strategy is proposed for the control of an autonomous vehicle. The aim of the autonomous vehicle deployment is the study of unknown environmental spatial features. The proposed approach allows the solution of both boundary tracking and source-seeking problems with a single autonomous vehicle capable of sensing the value of the spatial field at its position. The movement of the vehicle is controlled through the proposed guidance strategy, which is designed on the basis of the collected measurements without the necessity of preplanning or human intervention. Moreover, no a priori knowledge about the field and its gradient is required. The proposed strategy is based on the so-called suboptimal sliding mode controller. The guidance strategy is demonstrated by computer-based simulations and a set of boundary tracking experimental sea trials. The efficacy of the algorithm to autonomously steer the C-Enduro surface vehicle to follow a fixed depth contour in a dynamic coastal region is demonstrated by the results from the trial described in this paper.

The study of extending navigation range of autonomous underwater vehicles (AUVs) is popular for ocean exploration. A common method is to use the underwater docking station to recover and recharge AUVs. This paper uses the simple but effective inductive power transmission (IPT) system to realize underwater wireless charging for an AUV docking system. A design procedure is provided to select the optimal underwater operating frequency of the IPT system in consideration of eddy-current loss in seawater. To solve the power failure problem of the control power system, an inrush current limiter (ICL) is proposed on the basis of the dynamic model of the IPT system. The test results in the laboratory experiment and sea trial both verify the advantages of the frequency design method and the effectiveness of the ICL.

Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.

Using underwater gliders to measure underwater acoustic signals is a new measurement method emerging with the development of platform technology. Although underwater gliders are relatively quiet due to the absence of propellers, vehicle noise generated during motion is still inevitable, which will affect the recorded acoustic data. In this paper, we analyze the self-noise characteristics of underwater gliders based on simulated data by CFD technology for the hydrodynamic flow noise and experimental data acquired in an anechoic-water-tank experiment for the mechanical noise. The mechanical noise covers noises generated by buoyancy regulating (increasing and reducing), pitch regulating, rudder regulating and CTD pump working. According to the analysis results, the flow noise and CTD pump working noise could be ignored for the experimental data processing of sea trials. An experiment was conducted with an acoustic Sea-Wing underwater glider in the South China Sea from July 31 to September 4, 2018. Two kinds of noisy data were recorded, including target signals and ambient noise. All the target signals could be recognized after convolution filtering, except during the buoyancy regulating periods due to the high noise spectrum level. For the recorded ambient noise, in addition to the buoyancy regulating noise, the rudder and pitch regulating noises affected the recorded data. Then based on the acquired knowledge, a joint convolution filtering and thresholding method is proposed to remove the rudder and pitch noises from recorded noisy data. Kernels extracted from data acquired in the anechoic-water-tank experiment are used in the convolution filtering to localize each regulating action and energy thresholding is adopted to determine the duration of each regulation. All the rudder and pitch noises are removed from the recorded noisy data.

The unmanned wave glider (UWG) is a new kind of wave-powered unmanned marine vehicle for persistent marine environment monitoring. However, the course control of UWG is unusual due to the special multijoint structure and special operating principle. Control methods for single body marine vehicles cannot guarantee the control performance of UWG. In this paper, first, we propose the dynamic linearized model of UWG's multiheading system to describe the interrelation of Float's heading, Glider's heading, and system course. The dynamic linearized model is real-time corrected by dynamic I/O data of the controlled system in the sailing process of UWG. Then, the heading information fusion strategy is proposed to calculate the desired heading of Glider. Finally, via direct heading control of Glider, the course control of UWG is realized indirectly. Aiming at the “Ocean Rambler” UWG, simulation experiments and sea trials in Qingdao sea area are carried out. Simulation and sea trial results demonstrate that the proposed dynamic linearized model of UWG's multiheading system and heading information fusion strategy improve the course control performance of UWG significantly.

Recent advances in transducer and computing technology have pushed the concept of continuous active sonar (CAS) or high duty cycle sonar as an area of interest for application to antisubmarine warfare. Unlike conventional pulsed active sonars, CAS processing aims at detecting echoes while transmitting with a nearly 100% duty cycle. This paper describes the signal processing chain developed at the Centre for Maritime Research and Experimentation (CMRE), La Spezia, Italy, for real-time CAS processing within an autonomous networked multistatic sonar system. The algorithm uses subband processing, which can potentially provide a higher target update rate than the traditional pulsed active sonar, while maintaining the same search radius. The higher rate of contact information can improve target tracking performance. Performance results are given from the COLLAB–NGAS14 sea trial where the CAS processor was deployed on two autonomous underwater vehicles (AUVs) acting as receivers of the CMRE experimental multistatic demonstrator. Results show the feasibility of the CAS concept in littoral scenarios, using AUVs as real-time receivers.

