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The article discusses the problem of adaptive system synthesis to provide the detection, estimation, and elimination of the negative consequences of faults in the thrusters of underwater robots and deviations of their parameters. For this purpose, an approach consisting of three stages is proposed. At the first stage, based on a bank of diagnostic observers, deviations of the thruster parameters from their nominal values or the occurrence of errors in the readings of its sensors are detected. At the second stage, the magnitudes of the detected deviations and errors are estimated using sliding mode observers. To estimate not only single but also multiple faults, the additional sliding mode observers are used. At the third stage, a control signal is generated to retain the basic dynamic properties of the thruster based on the estimated magnitudes of deviations and errors. Thus, the main contribution of this paper is designing the systems compensating for the consequences of faults occurring in the thrusters of the UR and errors in the readings of the angular velocity sensors. This avoids reducing the accuracy of movement along specified trajectories, as well as preventing accidents that could lead to the loss of the vehicle. The operability and efficiency of the proposed systems were tested using computer simulations and experimental studies on an electro-mechanical stand. [ABSTRACT FROM AUTHOR]

The Norway lobster (Nephrops norvegicus) is one of the most important fishery items for the EU blue economy. This paper describes a software architecture based on neural networks, designed to identify the presence of N. norvegicus and estimate the number of its individuals per square meter (i.e. stock density) in deep-sea (350–380 m depth) Fishery No-Take Zones of the northwestern Mediterranean. Inferencing models were obtained by training open-source networks with images obtained from frames partitioning of in submarine vehicle videos. Animal detections were also tracked in successive frames of video sequences to avoid biases in individual recounting, offering significant success and precision in detection and density estimations. [ABSTRACT FROM AUTHOR]

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The field of bioinspired underwater robots aims to replicate the capabilities of marine animals in artificial systems. Stingrays have emerged as highly promising species to be mimicked because of their flat body morphology and size. Furthermore, they are considered high-performance species due to their maneuverability, propulsion mode, and sliding efficiency. Designing and developing mechanisms to imitate their pectoral fins is a challenge for underwater robotic researchers mainly because the locomotion characteristics depend on the coordinated movement of the fins. In the state of the art, several mechanisms were proposed with 2 active rotation degrees of freedom (DoFs) to replicate fin movement. In this paper, we propose adding an additional active DoF in order to improve the realism in the robotic manta ray movement. Therefore, in this article, we present the mechanical design, modeling, and kinematics analysis of a 3-active-and-rotational-DoF pectoral fin inspired by the Mobula Alfredi or reef manta ray. Additionally, by using the kinematics model, we were able to simulate and compare the behaviour of both mechanisms, that is, those with 2 and 3 DoFs. Our simulation results reveal an improvement in the locomotion, and we hypothesized that with the third DoF, some specific missions, such as hovering or fast emergence to the surface, will have a better performance. [ABSTRACT FROM AUTHOR]

Electrohydraulic soft actuators are a promising soft actuation technology for constructing bio-inspired underwater robots owing to the features of this technology such as large deformations and forces, fast responses, and high electromechanical efficiencies. However, this actuation technology requires high voltages, thereby limiting the use of these actuators in water and hindering the development of underwater robots. This paper describes a method for creating bio-inspired underwater robots using silicone-layered electrohydraulic soft actuators. The silicone layer functions as an insulator, enabling the application of high voltages underwater. Moreover, bending and linear actuation can be achieved by applying the silicone layers on one or both sides of the actuator. As a proof of concept, bending and linear actuators with planar dimensions of 20 mm χ 40 mm (length χ width) are fabricated and characterized. Underwater actuation is observed in both types of actuators. The bending actuators exhibit a bending angle and blocked force of 39.0° and 9.6 mN, respectively, at an applied voltage of 10 kV. Further, the linear actuators show a contraction strain and blocked force of 6.6% and 956.1 mN, respectively, at an applied voltage of 10 kV. These actuators are tested at a depth near the surface of water. This ensured that they can operate at least at that depth. The actuators are subsequently used to implement various soft robotic devices such as a ray robot, a fish robot, a water-surface sliding robot, and a gripper. All of the robots exhibit movements as expected; up to 31.2 mm/s (0.91 body length/s) of locomotion speed is achieved by the swimming robots and a retrieve and place task is performed by the gripper. The results obtained in this study indicate the successful implementation of the actuator concept and its high potential for constructing bio-inspired underwater robots and soft robotics applications. [ABSTRACT FROM AUTHOR]

