Remotely Operated Vehicle Research Articles

Review of EMC standards for Underwater Vehicles

Electromagnetic Compatibility (EMC) is a critical consideration in the design and manufacturing of any kind of automobile, including underwater vehicles like Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV), to ensure their proper functioning and to prevent Interference with other equipment. The purpose of this paper is to understand the significant importance attached to the Electromagnetic Compatibility (EMC) concept in the design and manufacturing of underwater vehicles such as Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). It critically reviews available standards concerning Electromagnetic Compatibility (EMC) for these vehicles while pointing out some of the many sources of Electromagnetic Interference (EMI) within the surroundings and the geography, such as the sea. This research makes it clear that optimum performance and lack of Interference with other marine systems are only realizable with the use of necessary EMI requirements. This paper comprehensively explores the existing and diverse EMC standards applicable to underwater Vehicles.

Research on multi-scale fusion image enhancement and improved YOLOv5s lightweight ROV underwater target detection method.

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields.

ROV localization in the nuclear reactor pressure vessel using LSTM and an improved adaptive Kalman Filter

In this paper, a remotely operated vehicle (ROV) localization algorithm for nuclear reactor pressure vessels is introduced. Traditional machine vision localization suffers from visual blind zones. To address this issue, a long short-term memory (LSTM) network was employed to obtain the ROV’s position. To further enhance the accuracy, an improved Sage-Husa adaptive Kalman Filter (SHAKF) was applied to eliminate cumulative errors from LSTM. Finally, upper-diagonal decomposition and confidence checks were used to enhance the traditional SHAKF, addressing issues associated with the non-positive definite error covariance matrix and sensor limitations. Simulated and experimental results demonstrated that the LSTM network significantly reduced the cumulative error from trajectory projection localization. The root mean square error of the localization results was reduced from 51.776 to 12.808 mm due to the improvements made to the traditional SHAKF. Additionally, the improved SHAKF effectively minimized the cumulative LSTM localization error.

Deep-ocean macrofaunal assemblages on ferromanganese and phosphorite-rich substrates in the Southern California Borderland.

Mineral-rich hardgrounds, such as ferromanganese (FeMn) crusts and phosphorites, occur on seamounts and continental margins, gaining attention for their resource potential due to their enrichment in valuable metals in some regions. This study focuses on the Southern California Borderland (SCB), an area characterized by uneven and heterogeneous topography featuring FeMn crusts, phosphorites, basalt, and sedimentary rocks that occur at varying depths and are exposed to a range of oxygen concentrations. Due to its heterogeneity, this region serves as an optimal setting for investigating the relationship between mineral-rich hardgrounds and benthic fauna. This study characterizes the density, diversity, and community composition of macrofauna (>300 μm) on hardgrounds as a function of substrate type and environment (depth and oxygen ranges). Rocks and their macrofauna were sampled quantitatively using remotely operated vehicles (ROVs) during expeditions in 2020 and 2021 at depths above, within, and below the oxygen minimum zone (OMZ). A total of 3,555 macrofauna individuals were counted and 416 different morphospecies (excluding encrusting bryozoans and hydrozoans) were identified from 82 rocks at depths between 231 and 2,688 m. Average density for SCB macrofauna was 11.08 ± 0.87 ind. 200 cm-2 and mean Shannon-Wiener diversity per rock (H'[loge]) was 2.22 ± 0.07. A relationship was found between substrate type and macrofaunal communities. Phosphorite rocks had the highest H' of the four substrates compared on a per-rock basis. However, when samples were pooled by substrate, FeMn crusts had the highest H' and rarefaction diversity. Of all the environmental variables examined, water depth explained the largest variance in macrofaunal community composition. Macrofaunal density and diversity values were similar at sites within and outside the OMZ. This study is the first to analyze the macrofaunal communities of mineral-rich hardgrounds in the SCB, which support deep-ocean biodiversity by acting as specialized substrates for macrofaunal communities. Understanding the intricate relationships between macrofaunal assemblages and mineral-rich substrates may inform effects from environmental disruptions associated with deep-seabed mining or climate change. The findings contribute baseline information useful for effective conservation and management of the SCB and will support scientists in monitoring changes in these communities due to environmental disturbance or human impact in the future.