In the development of ocean energy technologies, full-scale sea trials have proven technically challenging and expensive. As a contribution to the development of flexible, reliable, and affordable measurement systems for such sea trials, this paper presents and evaluates a portable data acquisition system. The system offers a cheap and flexible option for when and where signal infrastructure at site is not available. It is battery powered and consists of a sensor system and a logger unit. The sensor system is placed inside the object of study, which for this application is a wave power generator standing on the seafloor. The logger unit, which contains a logger with integrated data storage and a battery, is placed outside the object of study in a submersible and retrievable vessel. Sensor output is carried with 4–20-mA current signals between the sensor system and the logger, which makes the system directly compatible with a wide range of industrial sensors. For the specific setup implemented in this paper, the sensor system was used to measure voltage and current inside the generator. Beyond this, the system is designed to be adaptable for a wider range of sensors, with 16 individual signals and a sampling frequency up to 1 kHz. The custom logger is programmable and offers many options to apply different logging schedules and to limit the sensor system power supply accordingly. Evaluations show that the implemented system offers 5–22 weeks of 1-kHz and 16-b monitoring of 16 signals, depending on a chosen logging schedule. Suggestions are also given for how to raise the operation time up to 50 weeks. The system has been tested offshore for the collection of power production data from two wave energy converters, with good results.

Fiber-optic interferometric sensing is a very attractive approach for creating lightweight, high-sensitive, and fully passive fiber-optic hydrophone arrays. In this paper, results of sea trials of the thin cable fiber-optic hydrophone array for passive acoustic surveillance applications are presented. The performed trials consisted of two parts: the first part was devoted to the estimation of the array acoustic performance and the second part included the direction of arrival (DOA) estimation of the moving acoustic source. The estimation of the array acoustic performance showed that the average measured pressure sensitivity of fiber-optic hydrophones increased as the acoustic frequency increased from −122 dB re rad/ $ {\mu }$ Pa at 20 Hz to −116 dB re rad/ ${\mu }$ Pa at 495 Hz. The average minimum detectable pressure of hydrophones decreased from 78, 1 dB re $1~{\mu }$ Pa at 20 Hz to 28, 5 dB re $1~{\mu }$ Pa at 495 Hz. Obtained values of the minimum detectable pressure were below the sea state zero noise level in the considered frequency range. The average dynamic range of fiber-optic hydrophones was above 120 dB at 495 Hz. The proper capability of the DOA estimation was successfully demonstrated using the motor boat as the moving acoustic source. Trajectories of the moving boat were obtained by using the beamscan DOA estimation algorithm with additional Kalman filtering. Obtained results demonstrated the great potential of using fiber-optic interferometric sensors for hydroacoustic surveillance applications. The presented results might be helpful for the implementation of extended fiber-optic hydroacoustic surveillance systems.

Prediction of the dynamic behavior of autonomous underwater vehicles (AUVs) is the basis for developing the control system and optimizing the hydrodynamic shape of AUVs. A fast and efficient prediction method of dynamic behavior of AUVs was developed with the secondary programming of a commercial computational fluid dynamics (CFD) code of FLUENT. In the proposed CFD method, mass properties of the AUV and thrust of the propeller were coupled to the 6-degree-of-freedom (DOF) dynamic equations of AUV in the form of user defined functions. A subregional unstructured mixed meshing strategy combining static meshes and dynamic meshes was proposed to improve the computational efficiency and to realize the 6-DOF movement of AUV. The proposed CFD prediction method can predict not only the dynamic behavior of the AUV, but also its surrounding flow field. Dynamic behavior of a seafloor mapping AUV in straight running and turn running were predicted using the developed CFD strategy. Comparison between the prediction results from the CFD method and those from the sea trials proved the proposed method to be accurate and effective. Some measures were put forward to improve motion performance of the vehicle according to the prediction results.