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments. [ABSTRACT FROM AUTHOR]

A bistable mechanism has two stable states with energy input required to move from one stable state to another. This energy barrier allows for energy storage and release which can be used to improve systems characteristics. Bistability has been used to increase the frequency range over which a kinetic energy harvester is effective, and it has been proposed that bistability can increase the efficiency of biomimetic swimming robots. However, experiments involving bistable swimming robots have typically used bistability as a means of overcoming limitations inherent to soft actuators, rather than to increase overall performance. This article implements bistability into a swimming robotic and compares performance with and without bistable action. The static thrust generation and power consumption for bistable and nonbistable configurations for five different tail morphologies are compared. Bistability is generally found to increase the system efficiency, particularly at lower frequencies where increases are observed up to 250%. The untethered swimming speed of the robot in open water is also found to increase by approximately 30%. The results show that bistability can offer direct performance benefits for biomimetic swimming, but that the bistable transmission must be well tuned to the dynamics of the rest of the system. [ABSTRACT FROM AUTHOR]

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Underwater unmanned robots are an essential tool for human underwater exploration and detection and are widely employed in a variety of underwater operational settings. One of the hottest issues in this field is applying bionic notions to the creation of underwater unmanned robots by simulating fish swimming or cephalopod crawling. Using the tentacle suction cup adsorption technique during octopus' predation as a model, underwater magnetic adsorption robots with the opening and closing claws were studied in this paper. First, the robot's general structural design is presented. The claw mechanism is demonstrated by mimicking the octopus's tentacle action during feeding, which primarily consists of an opening and closing claw that replicates the octopus's tentacle and a magnetic adsorption unit that replicates the octopus's suction cup adsorption. Then, the Kriging response surface optimization method is used to optimize the design of the claw mechanism to obtain excellent mechanical properties, and simulation software is used to verify. Finally, a robot prototype was built and its pool tests were conducted, with some experimental results presented. The experimental results show that after the robot reaches the predetermined position through pneumatic ejection and secondary propulsion launch, it can quickly open its claws within 0.11 s and apply 462.42 N adsorption force to complete the adsorption of the target. [ABSTRACT FROM AUTHOR]

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments. [ABSTRACT FROM AUTHOR]

Siltation is a significant element that affects the efficiency and safety of water conveyance tunnels. One efficient inspection technique is optical vision inspection carried out by underwater robots. However, efficient processing is required to handle the volume of images that underwater robots collect. Convolutional neural networks (CNNs), have demonstrated considerable promise in computer vision, however it is challenging to implement these models in underwater robots. In this paper, we propose a classification framework for multiple siltation types based on siltation images of water conveyance tunnels using the structure-optimized MobileNet v3, namely SRNet. An underwater robotic image acquisition device is used to acquire the siltation images for training and testing. Out of 6000 images collected from 7 water conveyance tunnels, 4172 are used to train the proposed SRNet network. The remaining 1828 images are used to test it. Furthermore, multiple learning strategies are used to optimize the entire training process. Compared with other deep learning models, the proposed method shows great superiority in terms of recognition results, computational cost and model size. The proposed method effectively weighs model accuracy and complexity and can be used for rapid and accurate identification of siltation in water conveyance tunnel health monitoring. [ABSTRACT FROM AUTHOR]

A bistable mechanism has two stable states with energy input required to move from one stable state to another. This energy barrier allows for energy storage and release which can be used to improve systems characteristics. Bistability has been used to increase the frequency range over which a kinetic energy harvester is effective, and it has been proposed that bistability can increase the efficiency of biomimetic swimming robots. However, experiments involving bistable swimming robots have typically used bistability as a means of overcoming limitations inherent to soft actuators, rather than to increase overall performance. This article implements bistability into a swimming robotic and compares performance with and without bistable action. The static thrust generation and power consumption for bistable and nonbistable configurations for five different tail morphologies are compared. Bistability is generally found to increase the system efficiency, particularly at lower frequencies where increases are observed up to 250%. The untethered swimming speed of the robot in open water is also found to increase by approximately 30%. The results show that bistability can offer direct performance benefits for biomimetic swimming, but that the bistable transmission must be well tuned to the dynamics of the rest of the system.

