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We propose a data-driven, model-free adaptive sliding mode control (MFASMC) approach to address the Haidou-1 ARV under-actuated motion control problem with uncertainties, including external disturbances and parameter perturbations. Firstly, we analyzed the two main difficulties in the motion control of Haidou-1 ARV. Secondly, in order to address these problems, a MFASMC control method was introduced. It is combined by a model-free adaptive control (MFAC) method and a sliding mode control (SMC) method. The main advantage of the MFAC method is that it relies only on the real-time measurement data of an ARV instead of any mathematical modeling information, and the SMC method guarantees the MFAC method's fast convergence and low overshooting. The proposed MFASMC control method can maneuver Haidou-1 ARV cruising at the desired forward speed, heading, and depth, even when the dynamic parameters of the ARV vary widely and external disturbances exist. It also addresses the problem of under-actuated motion control for the Haidou-1 ARV. Finally, the simulation results, including comparisons with a PID method and the MFAC method, demonstrate the effectiveness of our proposed method.

Many recent works have addressed the problem of wideband direction of arrival (DOA) estimation using grid-less sparse techniques, and these methods have been shown to outperform the traditional wideband DOA estimation methods. However, these methods often suffer from the problem of requiring manual parameter tuning or high computational complexity, which reduces their practicality. To alleviate this problem, a grid-less wideband DOA estimation method based on variational Bayesian inference is proposed in this paper. The method approximates the posterior probability density function of DOA with the help of variational Bayesian inference, which does not require manual adjustment of parameters and can obtain accurate DOA estimation results with low computational complexity. Numerical simulations and real measurement data processing show that the proposed method has a higher DOA estimation accuracy than other grid-less wideband methods while providing higher computational speed., (© 2024 Acoustical Society of America.)

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel-terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel-terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel-terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel-terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.

Underwater target detection techniques have been extensively applied to underwater vehicles for marine surveillance, aquaculture, and rescue applications. However, due to complex underwater environments and insufficient training samples, the existing underwater target recognition algorithm accuracy is still unsatisfactory. A long-term effort is essential to improving underwater target detection accuracy. To achieve this goal, in this work, we propose a modified YOLOv5s network, called YOLOv5s-CA network, by embedding a Coordinate Attention (CA) module and a Squeeze-and-Excitation (SE) module, aiming to concentrate more computing power on the target to improve detection accuracy. Based on the existing YOLOv5s network, the number of bottlenecks in the first C3 module was increased from one to three to improve the performance of shallow feature extraction. The CA module was embedded into the C3 modules to improve the attention power focused on the target. The SE layer was added to the output of the C3 modules to strengthen model attention. Experiments on the data of the 2019 China Underwater Robot Competition were conducted, and the results demonstrate that the mean Average Precision (mAP) of the modified YOLOv5s network was increased by 2.4%.

Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness can be dramatically degraded when conventional approaches are used. Deep learning has been found to have significant impact on a variety of applications, including marine engineering. In this context, we offer a review of deep learning-based underwater marine object detection techniques. Underwater object detection can be performed by different sensors, such as acoustic sonar or optical cameras. In this paper, we focus on vision-based object detection due to several significant advantages. To facilitate a thorough understanding of this subject, we organize research challenges of vision-based underwater object detection into four categories: image quality degradation, small object detection, poor generalization, and real-time detection. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we provide a detailed critical examination of the most extensively used datasets. In addition, we present comparative studies with previous reviews, notably those approaches that leverage artificial intelligence, as well as future trends related to this hot topic.

Alzheimer's Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model's prediction accuracy depends on the dataset's size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer's disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer's disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer's disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer's Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer's disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.

This paper reports trends in the input of underwater noise source energy emission from global shipping, based on bottom-up modeling of individual ships. In terms of energy, we predict the doubling of global shipping noise emissions every 11.5 years, on average, but there are large regional differences. Shipping noise emissions increase rapidly in Arctic areas and the Norwegian Sea. The largest contributors are the containerships, dry bulk and liquid tanker vessels which emit 75% of the underwater shipping noise source energy. The COVID-19 pandemic changed vessel traffic patterns and our modeling indicates a reduction of -6% in global shipping noise source energy in the 63 Hz ⅓ octave band. This reduction was largest in the Greenland Sea, the Coastal Waters of Southeast Alaska and British Columbia as well as the Gulf of California, temporarily disrupting the increasing pre-pandemic noise emission trend. However, in some sea areas, such as the Indian Ocean, Yellow Sea and Eastern China Sea the emitted noise source energy was only slightly reduced. In global scale, COVID-19 pandemic reduced the underwater shipping noise emissions close to 2017 levels, but it is expected that the increasing trend of underwater noise emissions will continue when the global economy recovers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.

This article devotes to the finite-time (FT) input anti-bump switching control (SC) issue for a kind of delay switched systems (DSSs). Our objective is to carry the FT control while bringing down possible large jumps in the control input. First, a formulation of the alleviation for the control signal jumps is given. Second, by the multiple Lyapunov-Krasovskii functions method, a sufficient criterion is developed to derive the DSSs to hold both the FT H∞ performance combined with the input anti-bump switching performance, capturing the dual objectives control of the switched system, both transient performance aims. Also, via this criterion, these two performances are admitted not to be held by subsystems. Then, a switching index in conjunction with a switching controller are simultaneously constructed to solve the problem of the FT input anti-bump SC of DSSs. Finally, through the simulation verification of an F-18 aircraft model, the validity of the method driven is illustrated., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

The use of unoccupied aircraft systems (UASs, also known as drones) in science is growing rapidly. Recent advances in microelectronics and battery technology have resulted in the rapid development of low-cost UASs that are transforming many industries. Drones are poised to revolutionize marine science and conservation, as they provide essentially on-demand remote sensing capabilities at low cost and with reduced human risk. A variety of multirotor, fixed-wing, and transitional UAS platforms are capable of carrying various optical and physical sampling payloads and are being employed in almost every subdiscipline of marine science and conservation. This article provides an overview of the UAS platforms and sensors used in marine science and conservation missions along with example physical, biological, and natural resource management applications and typical analytical workflows. It concludes with details on potential effects of UASs on marine wildlife and a look to the future of UASs in marine science and conservation.