The current manual spectra acquisition for monitoring water constituents has resulted in discontinuous data acquisition, insufficient amount of data, and small ocean coverage. This article presents the design of a novel fully automatic ocean spectra acquisition and control system based on the real-time solar angle analyzing and tracking. To ensure that the requirements for spectra acquisition are met, the system is capable of accurately calculating the solar angle by collecting the information of latitude, longitude, date, time and direction, and automatically adjusting the position of instrument observation plane and the pointing angle of fiber optic probe in real-time. It achieves full automation of collecting the downward radiance of skylight, the upward radiance from reference panel and seawater separately through controlling the rotation of fiber optic probe. A 188-day observation experiment was carried out at the coastal ocean experimental station in Qingdao from September 11, 2018 to March 17, 2019. After that, the system was conducted onboard the Dongfanghong 3 scientific research vessel for a one-month demonstration and sea trial in June 2019. Comparative experiments including manual spectra collection, chlorophyll-a sensor measurement, and water samples collection were carried out. The experimental results show that the relative error of the spectra between the system and manual collection is less than 5%, and the relative error of the remote sensing reflectance calculated by the spectra is less than 4%. Considering the chlorophyll-a concentration obtained by the sensor and the water samples as the true value, the relative error of the chlorophyll-a concentration obtained by the system is 10% and 25% respectively. The results show its full and reliable capacity in collecting spectra of seawater automatically and continuously in real-time, with satisfactory accuracy and timeliness.

A ship roll prediction scheme is proposed using an adaptive sliding data window (SDW), which is designed to represent time-varying nonlinear dynamics of ship roll motion. The adjustment of SDW is realized by developing an improved fuzzy Gath-Geva (IFGG) segmentation approach, which detects the changes of system dynamics and thereby automatically adapting the scale of SDW. By virtue of the learning scheme with an adaptive SDW, the variable-structure radial basis function network is constructed sequentially to online predict ship roll dynamics. Experimental studies on online ship roll prediction are conducted on measured data from YuKun ’s full-scale sea trial. Results demonstrate the remarkable predictive accuracy of the proposed ship roll prediction model as well as the effectiveness of the IFGG-based SDW in terms of representing time-varying dynamics.

A new pair of optical wireless modems has been realized, which exploit visible light communication to transmit Ethernet signals through water. The modem prototypes were finally tested in sea waters at La Spezia harbor; they successfully transmitted 10 Mbit/s 10Base-T signals over up to 10 m, notwithstanding the high turbidity and the strong sunlight. Final tests included the integration with SUNRISE testbed and the use with a moving robot, remotely operated. Commercial components were used to realize the modems; thus, we expect that the key design concepts can be used as a starting point for practical deployment of this technology.

A gradual change of the pitch angle was observed for a glider in a sea trial in August 2014. It took place when the glider moved in both upward and downward glides between the sea surface and a depth of 1200 m and there was neither any internal mass movement nor buoyancy engine regulation in the steady glide. In this paper, this phenomenon is validated via a nonlinear dynamic model, which is restricted to the vertical plane. The model is based on Kirchhoff's equations of motion for a submerged rigid body and takes the seawater pressure, temperature, and density, and the deformation of the pressure hull of the glider into consideration. While the variations of aforementioned four parameters influence many physical parameters, including added mass, buoyancy, center of buoyancy (CB), hydrodynamic forces, and torques, their effects on glide velocity appear to be the primary cause for the gradual change of pitch angle observed. To minimize the gradual change of pitch angle, two effective approaches are proposed and validated, namely increasing the lift/drag ratio and decreasing the hydrodynamic torque coefficients.

This paper is focused on improving the self-awareness of autonomous underwater vehicles (AUVs) operating in unknown environments. A runtime estimation framework is introduced to derive energy usage and navigation performance metrics in the presence of external disturbances, such as slowly varying sea currents. These are calculated by a state-of-the-art nonlinear regression algorithm (LWPR) using measurements commonly available on-board modern AUVs without relying on external sensors or a priori knowledge about the environment. The proposed framework is validated on two vehicles, an IVER3 AUV and a Nessie VII AUV, in the context of real sea trials with no modification required for the AUVs or their missions. Derived metrics are used to estimate the feasibility of underwater missions employing the concept of probability of mission completion (PoMC). If environmental effects modify the vehicle's effectiveness, a mission plan update is performed. This is based on an energy-aware route optimization algorithm that is also introduced in the paper. This algorithm, known as energy-aware orienteering problem (EA-OP), shows a practical usage for the runtime metrics. It allows an AUV to optimize its navigation and to maximize its mission's outcome according to measured performances. Simulation results are also presented for inspection scenarios. These show average improvements of 5%–20% for the mission's outcome when using the proposed strategy in the presence of environmental disturbances.