Electrohydraulic soft actuators are a promising soft actuation technology for constructing bio-inspired underwater robots owing to the features of this technology such as large deformations and forces, fast responses, and high electromechanical efficiencies. However, this actuation technology requires high voltages, thereby limiting the use of these actuators in water and hindering the development of underwater robots. This paper describes a method for creating bio-inspired underwater robots using silicone-layered electrohydraulic soft actuators. The silicone layer functions as an insulator, enabling the application of high voltages underwater. Moreover, bending and linear actuation can be achieved by applying the silicone layers on one or both sides of the actuator. As a proof of concept, bending and linear actuators with planar dimensions of 20 mm × 40 mm (length × width) are fabricated and characterized. Underwater actuation is observed in both types of actuators. The bending actuators exhibit a bending angle and blocked force of 39.0° and 9.6 mN, respectively, at an applied voltage of 10 kV. Further, the linear actuators show a contraction strain and blocked force of 6.6% and 956.1 mN, respectively, at an applied voltage of 10 kV. These actuators are tested at a depth near the surface of water. This ensured that they can operate at least at that depth. The actuators are subsequently used to implement various soft robotic devices such as a ray robot, a fish robot, a water-surface sliding robot, and a gripper. All of the robots exhibit movements as expected; up to 31.2 mm/s (0.91 body length/s) of locomotion speed is achieved by the swimming robots and a retrieve and place task is performed by the gripper. The results obtained in this study indicate the successful implementation of the actuator concept and its high potential for constructing bio-inspired underwater robots and soft robotics applications.

The article discusses the problem of adaptive system synthesis to provide the detection, estimation, and elimination of the negative consequences of faults in the thrusters of underwater robots and deviations of their parameters. For this purpose, an approach consisting of three stages is proposed. At the first stage, based on a bank of diagnostic observers, deviations of the thruster parameters from their nominal values or the occurrence of errors in the readings of its sensors are detected. At the second stage, the magnitudes of the detected deviations and errors are estimated using sliding mode observers. To estimate not only single but also multiple faults, the additional sliding mode observers are used. At the third stage, a control signal is generated to retain the basic dynamic properties of the thruster based on the estimated magnitudes of deviations and errors. Thus, the main contribution of this paper is designing the systems compensating for the consequences of faults occurring in the thrusters of the UR and errors in the readings of the angular velocity sensors. This avoids reducing the accuracy of movement along specified trajectories, as well as preventing accidents that could lead to the loss of the vehicle. The operability and efficiency of the proposed systems were tested using computer simulations and experimental studies on an electro-mechanical stand.

The field of bioinspired underwater robots aims to replicate the capabilities of marine animals in artificial systems. Stingrays have emerged as highly promising species to be mimicked because of their flat body morphology and size. Furthermore, they are considered high-performance species due to their maneuverability, propulsion mode, and sliding efficiency. Designing and developing mechanisms to imitate their pectoral fins is a challenge for underwater robotic researchers mainly because the locomotion characteristics depend on the coordinated movement of the fins. In the state of the art, several mechanisms were proposed with 2 active rotation degrees of freedom (DoFs) to replicate fin movement. In this paper, we propose adding an additional active DoF in order to improve the realism in the robotic manta ray movement. Therefore, in this article, we present the mechanical design, modeling, and kinematics analysis of a 3-active-and-rotational-DoF pectoral fin inspired by the Mobula Alfredi or reef manta ray. Additionally, by using the kinematics model, we were able to simulate and compare the behaviour of both mechanisms, that is, those with 2 and 3 DoFs. Our simulation results reveal an improvement in the locomotion, and we hypothesized that with the third DoF, some specific missions, such as hovering or fast emergence to the surface, will have a better performance.