Field geology under the sea with a remotely operated vehicle: Mona Rift, Puerto Rico

We implemented concepts of field geology at great ocean depths by constructing virtual outcrops from a string of overlapping video frames collected by remotely operated vehicles (ROVs). This lower-cost alternative to drilling boreholes allows stratigraphic extension into the offshore and regional interpretation of marine seismic profiles. The imagery was collected along a dive transect on the western wall of Mona Rift, a deep and narrow rift northwest of Puerto Rico, between water depths of 1560 m and 3927 m. The northern coast of Puerto Rico and its large offshore area are underlain by a mid-Eocene and younger forearc basin topped by a thick carbonate platform. There are no drill holes offshore, and tying seismic lines across the shoreline there is problematic. We describe our virtual outcrop and constrain its age and stratigraphy using seven rock samples collected by ROV and compare the outcrop’s stratigraphy to deep boreholes and outcrops on land. Our formation descriptions and ages agree, for the most part, with those on land, but we identified a 100-m-thick section that is represented on land by an unconformity. Our stratigraphic interpretation indicates lateral variations in formation thicknesses and establishes a cross-section for additional sampling of the Eocene–Pliocene geology. It also suggests that Mona Rift has formed since the mid-Pliocene. The presence or absence of ferromanganese (Fe-Mn) crust on rocks along the transect may be correlated with the smoothness of the rock surface.

Siliceous microfossil assemblages in the southern Emperor Seamount Chain sediments and their biogeographical and paleoceanographical implications

The taxonomic composition and abundance of siliceous microfossils in sediments is a valuable source of information about environmental changes at the sea surface today and in the geological past. This paper presents the results of a study of siliceous microplankton (radiolaria, diatoms, and silicoflagellates) in the surface calcareous sediments (silty foraminiferal oozes and sands) of the Nintoku, Jingu, Ojin, Koko, and Yuryaku guyots at the southern end of the Emperor Seamount Chain (ESC). Bottom sediments were collected using an underwater remote-operated vehicle (ROV) Comanche-18. The study revealed the taxonomic diversity and relatively high abundance of siliceous microplankton. Radiolarians are represented by 237 taxa (104 taxa from 68 genera of Spumellaria, 120 taxa from 61 genera of Nassellaria, and 13 taxa from 7 genera of Collodaria). Diatoms are represented by 60 species from 29 genera, and silicoflagellates by 5 species from 3 genera. Differences in siliceous microplankton reflect the northern boundary of warm water in the ESC, passing at approximately 38°N, between the Jingu and Ojin guyots, which coincides with the boundary of the Kuroshio Bifurcation Current, confirmed by the corals and the bathyal fauna of brittle stars (Ophiura). The absence of remains of siliceous microplankton in some sediments is probably due to dissolution, and the presence of extinct taxa from older sedimentary rocks and neritic species is due to process of reworking caused by active hydrodynamics on ESC guyots and transport by currents.

Remote science at sea with remotely operated vehicles.