The Target and Reverberation EXperiment 2013 (TREX13) included a comprehensive reverberation field project in the frequency band of 2–10 kHz, and was carried out off the coast of Panama City, FL, USA, from April 21 to May 17, 2013. A spatially fixed transmit and receive acoustic system was used to measure reverberation over time under diverse environmental conditions, allowing study of reverberation level (RL) dependence on bottom composition, sea surface conditions, and water column properties. Extensive in situ measurements, including a multibeam bathymetric survey, chirp sonar subbottom profiling, gravity/diver cores, sediment sound speed and attenuation, interface roughness, wind-generated sea surface waves, and water column properties, were made to support studies of environmental effects on RL. Beamformed RL data are categorized to facilitate studies emphasizing physical mechanisms of 1) bottom reverberation; 2) sea surface impact; and 3) biological impact. This paper is an overview of RL over the entire sea trial, intending to summarize major observations and provide both a road map and suitable data sets for follow-up efforts on model/data comparisons. Emphasis is placed on the dependence of RL on local geoacoustic properties and sea surface conditions.

The integration of inaccurate remotely operated vehicle (ROV) model data obtained by computational fluid dynamics for control is presented. Since the ROV is highly nonlinear and uncertain, a sliding-mode control (SMC) system using a direction-based genetic algorithm (GA) and fuzzy inference mechanism is proposed. The GA influences the right evolutionary step and direction of the SMC parameters subjected to uncertainties in the evolutionary process. The effectiveness of reducing the sensitivity of the proposed control scheme to model parameters and external disturbance is verified by simulations and sea trial. The results demonstrate that the proposed controller performed better with less chattering in position responses than SMC without GA-fuzzy optimization, fuzzy logic controller, and proportional-integral derivative.

A crucial issue that cannot be ignored in deep-sea resource surveys and scientific research aqfd 3is underwater positioning and navigation of deep-sea manned submersibles. Ultra-short baseline (USBL) is an important technical method for performing underwater positioning and tracking of manned submersibles and ensuring the safety of their underwater operations. Using USBL, the mothership can obtain the manned submersible’s position in real time. Furthermore, the manned submersible can obtain its own position at longer time intervals using digital underwater acoustic communication to assist the pilot in driving. Long baseline (LBL) is a significant supplement to USBL because it is an independent positioning system that provides more accurate and real-time navigation and positioning data for manned submersibles in large areas and deep seas. In this paper, we analyzed USBL sea-trial calibration, its application in real diving, and details of LBL sea-trial using the Jiaolong deep-sea manned submersible as a case study. Data measured during 10 consecutive dives of the Jiaolong manned submersible are utilized to analyze the USBL positioning performance. We found that the positioning performance (relative slant range error, relative depth error, and data efficiency) is consistent with calibration results. Finally, a rectangular path with a length of 900 m and breadth of 300 m is designed to verify LBL performance in deep sea. The LBL sea-trial results show that the positioning accuracy of LBL is better than that of USBL. The USBL positioning performance is affected by the distance from the mothership and its heading.

Reliable SONAR-performance prediction in shallow water requires knowledge of the seabed reflectivity, or its geoacoustic properties, which is expensive and difficult to acquire in situ . This paper illustrates two sea trials conducted in different shallow water areas to investigate the feasibility of acquiring such knowledge efficiently from measurements of naturally occurring ambient noise by an array that is compact enough to be mounted on a small autonomous underwater vehicle. The system relies on a previous technique for passively estimating the bottom reflection loss from the acoustic noise field generated by wind and breaking waves at the sea surface. Results from these experiments supported by numerical modeling are presented and compared with independent measurements of the relevant seabed reflectivity properties. The results obtained from both experiments demonstrate the potential of using autonomous underwater vehicles for seabed characterization.