This paper presents a novel hybrid control method for position tracking of an underwater quadruped walking robot. The proposed approach combines an existing position-tracking control method with a deep-learning neural network. The neural network compensates for non-linear dynamic characteristics, such as the effect of fluid, without relying on mathematical modeling. To achieve this, a Multi-Layer Perceptron neural network is designed to analyze joint torque in relation to the joint angle and angular velocity of the robot, as well as the position and orientation of the foot tip and environmental data. The improvement in tracking control performance is evaluated using a 4-DOF underwater robot leg. For the neural network design, position tracking control data, including dynamic characteristics, were collected through position command-based position tracking control. Afterward, a learning model was constructed and trained to predict joint torque related to the robot's motion and posture. This learning process incorporates non-linear dynamic characteristics, such as joint friction and the influence of fluid, in the joint torque prediction. The proposed method is then combined with conventional task-space PD control to perform position-tracking control with enhanced performance. Finally, the proposed method is evaluated using the underwater robot leg and compared to a single task-space PD controller. The proposed method demonstrates higher position accuracy with similar joint torque output, thereby increasing compliance and tracking performance simultaneously. [ABSTRACT FROM AUTHOR]

This study presents the design of an underwater superlimb as a wearable robot, providing divers with mobility assistance and freeing their hands for manipulating tools underwater. The wearable design features a thrust vectoring system with two 3D‐printed, waterproofed modules. The module with adjustable connections and strapping holes is designed to enable reconfiguration for multiple purposes, including regular use as an underwater superlimb for divers, manually operated as a handheld glider for swimmers, combined with an amphibian, legged robot as a quadruped superlimb, and coupled as a dual‐unit autonomous underwater vehicle for underwater navigation. The kinematics and dynamics of the prototype and all of its reconfigured modes are developed. A sliding‐mode controller is also introduced to achieve stable simulation in PyBullet. Field tests further support the feasibility of the underwater superlimb when worn on a test diver in a swimming pool. As the first underwater superlimb presented in the literature, this study opens new doors for supernumerary robotic limbs in underwater scenarios with multifunctional reconfiguration. [ABSTRACT FROM AUTHOR]

Sea turtles gracefully navigate their marine environments by flapping their pectoral flippers in an elegant routine to produce the required hydrodynamic forces required for locomotion. The propulsion of sea turtles has been shown to occur for approximately 30% of the limb beat, with the remaining 70% employing a drag-reducing glide. However, it is unknown how the sea turtle manipulates the flow during the propulsive stage. Answering this research question is a complicated process, especially when conducting laboratory tests on endangered animals, and the animal may not even swim with its regular routine while in a captive state. In this work, we take advantage of our robotic sea turtle, internally known as Cornelia, to offer the first insights into the flow features during the sea turtle's propulsion cycle consisting of the downstroke and the sweep stroke. Comparing the flow features to the animal's swim speed, flipper angle of attack, power consumption, thrust and lift production, we hypothesise how each of the flow features influences the animal's propulsive efforts and cost of transport (COT). Our findings show that the sea turtle can produce extremely low COT values that point to the effectiveness of the sea turtle propulsive technique. Based on our findings, we extract valuable data that can potentially lead to turtle-inspired elements for high-efficiency underwater drones for long-term underwater missions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid propulsion-based small underwater robot for robust trajectory tracking control in a harsh and complex underwater environment. The robot is equipped with a Coanda-effect jet thruster and a pair of propeller-based reconfigurable magnetic-coupling thrusters, allowing it to traverse safely in confined or cluttered spaces as well as cruise efficiently in the open water. To investigate the robot dynamic modeling, we first formulated its simplified mathematical model and estimated the hydrodynamic coefficients by performing the planar motion mechanism using CFD (computational fluid dynamics) simulation. Then, a double-loop trajectory tracking control architecture was designed considering the model uncertainties and environmental disturbances. Based on Lyapunov theory, the outer-loop kinematic control produces the virtual velocity command, while the inner-loop dynamic control adopts the full-state feedback L1-adaptive control to match the command. The asymptotic convergence of the tracking errors and the stability of the whole closed-loop system are guaranteed. Finally, comparative simulations in the presence of unknown disturbances and the variation of model parameters were carried out to verify the robustness of our proposed trajectory tracking control, which is also suitable for the separated son robots. [ABSTRACT FROM AUTHOR]

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments.