Conducting sea-going ocean science no longer needs to be limited to the number of berths on a ship given that telecommunications, computing, and networking technologies onboard ships have become familiar mechanisms for expanding scientists' reach from onshore. The oceanographic community routinely works with remotely operated vehicles (ROVs) and pilots to access real-time video and data from the deep sea, while onboard a ship. The extension of using an ROV and its host vessel's live-streaming capabilities has been popularized for almost 3 decades as a telepresence technology. Telepresence-enabled vessels with ROVs have been employed for science, education, and outreach, giving a greater number of communities viewing access to ocean science. However, the slower development of technologies and social processes enabling sustained real-time involvement between scientists on-ship and onshore undermines the potential for broader access, which limits the possibility of increasing inclusivity and discoveries through a diversity of knowledge and capabilities. This article reviews ocean scientists' use of telepresence for ROV-based deep-sea research and funded studies of telepresence capabilities. The authors summarize these studies findings and conditions that lead to defining the use of telepresence-enabled vessels for "remote science at sea." Authors define remote science at sea as a type of ocean expedition, an additional capability, not a replacement for all practices by which scientists conduct ocean research. Remote science for ocean research is an expedition at-sea directed by a distributed science team working together from at least two locations (on-ship and onshore) to complete their science objectives for which primary data is acquired by robotic technologies, with connectivity supported by a high-bandwidth satellite and the telepresence-enabled ship's technologies to support the science team actively engaged before, during, and after dives across worksites. The growth of productive ocean expeditions with remote science is met with social, technical, and logistical challenges that impede the ability of remote scientists to succeed. In this article, authors review telepresence-enabled ocean science, define and situate the adjoined model of remote science at sea, and some infrastructural, technological and social considerations for conducting and further developing remote science at sea.

Improving Underwater Photogrammetric 3D Reconstruction Processing of Shipwreck Sites

Abstract. Applying image processing algorithms to enhance the clarity of underwater images can significantly assist in the visual interpretation of subsea environments. Taking advantage of techniques to maximise the information extracted from the captured input data to produce the highest quality output ensures a higher utilisation of the valuable recorded content which may have been collected at the high cost of a laborious diver survey or an expensive ROV (Remotely Operated Vehicle) expedition. Adopting photogrammetric 3D reconstruction processing in underwater imaging has enabled the creation of high-fidelity digital 3D models which have been used to map subsea infrastructure for maintenance and repair, coral reefs for ecological monitoring, and in the primary context of this article, shipwrecks sites for maritime archaeology and accident investigation. Alongside exploring algorithms to reduce the characteristic haze of underwater photography, this article illustrates the advantages of optimising the processes utilised at distinct stages of the photogrammetric 3D reconstruction workflow to improve the photorealism of the digital 3D models.

Quantifying marine growth on pipelines with archival remotely operated vehicle footage – a case study

Abstract. Marine growth can substantially affect the hydrodynamic properties of subsea infrastructure. The ability to accurately quantify this growth is essential for designing and maintaining subsea structures. Traditionally, quantifying marine growth has been done by applying simple ratio equations putting in relationship the known distance of an object and its length observed in the image to the length of an unknown object only observed in an image. This method is very sensitive if measurements are not taken in the same plane. Hence, this study aims to establish a method for creating a 3D model of a subsea pipeline segment based on Structure from Motion (SfM). The proof of concept is done using a case study. For this study, Remotely Operated Vehicle (ROV) footage from a 2019 inspection survey is utilised. The scenario in which the procedure is tested represents a difficult, but realistic test case. Despite the lack of metadata, video quality and obstructions (e.g. watermarks), meaningful results were produced. The SfM workflow included masking obstructions (e.g. watermarks), and an iterative camera alignment procedure to generate 3D models, enabling the measurement of marine growth using either the models or ortho images created. While the resulting models were sufficiently accurate to provide meaningful measurements for harder growth types, the method faced challenges in accurately modelling softer growth types, suggesting the need for further refinement. Nevertheless, SfM was proven to be a more reliable method of measuring biofouling than the ratio approach that been traditionally used.

Optimized Trajectory Tracking for ROVs Using DNN + ENMPC Strategy

This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed double closed-loop control system operates in two distinct phases: (1) The outer loop controller uses a DNN controller to replace the LMPC controller, overcoming the uncertainties in the kinematic model while reducing the computational burden. (2) The inner loop velocity controller is designed using a nonlinear model predictive control (NMPC) algorithm with its closed-loop stability proven. A DNN + ENMPC 3D trajectory tracking method is proposed, integrating a velocity threshold-triggered mechanism into the inner-loop NMPC controller to reduce computational iterations while sacrificing only a small amount of tracking control performance. Finally, simulation results indicate that compared with the ENMPC algorithm, NMPC + ENMPC can better track the desired trajectory, reduce thruster oscillations, and further minimize the computational load.