This paper presents the design, test, and experimentation at sea of four JANUS-based services for operationally relevant underwater applications: 1) first contact and language switching; 2) transmission of automatic identification system data to submerged assets; 3) transmission of meteorological and oceanographic data to underwater vessels; and 4) support in distressed submarine operations. On March 24, 2017, JANUS was promulgated as a NATO standard (STANAG 4748) [1] , marking the first time that a digital acoustic communications protocol is adopted at international level. JANUS is an open, simple, and robust modulation and coding scheme developed by the NATO STO Centre for Maritime Research and Experimentation, in collaboration with academia and industry. The implementation of the services presented in this paper is based on a standardized protocol and offers the potential to widely increase the safety and efficiency in maritime operations. The objective of this paper is to demonstrate that JANUS can be used in support of maritime operations, potentially increasing their efficiency. Such demonstration is achieved through experimentation at sea of four operationally relevant JANUS-based services. The four JANUS-based services discussed were validated during two different sea trials: REP15-Atlantic and REP16-Atlantic. During those trials, various heterogeneous configurations were considered, including the use of a state-of-the-art diesel-electric submarine, guaranteeing maximum relevance for validation and evaluation of the designed solutions. The collected results demonstrate that JANUS is a viable solution for operationally relevant underwater applications, thus validating the objective of this paper. Additionally, encouraging feedback has been provided by the operational community participating to the trials. The capabilities demonstrated served as an initial proof of concept and will certainly lead to newer requirements and eventually even more functionalities.

A significant driver of uncertainty in sonar performance is the variability in underwater acoustical propagation caused by environmental fluctuations and uncertainty in the position of sources, targets, and receivers. A set of echo-repeat experiments was conducted during the Target and Reverberation Experiment 2013 (TREX13), a sea trial that took place in April to May 2013 in the Gulf of Mexico near Panama City, FL, USA. The variability in measured transmission loss (TL) was characterized using two different methods: Variability with respect to a mean observed TL, and variability with respect to modeled TL. Both one-way and quasi-reciprocal two-way TL measurements at 2250 and 7500 Hz were analyzed to characterize the variability at timescales ranging from less than one second to several days, with the results indicating that the acoustic propagation fluctuates stochastically on all these time scales. The results of statistical tests suggest that the TL variability can be treated as Gaussian fluctuations about a central TL obtained from an acoustic propagation model, with standard deviations of 5 dB over timescales up to one day, or 10 dB over timescales from one to six days.

The 2013 Target and Reverberation Experiment (TREX13) sea trial was held just off the coast of Panama City, FL, USA, in May 2013. One of Defence Research and Development Canada's primary objectives was comparing the performance of continuous active sonar (CAS) and pulsed active sonar (PAS) in shallow water, where reverberation and clutter challenge sonar systems. The only option for a moving target during the trial was an echo repeater towed from a ship; however, this posed a serious challenge because before the trial there were no known echo repeater solutions that were compatible with CAS. Existing echo repeaters required short-duration waveforms as used in PAS, and even then they introduced range errors that caused a mismatch in reverberation background and made localization problematic. This paper presents three echo repeater techniques developed for TREX13 and tested on Canadian Forces Auxiliary research vessel CFAV Quest during the trial. Experimental results presented in this paper demonstrate detection of the echo repeater by a monostatic CAS system on R/V Hugh R Sharp .

This paper presents an intelligent control of a ship's roll motion damping system consisting of a pair of active fins driven by a hydraulic system. The suggested intelligent controller updates coefficients of the proportional-derivative-second derivative controller (PDD2) using particle swarm optimization (PSO). The memory-based and the triggered-memory-based PSO algorithms are studied to improve the performance of PSO-PDD2 algorithm for various sea conditions using simulations. The resulting triggered-memory-based PSO-PDD2 controller shows a superior total roll motion damping performance. Damping of small roll motions has also been achieved. The PSO-PDD2 active fin system controller has been implemented for the sample ship, Marti, in a full-scale sea trial in real time. Results of both simulations and real-time trials are presented. Application of PSO has yielded very promising results not only for PDD2 roll damping controller but also for other possible marine control application.

Ocean exploration is typically interpreted with reference to the location of a data collecting node, e.g., when reporting an event occurrence, or the location of an object itself. Underwater tracking (UT) is somewhat different from tracking using RADAR or radio-frequency signals, due to irregularities of the ocean current and depth-varying sound speed in water. Sound-speed uncertainty also makes it challenging to incorporate Doppler shift measurements into UT. In this paper, we present a new UT scheme which considers the above challenges and utilizes spatial correlation of ocean current to estimate the drift velocity of the tracked node (TN) as a combination of the drift velocities of anchor nodes. We also offer two types of unbiased confidence indexes aimed to control the use of drift velocity estimation. To evaluate the performance of our UT scheme, we employ a hybrid simulator that combines numerical models for the ocean current and the signal-power attenuation in the ocean. We also report results from two sea trials conducted in the Mediterranean Sea and in the Indian Ocean. By tracking the sound speed, and utilizing Doppler shift measurements and drift velocity information of anchor nodes, accuracy is significantly improved.