Underwater unmanned robots are an essential tool for human underwater exploration and detection and are widely employed in a variety of underwater operational settings. One of the hottest issues in this field is applying bionic notions to the creation of underwater unmanned robots by simulating fish swimming or cephalopod crawling. Using the tentacle suction cup adsorption technique during octopus’ predation as a model, underwater magnetic adsorption robots with the opening and closing claws were studied in this paper. First, the robot’s general structural design is presented. The claw mechanism is demonstrated by mimicking the octopus’s tentacle action during feeding, which primarily consists of an opening and closing claw that replicates the octopus’s tentacle and a magnetic adsorption unit that replicates the octopus’s suction cup adsorption. Then, the Kriging response surface optimization method is used to optimize the design of the claw mechanism to obtain excellent mechanical properties, and simulation software is used to verify. Finally, a robot prototype was built and its pool tests were conducted, with some experimental results presented. The experimental results show that after the robot reaches the predetermined position through pneumatic ejection and secondary propulsion launch, it can quickly open its claws within 0.11 s and apply 462.42 N adsorption force to complete the adsorption of the target.

This study presents the design of an underwater superlimb as a wearable robot, providing divers with mobility assistance and freeing their hands for manipulating tools underwater. The wearable design features a thrust vectoring system with two 3D‐printed, waterproofed modules. The module with adjustable connections and strapping holes is designed to enable reconfiguration for multiple purposes, including regular use as an underwater superlimb for divers, manually operated as a handheld glider for swimmers, combined with an amphibian, legged robot as a quadruped superlimb, and coupled as a dual‐unit autonomous underwater vehicle for underwater navigation. The kinematics and dynamics of the prototype and all of its reconfigured modes are developed. A sliding‐mode controller is also introduced to achieve stable simulation in PyBullet. Field tests further support the feasibility of the underwater superlimb when worn on a test diver in a swimming pool. As the first underwater superlimb presented in the literature, this study opens new doors for supernumerary robotic limbs in underwater scenarios with multifunctional reconfiguration.

The underlying principle of the unique dynamic adaptive adhesion capability of a rock-climbing fish (Beaufortia kweichowensis) that can resist a pull-off force of 1000 times its weight while achieving simultaneous fast sliding (7.83 body lengths per second (BL/S)) remains a mystery in the literature. This adhesion-sliding ability has long been sought for underwater robots. However, strong surface adhesion and fast sliding appear to contradict each other due to the need for high surface contact stress. The skillfully balanced mechanism of the tight surface adhesion and fast sliding of the rock-climbing fish is disclosed in this work. The Stefan force (0.1 mN/mm2) generated by micro-setae on pectoral fins and ventral fins leads to a 70 N/m2 adhesion force by conforming the overall body of the fish to a surface to form a sealing chamber. The pull-off force is neutralized simultaneously due to the negative pressure caused by the volumetric change of the chamber. The rock-climbing fish's micro-setae hydrodynamic interaction and sealing suction cup work cohesively to contribute to low friction and high pull-off-force resistance and can therefore slide rapidly while clinging to the surface. Inspired by this unique mechanism, an underwater robot is developed with incorporated structures that mimic the functionality of the rock-climbing fish via a micro-setae array attached to a soft self-adaptive chamber, a setup which demonstrates superiority over conventional structures in terms of balancing tight underwater adhesion and fast sliding. [ABSTRACT FROM AUTHOR]

In this work, we present the design, implementation, and simulation of a topology-based navigation system for the UX-series robots, a spherical underwater vehicle designed to explore and map flooded underground mines. The objective of the robot is to navigate autonomously in the 3D network of tunnels of a semi-structured but unknown environment in order to gather geoscientific data. We start from the assumption that a topological map has been generated by a low-level perception and SLAM module in the form of a labeled graph. However, the map is subject to uncertainties and reconstruction errors that the navigation system must address. First, a distance metric is defined to compute node-matching operations. This metric is then used to enable the robot to find its position on the map and navigate it. To assess the effectiveness of the proposed approach, extensive simulations have been carried out with different randomly generated topologies and various noise rates. [ABSTRACT FROM AUTHOR]

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

This paper presents a novel hybrid control method for position tracking of an underwater quadruped walking robot. The proposed approach combines an existing position-tracking control method with a deep-learning neural network. The neural network compensates for non-linear dynamic characteristics, such as the effect of fluid, without relying on mathematical modeling. To achieve this, a Multi-Layer Perceptron neural network is designed to analyze joint torque in relation to the joint angle and angular velocity of the robot, as well as the position and orientation of the foot tip and environmental data. The improvement in tracking control performance is evaluated using a 4-DOF underwater robot leg. For the neural network design, position tracking control data, including dynamic characteristics, were collected through position command-based position tracking control. Afterward, a learning model was constructed and trained to predict joint torque related to the robot’s motion and posture. This learning process incorporates non-linear dynamic characteristics, such as joint friction and the influence of fluid, in the joint torque prediction. The proposed method is then combined with conventional task-space PD control to perform position-tracking control with enhanced performance. Finally, the proposed method is evaluated using the underwater robot leg and compared to a single task-space PD controller. The proposed method demonstrates higher position accuracy with similar joint torque output, thereby increasing compliance and tracking performance simultaneously.