Convolutional Long Short-Term Memory Predictor for Collaborative Remotely Operated Vehicle Trajectory Tracking in a Leader–Follower Formation Subject to Communication and Sensor Latency in the Presence of External Disturbances

Nowadays, collaborative operations between Remotely Operated Vehicles (ROVs) face considerable challenges, particularly in leader–follower schemes. The underwater environment imposes limitations on acoustic modems, leading to reduced transmission speeds and increased latency in ROV position and speed transmission. This complicates effective communication between the ROVs. Traditional methods, such as Recursive Least Squares (RLS) predictors and the Kalman filter, have been employed to address these issues. However, these approaches have limitations in handling non-linear patterns and disturbances in underwater environments. This paper introduces a Convolutional Long Short-Term Memory (ConvLSTM) predictor designed to enhance communication and trajectory tracking between ROVs in a leader–follower scheme. The proposed ConvLSTM aims to address the shortcomings of previous methods by adapting effectively to varying conditions, including ocean currents, communication delays, and signal interruptions. Simulations were conducted to evaluate ConvLSTM’s performance and compare it with other advanced predictors, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), under different conditions. The results demonstrated that ConvLSTM achieved a 13.9% improvement in trajectory tracking, surpassing other predictors in scenarios that replicate real underwater conditions and multi-vehicle communication. These results highlight ConvLSTM’s potential to significantly enhance the performance and stability of collaborative ROV operations in dynamic underwater environments.

Dynamic modeling and learning based path tracking control for ROV-based deep-sea mining vehicle

Track slippage and body sinking of the tracked mining vehicle in the traditional deep-sea mining system are the critical issues for operating stability. To solve this bottleneck problem, a novel ROV-based deep-sea mining system is proposed in this study, in which a remotely-operated vehicle (ROV) towering a sledge-shaped mining robot (MRT) named ROV-based Deep-sea Mining Vehicle (ROVDMV) is instead of the traditional tracked Deep-sea mining vehicle. The design of the ROVDMV can fundamentally overcome the bottleneck problem. However, the complex marine environment and multi-rigid-body design of the ROVDMV pose new challenges for its path-tracking control. Firstly, the dynamic model of the ROVDMV considering the ROV at a fixed depth is established based on the bicycle model, which is mainly used as the control object in the numerical simulation. Secondly, a learning-based path-tracking control strategy is proposed for the path-tracking control of the ROVDMV. In the control strategy, a novel nonparametric learning (NPL) method is introduced to learn the uncertain nonlinear dynamics considering the external disturbances and parametric uncertainty. The NPL method is proven to provide bounded estimated error. Besides, the enhanced NPL method can save approximately 33 % of the computation time, and the average computation time for its optimization control problem is only 12.47 ms. Finally, the numerical results show that the NPL method can learn nonlinear dynamics accurately, and the proposed strategy has proven to be effective

Learning Underwater Intervention Skills Based on Dynamic Movement Primitives

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments.

New carnivorous sponges (Porifera: Cladorhizidae) from Western Australia, collected by a Remotely Operated Vehicle (ROV).

The last two decades have reinvigorated systematic research on predatory sponges, mainly fuelled by advances in technology that have facilitated collection in deep-water habitats. This research presents six new species of carnivorous sponges from the family Cladorhizidae Dendy, 1922 from the western continental margin of Australia. The new species are Abyssocladia johnhooperi nov. sp., Abyssocladia aurora nov. sp., Abyssocladia janusi nov. sp., Axoniderma challengeri nov. sp., Cladorhiza vanessaekins nov. sp. and Nullarbora ningalooa nov. sp.. This material was collected by ROV during expeditions FK200308 to the Ningaloo Canyons expedition off the mid-west coast near Ningaloo, and FK200126 to the Southwest Australian canyons expedition, in Western Australia. These and other expeditions by the Schmidt Ocean Institute in 2020-21 formed a campaign around Australia's deep sea and mesophotic environments, which has vastly increased our understanding of biodiversity in these habitats.