A fundamental successful docking operation requires the autonomous underwater vehicle (AUV) to be able to guide, navigate, and control itself into the docking station in a strategic manner and even possibly execute different maneuvers at different mission phases, depending on docking scenario, requirements, and homing sensor type. A docking station, due to environmental or mission requirements, is possibly oriented at a specific direction instead of allowing omnidirectional homing, and necessitates vehicle docking in only this direction. Depending on the operating environment, either wave or current presence or both can result in a dominating disturbance to the vehicle docking operation. In this work, an inverted ultrashort baseline (USBL) system is used as the main homing sensor to complement the existing navigation suite on the DSO-developed AUV. A docking guidance system was designed and implemented using the Sugeno fuzzy inference system (FIS). A desired heading vector field and the fuzzy rules were developed to perform the fuzzy docking maneuver. An error-state Kalman filter (KF) was designed, formulated, and implemented successfully on the AUV and has proven to perform excellent relative positioning estimation in sea trials. A software architecture was designed for the docking algorithms, and implemented onto a single board computer in the AUV. A sensor fusion approach to the software programming was adopted to ensure that navigation data from all navigation sensors are properly acquired and synchronized. A docking station was designed and eventually deployed at sea for docking trials. Successful AUV docking attempts at sea trials were demonstrated, thus showing the effectiveness of the implemented docking algorithms.

The goal of this study is to verify the applicability of fuel cells for powering surface vessels by developing a small catamaran-type unmanned surface vehicle (USV) powered by a hydrogen fuel cell. This study encompasses the entire process of designing a fuel-cell-powered USV, as well as sea trial tests with a 1.487-m full-scale prototype. To improve the performance and system efficiency of the USV, a simulation-based hull form design and integrated control systems were implemented during the design process. First, the optimum hull form of the catamaran, with the optimum separation, was determined based on hydrodynamic optimization using parametric models to achieve good fuel economy. Second, an automatic control system integrated with the global positioning and inertial navigation systems was implemented in the designed catamaran so that it had the ability to perform waypoint, departure, station keeping, and turning circle maneuvers. Next, the gain parameters of the fuzzy proportional–integral–derivative (PID) controller were adjusted to assure reliable tracking control. Automatically controlled trial tests were then conducted to validate the performance of the autonomous surface vehicle with respect to the fuel cell power requirements and its dynamic motion. Based on the results of the trial tests and the transient response of the fuel cell, a hybrid power control algorithm for the fuel cell and the batteries was implemented for more efficient power utilization. This catamaran-type unmanned surface vehicle powered with a fuel cell can be utilized for ocean environment monitoring and multifunctional missions.

This article discusses the development of Zhejiang University's Zhairuoshan Island Experimental Research Observatory (ZERO). The authors discuss its background, network structure, components, sea trials, and future plans. The authors predict that ZERO will have an important influence as a collaborative center for scientists, engineers, and the public.

Unmanned surface vehicles (USVs) are important autonomous marine robots that have been studied and gradually applied into practice. However, the autonomous navigation of USVs, especially the issue of obstacle avoidance in complicated marine environment, is still a fundamental problem. After studying the characteristics of the complicated marine environment, we propose a novel adaptive obstacle avoidance algorithm for USVs, based on the Sarsa on-policy reinforcement learning algorithm. The proposed algorithm is composed of local avoidance module and adaptive learning module, which are organized by the “divide and conquer” strategy-based architecture. The course angle compensation strategy is proposed to offset the disturbances from sea wind and currents. In the design of payoff value function of the learning strategy, the course deviation angle and its tendency are introduced into action rewards and penalty policies. The validity of the proposed algorithm is verified by comparative experiments of simulations and sea trials in three sea-state marine environments. The results show that the algorithm can enhance the autonomous navigation capacity of USVs in complicated marine environments.

The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects with high reflection loss, for example, ships hull, docks, rocks and so on. In the second step, excluding PD measurements related to ONLOS links, we use a constrained expectation-maximization algorithm to classify PD measurements into two classes: LOS and sea-related NLOS (SNLOS), and to estimate the statistical parameters of each class. Since our classifier relies on models for the underwater acoustic channel, which are often simplified, alongside simulation results, we validate the performance of our classifier based on measurements from three sea trials. Both our simulation and sea trial results demonstrate a high detection rate of ONLOS links, and accurate classification of PD measurements into LOS and SNLOS.