With the increasing requirements of underwater missions and the rapid development of soft robotics technologies, soft underwater swimming robots have become a hot topic of research due to their low noise, high flexibility, and high environmental adaptability. In the past 10 years, research into soft underwater robots based on artificial muscle actuation has made considerable progress. Herein, a comprehensive survey on recent advances in soft underwater swimming robots based on different actuation methods is reviewed systematically, including pressure actuation, intelligent material actuation, and biomaterial actuation. First, for each type of actuation, the actuating principle, structural design, and swimming performance of soft underwater robots are introduced in detail. Then, according to the different swimming modes of underwater organisms, the aforementioned soft underwater robots are classified and compared. The results show that for different swimming tasks and application scenarios, the robot needs to realize the optimal design by reasonably selecting the actuating method and the swimming mode. This review summarizes the advanced information and critical technology in designing high‐performance soft underwater robots in the aspect of structural design, actuating methods, and swimming modes and offers an insightful outlook for the future development of soft robots. [ABSTRACT FROM AUTHOR]

Propeller-based reconfigurable magnetic coupling thrusters are a promising propulsion system for underwater robots, due to their vectored thrust and watertightness. This paper presents a lightweight and compact 3-DOF spherical reconfigurable magnetic coupling thruster (SRMCT-II), and focuses on its thruster configuration and control allocation, considering its intrinsic characteristics. To enhance robot agility, we develop an optimization problem for the configuration of SRMCT-II without relying on the engineers' experience or intuition and use a genetic algorithm to determine the optimal layout of three SRMCT-IIs for an underwater robot prototype. To assign motion control commands to the SRMCT-IIs, we formulate the control allocation as a constrained convex optimization problem and solve it using a practical augmented Lagrangian method. In the experiments, a customized force-sensing testbed is fabricated to evaluate the performance of the SRMCT-II and the robot's control allocation with its optimal thruster layout. The results verify the vectored characteristics of the SRMCT-II and demonstrate the computational efficiency and accuracy of the control allocation. The validation of thrust attainability reveals that the optimized thruster configuration achieves complete decoupling of the generalized control forces and torques. This indicates that the underwater robot can control its thrust and torque independently in all three dimensions. • A lightweight and compact 3-DOF spherical reconfigurable magnetic coupling thruster (SRMCT-II) is presented and we're devoted to optimizing the thruster configuration and control allocation considering its intrinsic characteristics. • To maximize robot agility, the task of configuring SRMCT-IIs for underwater robots is formulated as an optimization problem, and a genetic algorithm is applied to search for the optimal thruster layout. • The control allocation is formulated as a constrained convex optimization problem and the augmented Lagrangian method is implemented on the controller to solve it. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid propulsion-based small underwater robot for robust trajectory tracking control in a harsh and complex underwater environment. The robot is equipped with a Coanda-effect jet thruster and a pair of propeller-based reconfigurable magnetic-coupling thrusters, allowing it to traverse safely in confined or cluttered spaces as well as cruise efficiently in the open water. To investigate the robot dynamic modeling, we first formulated its simplified mathematical model and estimated the hydrodynamic coefficients by performing the planar motion mechanism using CFD (computational fluid dynamics) simulation. Then, a double-loop trajectory tracking control architecture was designed considering the model uncertainties and environmental disturbances. Based on Lyapunov theory, the outer-loop kinematic control produces the virtual velocity command, while the inner-loop dynamic control adopts the full-state feedback L1-adaptive control to match the command. The asymptotic convergence of the tracking errors and the stability of the whole closed-loop system are guaranteed. Finally, comparative simulations in the presence of unknown disturbances and the variation of model parameters were carried out to verify the robustness of our proposed trajectory tracking control, which is also suitable for the separated son robots.