Flow memory effects on the nonlinear hydrodynamic load of an ROV in translational maneuvering

This paper describes experimental and numerical investigations of the flow memory effect on the nonlinear hydrodynamic load response of a work-class remotely operated vehicle (ROV) under surge, sway, and heave impulse motions. The hysteresis loops of the dynamic drag coefficients during impulse motion tests are analyzed by comparing them with those obtained in steady drag tests. The areas of the loops and differences between the maximum and minimum dynamic and steady coefficients are used to quantify the flow memory effect. A novel unsteady hydrodynamic model for ROVs in translational maneuvering is established. This model considers the flow memory effect in terms of the unsteady hydrodynamic load response obtained from impulse motion response tests. The results given by the unsteady model are found to be in good agreement with those from CFD simulations. The unsteady hydrodynamic characteristics (including the asymmetric forces, force magnitude, and phase delay), motion amplitude, and frequency effect on the hydrodynamic forces of the ROV are analyzed by comparing the unsteady model results with those obtained from the steady model, revealing the rules governing the flow memory effect.

Coupled Motion Response Analysis for Dynamic Target Salvage under Wave Action

The strategic recovery of buoys is a critical task in executing deep-sea research missions, as nations extend their exploration of marine territories. This study primarily investigates the dynamics of remotely operated vehicle (ROV)-assisted salvage operations for floating bodies during the recovery of dynamic maritime targets. It focuses on the hydrodynamic coefficients of dual floating bodies in this salvage process. The interaction dynamics of the twin floats are examined using parameters such as the kinematic response amplitude operator (RAO), added mass, damping coefficient, and mean drift force. During the “berthing stage”, when the double floats are at Fr = 0.15–0.18, their roll and yaw Response Amplitude Operators are diminished, resulting in smoother motion. Thus, the optimal berthing speed range for this stage is Fr = 0.15–0.18. During the “side-by-side phase”, the spacing between the ROV and FLOAT under wave action should be approximately 0.4 L to 0.5 L. The coupled motion of twin floating bodies under the influence of following waves can further enhance their stability. The ideal towing speed during the “towing phase” is Fr = 0.2. This research aims to analyze the mutual influence between two floating bodies under wave action. By simulating the coupled motion of dual dynamic targets, we more precisely assess the risks and challenges inherent in salvage operations, thus providing a scientific basis for the design and optimization of salvage strategies.

Forearc crustal faults as tsunami sources in the upper plate of the Lesser Antilles subduction zone: the case study of the Morne Piton fault system

Abstract. In this study, alternatively to the megathrust, we identify upper-plate normal faults orthogonal to the trench as a possible tsunami source along the Lesser Antilles subduction zone. The Morne Piton fault system is such a trench-perpendicular upper crustal fault at the latitude of Guadeloupe. By means of seismic reflection, high-resolution bathymetry, remotely operated vehicle (ROV) imaging and dating, we reassess the slip rate of the Morne Piton fault since 7 Ma, i.e., its inception, and quantify an average rate of 0.25 mm yr−1 since ca. 1.2 Ma. This result divides by two previous estimations, increases the earthquake time recurrence and lowers the associated hazard. The ROV dive revealed a metric scarp with striae at the toe of the Morne Piton fault system, suggesting a recent fault rupture. We estimate a fault rupture area of ∼ 450–675 km2 and then a magnitude range for a maximum seismic event around Mw 6.5 ± 0.5, making this fault potentially tsunamigenic as the nearby Les Saintes fault responsible for a tsunami following the 2004 Mw 6.3 earthquake. Consequently, we simulate a multi-segment tsunami model representative of a worst-case scenario if all the identified Morne Piton fault segments ruptured together. Our model provides clues for the potential impact of local tsunamis on the surrounding coastal area as well as for local bathymetric controls on tsunami propagation. We illustrate that (i) shallow-water plateaus act as secondary sources and are responsible for a wrapping of the tsunami waves around the island of Marie-Galante; (ii) canyons indenting the shallow-water plateau slope break focus and enhance the wave height in front of the most touristic and populated town of the island; and (iii) the resonance phenomenon is observed within the Les Saintes archipelago, showing that the waves' frequency content is able to perturb the sea level for many hours after the seismic rupture.