In this paper we present the basic features of the flight control of the SkySails towing kite system. After introduction of coordinate definitions and basic system dynamics we introduce a novel model used for controller design and justify its main dynamics with results from system identification based on numerous sea trials. We then present the controller design which we successfully use for operational flights for several years. Finally we explain the generation of dynamical flight patterns., 12 pages, 18 figures; submitted to IEEE Trans. on Control Systems Technology; revision: Fig. 15 corrected, minor text changes

In this paper, we propose a novel navigation method for underwater vehicles based on a single seafloor station (SS), with which the vehicles can estimate their positions and orientations with respect to the SS without the need of expensive inertial navigation system or time-consuming calibration. This method is suitable for near-seafloor applications requiring real-time and accurate positioning, such as seafloor imaging and sampling. The method is also suitable for autonomous underwater vehicles (AUVs) since no other external aid is necessary other than SS. The key idea is to utilize mutual acoustical measurements between the vehicle and the SS. Simply explained: 1) the vehicle starts by interrogating the SS acoustically and measures the range between the two units as well as the bearing of the SS with respect to the vehicle in the vehicle reference frame; and then, 2) the SS computes the bearing of the vehicle with respect to the SS and transmits this information back to the vehicle using a similar acoustical device. By combining this information and inputting it into a nonlinear filter structure that includes measurements of the vehicle's ground velocity and yaw angular velocity, the vehicle computes its position and heading estimates. A pair of acoustical devices named acoustical localization and communication (ALOC) devices that can communicate and calculate their relative positions have been built. Sea trials were carried out in October 2011 using the AUV Tri-Dog1 (TD) and a trial SS at Kagoshima Bay in Japan. The AUV successfully navigated around the SS based on the measurements of the ALOC device mounted on both the AUV and the SS. The performance of the method was verified through simulations based on the experimental results.

This paper describes acoustic and visual instruments developed to perform high-resolution surveys of the volumetric distribution of manganese crusts from an underwater vehicle. The instruments consist of an acoustic device, developed to perform in situ measurements of manganese crust thickness at depths of up to 3000 m, and a vision-based mapping system that generates 3-D color reconstructions of the seafloor. Methods to process the information obtained by these sensors to automatically identify areas of exposed crust using the 3-D reconstructions, and subsequently determine the thickness of the crusts based on the acoustic measurements, are described. Sea trials were performed at #5 Takuyo seamount with the systems mounted onboard the remotely operated vehicle Hyper-Dolphin during the NT10-11 cruise of the R/V Natsushima. The results are that the first time in situ measurements of manganese crust thickness have been performed, and it is demonstrated that, for the types of substrate dominant in the surveyed area, continuous acoustic measurement of manganese crust thickness is possible. The work described in this paper indicates that the proposed instruments and data processing algorithms can form useful tools to enable more efficient survey of manganese crusts.

When conducting remote mine-hunting operations with a sidescan-sonar-equipped vehicle, a lawn-mowing search pattern is standard if no prior information on potential target locations is available. Upon completion of this initial search, a list of contacts is obtained. The overall classification performance can be significantly improved by revisiting these contacts to collect additional looks. This paper provides, for the first time, a link between the recent literature which finds optimal secondary looks and optimal route planning software. Automated planning algorithms are needed to generate multiaspect routes to improve the performance of mine-hunting systems and increase the capability of navies to efficiently clear potential mine fields. This paper introduces two new numerical techniques designed to enable current remote mine-hunting systems to achieve secondary paths minimizing the total distance to be traveled and satisfying all motion and imaging constraints. The first "local" approach is based on a sequential algorithm dealing with more tractable subproblems, while the second is "global" and based on simulated annealing. These numerical techniques are applied to two test sites created for the Mongoose sea trial held at the 2007 Autonomous Underwater Vehicle (AUV) Fest, Panama City, FL. Highly satisfactory planning solutions are obtained.