Sea turtles gracefully navigate their marine environments by flapping their pectoral flippers in an elegant routine to produce the required hydrodynamic forces required for locomotion. The propulsion of sea turtles has been shown to occur for approximately 30% of the limb beat, with the remaining 70% employing a drag-reducing glide. However, it is unknown how the sea turtle manipulates the flow during the propulsive stage. Answering this research question is a complicated process, especially when conducting laboratory tests on endangered animals, and the animal may not even swim with its regular routine while in a captive state. In this work, we take advantage of our robotic sea turtle, internally known as Cornelia, to offer the first insights into the flow features during the sea turtle’s propulsion cycle consisting of the downstroke and the sweep stroke. Comparing the flow features to the animal’s swim speed, flipper angle of attack, power consumption, thrust and lift production, we hypothesise how each of the flow features influences the animal’s propulsive efforts and cost of transport (COT). Our findings show that the sea turtle can produce extremely low COT values that point to the effectiveness of the sea turtle propulsive technique. Based on our findings, we extract valuable data that can potentially lead to turtle-inspired elements for high-efficiency underwater drones for long-term underwater missions.

In this study, an antagonistic actuator using dielectric elastomer actuators (DEAs) is developed to investigate the use of rolled DEAs in underwater robots. The actuator consists of a backbone, an elastic hinge, and two rolled DEAs placed in an antagonistic fashion, allowing for the generation of bidirectional movements of the actuator tip. To prove this concept, an analytical model of the actuator is built. The experimental samples are fabricated based on the specification determined by the model. In the fabricated actuator, each rolled DEA has a diameter of 6 mm and a length of 21 mm. The whole device weighs 1.7 g. In the tested voltage range of 0–1200 V, the actuator exhibits a voltage-controllable angle and torque of up to 2.2° and 11.3 mN∙mm, respectively. The actuator is then implemented into a swimming robot, which shows forward speed of 0.9 mm/s at the applied voltage of 1000 V and the driving frequency of 10 Hz. The results demonstrate the feasibility of using rolled DEAs in underwater robots. [ABSTRACT FROM AUTHOR]

Underwater robots are often used in marine exploration and development to assist divers in underwater tasks. However, the underwater robots on the market have some problems, such as only a single function of object detection or tracking, the use of traditional algorithms with low accuracy and robustness, and the lack of effective interaction with divers. To this end, we designed a type of gesture recognition based on interaction, using person tracking as an auxiliary means for an underwater accompanying robot (UAR). We train and test the SSDLite detection algorithm using the self-labeled underwater datasets, and combine the kernelized correlation filters (KCF) tracking algorithm with the "Active Control" target tracking rule to continuously track the underwater human body. Our experiments show that the use of underwater datasets and target tracking can effectively improve gesture recognition accuracy by 40–105%. In the outfield experiment, the performance of the algorithm was good. It achieved target tracking and gesture recognition at 29.4 FPS on Jetson Xavier NX, and the UAR made corresponding actions according to the diver gesture command. [ABSTRACT FROM AUTHOR]

Clear, correct imaging is a prerequisite for underwater operations. In real freshwater environment including rivers and lakes, the water bodies are usually turbid and dynamic, which brings extra troubles to quality of imaging due to color deviation and suspended particulate. Most of the existing underwater imaging methods focus on relatively clear underwater environment, it is uncertain that if those methods can work well in turbid and dynamic underwater environments. In this paper, we propose a turbidity-adaptive underwater image enhancement method. To deal with attenuation and scattering of varying degree, the turbidity is detected by the histogram of images. Based on the detection result, different image enhancement strategies are designed to deal with the problem of color deviation and blurring. The proposed method is verified by an underwater image dataset captured in real underwater environment. The result is evaluated by image metrics including structure similarity index measure, underwater color image quality evaluation metric, and speeded-up robust features. Test results exhibit that the method can correct the color deviation and improve the quality of underwater images. [ABSTRACT FROM AUTHOR]