Despite the advance of on-skin sensors over the last decade, a sensor that solves simultaneously the critical issues for using in everyday life, such as stable performance in various environments, use over a long period of time, and repeated use by easy handling, has not yet been achieved. Here, we introduce an auxetic hygroscopic sensor that simultaneously meets all of the conditions. The auxetic structure with a negative Poisson's ratio matches with deformation of the skin in ankles; hence, a conformal contact between the sensor and the skin could be maintained during repeated movements. Sweat was absorbed in the auxetic electrode made of a hydrogel pattern coated with Ag nanowires and evaporated quickly; such hygroscopic characteristic led to excellent breathability. An electrocardiogram sensor and a haptic device were fabricated according to the proposed design for a sensor electrode. The sensors provide stable detecting performance in various environments, such as exercising, submersion in water, exposure to concentrated salt water, and continuous wearing for long time (7 days). Also, the sensors could be manually attached repeatedly without degrading the performance. This study provides new structural insights for on-skin sensors and presents future research directions.

One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpinning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts that they will bring. Finally, the last challenge is related to responsible innovation and how ethics and security should be carefully considered as we develop the technology further., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status-i.e., the existence and identity (or name)-of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods-particle filtering and Bayesian feature estimation-are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented.

Oceanic conditions play an important role in determining the effects of climate change and these effects can be monitored through the changes in the physical properties of sea water. In fact, Oceanographers use various probes for measuring the properties within the water column. CTDs (Conductivity, Temperature and Depth) provide profiles of physical and chemical parameters of the water column. A CTD device consists of Conductivity (C), Temperature (T) and Depth (D) probes to monitor the water column changes with respect to relative depth. An optical fibre-based point sensor used as a combined pressure (depth) and temperature sensor and the sensor system are described. Measurements accruing from underwater trials of a miniature sensor for pressure (depth) and temperature in the ocean and in fresh water are reported. The sensor exhibits excellent stability and its performance is shown to be comparable with the Sea-Bird Scientific commercial sensor: SBE9Plus., Competing Interests: The authors declare no conflict of interest.

A miniature sensor for accurate measurement of pressure (depth) with temperature compensation in the ocean environment is described. The sensor is based on an optical fibre Extrinsic Fabry-Perot interferometer (EFPI) combined with a Fibre Bragg Grating (FBG). The EFPI provides pressure measurements while the Fibre Bragg Grating (FBG) provides temperature measurements. The sensor is mechanically robust, corrosion-resistant and suitable for use in underwater applications. The combined pressure and temperature sensor system was mounted on-board a mini remotely operated underwater vehicle (ROV) in order to monitor the pressure changes at various depths. The reflected optical spectrum from the sensor was monitored online and a pressure or temperature change caused a corresponding observable shift in the received optical spectrum. The sensor exhibited excellent stability when measured over a 2 h period underwater and its performance is compared with a commercially available reference sensor also mounted on the ROV. The measurements illustrates that the EFPI/FBG sensor is more accurate for depth measurements (depth of ~0.020 m).

There has been a significant increase in the proliferation and implementation of Wireless Sensor Networks (WSNs) in different disciplines, including the monitoring of maritime environments, healthcare systems, and industrial sectors. It has now become critical to address the security issues of data communication while considering sensor node constraints. There are many proposed schemes, including the scheme being proposed in this paper, to ensure that there is a high level of security in WSNs. This paper presents a symmetric security scheme for a maritime coastal environment monitoring WSN. The scheme provides security for travelling packets via individually encrypted links between authenticated neighbors, thus avoiding a reiteration of a global rekeying process. Furthermore, this scheme proposes a dynamic update key based on a trusted node configuration, called a leader node, which works as a trusted third party. The technique has been implemented in real time on a Waspmote test bed sensor platform and the results from both field testing and indoor bench testing environments are discussed in this paper., Competing Interests: The authors declare no conflict of interest.

This paper deals with two aspects, namely a historical analysis of the use of unmanned vehicles (UAVs ROVs, AUVs) in maritime spill incidents and a detailed description of a multi-agency oil and HNS incident response exercise involving the integration and analysis of unmanned vehicles environmental sensing equipment. The exercise was a first in terms of the level of robotic systems deployed to assist in survey, surveillance and inspection roles for oil spills and harmful and noxious substances., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

This work addresses the inverse kinematics problem of a bioinspired octopus-like manipulator moving in three-dimensional space. The bioinspired manipulator has a conical soft structure that confers the ability of twirling around objects as a real octopus arm does. Despite the simple design, the soft conical shape manipulator driven by cables is described by nonlinear differential equations, which are difficult to solve analytically. Since exact solutions of the equations are not available, the Jacobian matrix cannot be calculated analytically and the classical iterative methods cannot be used. To overcome the intrinsic problems of methods based on the Jacobian matrix, this paper proposes a neural network learning the inverse kinematics of a soft octopus-like manipulator driven by cables. After the learning phase, a feed-forward neural network is able to represent the relation between manipulator tip positions and forces applied to the cables. Experimental results show that a desired tip position can be achieved in a short time, since heavy computations are avoided, with a degree of accuracy of 8% relative average error with respect to the total arm length.