In the context of the recent Maritime Rapid Environmental Assessment/Blue Planet 2007 sea trial (MREA/BP07), this paper presents a range-resolving tomography method based on ensemble Kalman filtering of full-field acoustic measurements, dedicated to the monitoring of environmental parameters in coastal waters. The inverse problem is formulated in a state-space form wherein the time-varying sound-speed field (SSF) is assumed to follow a random walk with known statistics and the acoustic measurements are a nonlinear function of the SSF and the bottom properties. The state-space form enables a straightforward implementation of a nonlinear Kalman filter, leading to a data assimilation problem. Surface measurements augment the measurement vector to constrain the range-dependent structure of the SSF. Realistic scenarios of vertical slice shallow-water tomography experiments are simulated with an oceanic model, based on the MREA/BP07 experiment. Prior geoacoustic inversion on the same location gives the bottom acoustic properties that are input to the propagation model. Simulation results show that the proposed scheme enables the continuous tracking of the range-dependent SSF parameters and their associated uncertainties assimilating new measurements each hour. It is shown that ensemble methods are required to properly manage the nonlinearity of the model. The problem of the sensitivity to the vertical array (VA) configuration is also addressed.

There are approximately 7 million maritime-style containers within Canada, and 70 million within the US. The interiors of such containers are rarely inspected. With market globalization, a large amount of these containers enter North America on a daily basis. Such containers may include contraband or dangerous items that present an economic or security risk. Despite significant security improvements, only 3 to 5 % of the maritime containers, that arrive in, or transition through, North America, go through physical inspection. Current container tracking technologies based on GPS consume high DC power, are costly, require line-of-sight with satellites, and are often too large to be covert. It is possible to overcome some of these limitations by basing the tracking system on the FM broadcast signal which is an alternative man-made signal that is reasonably ubiquitous, provides a geographically-unique frequency spectrum and is about 100,000 times as strong as a GPS satellite signal. This allows the development of a low-cost, low-power and miniature FM receiver that can record frequency spectrums and compare them to known data in order to trace the path that the container has taken for less than $10 per container. This presents the first results of highway trials (train and sea trials are currently being conducted) of such a low-cost, covert, non-GPS based technology (called "FM Tag") that records the worldwide displacement of containers and displays the path undertaken by the container while in transit. As an added security benefit, the tag is also capable of detecting container door openings. In addition to presenting the first trial results, this covers the innovative engineering aspects of using the metal enclosure of a maritime container as a VBF antenna, thereby eliminating the need to add an external antenna and achieving the goal of being 100% covert.

When a fast container ship or a naval vessel turns, accompanying roll motions occur. This roll effect must be considered in the horizontal equations of the motion of the ship to predict the maneuverability of the ship properly. In this paper, a new method for determining a model structure of the hydrodynamic roll moment acting on a ship and for estimating the hydrodynamic coefficients is proposed. The method utilizes a system identification technique with the data from sea trial tests or from free running model (FRM) tests. To obtain motion data that is applied to the proposed algorithm, an FRM of a large container ship was developed. Using this model ship, standard maneuvering tests were carried out on a small body of water out of doors. A hydrodynamic roll moment model was constructed utilizing the data from turning circle tests and a 20-20 zig-zag test. This was then confirmed through a 10-10 zig-zag test. It was concluded that a model structure of the hydrodynamic roll moment model could be established without difficulty through a system identification method and FRM tests.

Multi-AUV operations have much to offer a variety of underwater applications. With sensors to measure the environment and coordination that is appropriate to critical spatial and temporal scales, the group can perform important tasks such as adaptive ocean sampling. We describe a methodology for cooperative control of multiple vehicles based on virtual bodies and artificial potentials (VBAP). This methodology allows for adaptable formation control and can be used for missions such as gradient climbing and feature tracking in an uncertain environment. We discuss our implementation on a fleet of autonomous underwater gliders and present results from sea trials in Monterey Bay in August 2003. These at-sea demonstrations were performed as part of the Autonomous Ocean Sampling Network (AOSN) II project., Workshop on Autonomous Underwater Vehicles, 17-18 June 2004, Sebasco Estates, Maine

We propose an approach for vision-based navigation of underwater robots that relies on the use of video mosaics of the sea bottom as environmental representations for navigation. We present a methodology for building high-quality video mosaics of the sea bottom in a fully automatic manner, which ensures global spatial coherency. During navigation, a set of efficient visual routines are used for the fast and accurate localization of the underwater vehicle with respect to the mosaic. These visual routines were developed taking into account the operating requirements of real-time position sensing, error bounding, and computational load. A visual servoing controller, based on the vehicle's kinematics, is used to drive the vehicle along a computed trajectory, specified in the mosaic, while maintaining constant altitude. The trajectory toward a goal point is generated online to avoid undefined areas in the mosaic. We have conducted a large set of sea trials, under realistic operating conditions. This paper demonstrates that without resorting to additional sensors, visual information can be used to create environment representations of the sea bottom (mosaics) and support long runs of navigation in a robust manner.