Research on improved underwater cable image processing technique based on CNN-GAN

In this study, we propose a CNN-GAN-based real-time processing technique for filtering images of underwater cables used in power systems. This addresses the excessive interference impurities that are frequently observed in images captured by remotely operated vehicles (ROVs). The process begins with the input of the original image into the convolutional neural network (CNN). Subsequently, the training outcomes, which serve as input parameters for the generative adversarial network (GAN), facilitate the filtering process. The system also calculates both the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), performing model updates via backward propagation. This technique utilizes deep learning technologies to achieve rapid, real-time filtering of underwater cable images. The experimental results reveal that the loss function of the CNN reaches 0.16 with an accuracy of 97.5%, while the loss function of the adversarial GAN network approaches 0.05. Compared with traditional methods such as DDN, JORDOR, RESCAN, and PRENet, the proposed CNN-GAN algorithm exhibits superior performance, as evidenced by the higher PSNR and SSIM values. Specifically, for clear water images, the PSNR reaches 29.86 dB and the SSIM is 0.9045. For severely polluted images, the PSNR is 28.67 dB and the SSIM is 0.8965, while for unevenly illuminated images, the PSNR and SSIM values are 24.37 dB and 0.88, respectively. These enhancements significantly benefit the monitoring and maintenance of power systems.

Adaptive Control for Underwater Simultaneous Lightwave Information and Power Transfer: A Hierarchical Deep-Reinforcement Approach

In this work, we consider a point-to-point underwater optical wireless communication scenario where an underwater sensor (US) transmits its sensing data to a remotely operated vehicle (ROV). Before the US transmits its data to the ROV, the ROV performs simultaneous lightwave information and power transfer (SLIPT), delivering both control data and lightwave power to the US. Under the considered scenario, our objective is to maximize energy harvesting at the US while supporting predetermined communication performance between the two nodes. To achieve this objective, we develop a hierarchical deep Q-network (DQN)–deep deterministic policy gradient (DDPG)-based online algorithm. This algorithm involves two reinforcement learning agents: the ROV and US. The role of the ROV agent is to determine an optimal beam-divergence angle that maximizes the received optical signal power at the US while ensuring a seamless optical link. Meanwhile, the US agent, which is influenced by the decision of the ROV agent, is responsible for determining the time-switching and power-splitting ratios to maximize energy harvesting without compromising the required communication performance. Unlike existing studies that do not account for adaptive parameter control in underwater SLIPT, the proposed algorithm’s adaptive nature allows for the dynamic fine-tuning of optimization parameters in response to varying underwater environmental conditions and diverse user requirements.

Opportunistic predatory feeding behaviour of Munida intermedia A. Milne-Edwards & Bouvier, 1899 targeting juveniles of Lepidopus caudatus (Euphrasen, 1788) in the north-western Mediterranean Sea

The genus Munida, inhabits various marine habitats and exhibits versatile feeding behaviour. This study explored the behavioural patterns of Munida intermedia, shedding light on its opportunistic behaviour and competitive interactions within its ecological niche. In 2018, during an investigation using a remotely operated vehicle (ROV), carried out on the Méjean shoal (north-western Mediterranean Sea), at 397 m depth, specimens were observed engaging in opportunistic predatory feeding, targeting juveniles of the bathypelagic silver scabbardfish Lepidopus caudatus. Our findings underscore the dynamic nature of M. intermedia feeding habits and the significant role of competitive interactions within its habitat, highlighting the role of video technology in capturing real-time information.