Net-winged midge larvae (Blephariceridae) are known for their remarkable ability to adhere to and crawl on the slippery surfaces of rocks in fast-flowing and turbulent alpine streams, waterfalls, and rivers. This remarkable performance can be attributed to the larvae's powerful ventral suckers. In this article, we first develop a theoretical model of the piston-driven sucker that considers the lubricated state of the contact area. We then implement a piston-driven robotic sucker featuring a V-shaped notch to explore the adhesion-sliding mechanism. Each biomimetic larval sucker has the unique feature of an anterior-facing V-shaped notch on its soft disc rim; it slides along the shear direction while the entire disc surface maintains powerful adhesion on the benthic substrate, just like the biological counterpart. We found that this biomimetic sucker can reversibly transit between "high friction" (4.26 ± 0.34 kPa) and "low friction" (0.41 ± 0.02 kPa) states due to the piston movement, resulting in a frictional enhancement of up to 93.9%. We also elucidate the frictional anisotropy (forward/backward force ratio: 0.81) caused by the V-shaped notch. To demonstrate the robotic application of this adhesion-sliding mechanism, we designed an underwater crawling robot Adhesion Sliding Robot-1 (ASR-1) equipped with two biomimetic ventral suckers. This robot can successfully crawl on a variety of substrates such as curved surfaces, sidewalls, and overhangs and against turbulent water currents with a flow speed of 2.4 m/s. In addition, we implemented a fixed-wing aircraft Adhesion Sliding Robot-2 (ASR-2) featuring midge larva-inspired suckers, enabling transit from rapid water surface gliding to adhesion sliding in an aquatic environment. This adhesion-sliding mechanism inspired by net-winged midge larvae may pave the way for future robots with long-term observation, monitoring, and tracking capabilities in a wide variety of aerial and aquatic environments.

This paper aims to comprehensively discuss the latest research developments in the field of underwater structural defect detection and health assessment. The underwater robots can carry the non-contact detection equipment such as optical and acoustic devices, as well as the contact-based detection equipment like ultrasonic instruments, making them important platforms for underwater structural detection methods/tools. This paper first introduces the current progress of underwater robots in the underwater structural detection, elucidating the research progress in autonomous and intelligent path planning algorithms for underwater robots. It discusses the advantages and disadvantages of non-contact defect detection methods for underwater structures based on optics and acoustics, as well as their adaptability to different detection objects and requirements. The research also investigates contact-based underwater defect detection methods and points out the limitations of such methods. It summarizes methods for the three-dimensional (3D) reconstruction using underwater images or video data, laying the foundation for precise localization and quantitative analysis of underwater structural defects. It discusses the mainstream methods for assessing the health of underwater structures, highlighting the shortcomings of existing methods. Finally, it identifies the challenges and future trends facing current underwater structural defect detection methods, providing direction for future research in the intelligent underwater structural detection and assessment. • This paper investigates the research status in underwater structure detection, and proposes future development directions. • This paper provides a comprehensive review of non-contact and contact based underwater structure detection methods. • This paper provides a comprehensive summary of the assessment methods and research directions for underwater structures. [ABSTRACT FROM AUTHOR]

A rapidly growing field of aquatic bio-inspired soft robotics takes advantage of the underwater animals’ bio-mechanisms, where its applications are foreseen in a vast domain such as underwater exploration, environmental monitoring, search and rescue, oil-spill detection, etc. Improved maneuverability and locomotion of such robots call for designs with higher level of biomimicry, reduced order of complex modeling due to continuum elastic dynamics, and challenging robust nonlinear controllers. This paper presents a novel design of a soft robotic fish actively actuated by a newly developed kind of artificial muscles—super-coiled polymers (SCP) and passively propelled by a caudal fin. Besides SCP exhibiting several advantages in terms of flexibility, cost and fabrication duration, this design benefits from the SCP’s significantly quicker recovery due to water-based cooling. The soft robotic fish is approximated as a 3-link representation and mathematically modeled from its geometric and dynamic perspectives to constitute the combined system dynamics of the SCP actuators and hydrodynamics of the fish, thus realizing two-dimensional fish-swimming motion. The nonlinear dynamic model of the SCP driven soft robotic fish, ignoring uncertainties and unmodeled dynamics, necessitates the development of robust/intelligent control which serves as the motivation to not only mimic the bio-mechanisms, but also mimic the cognitive abilities of a real fish. Therefore, a learning-based control design is proposed to meet the yaw control objective and study its performance in path following via various swimming patterns. The proposed learning-based control design employs the use of deep-deterministic policy gradient (DDPG) reinforcement learning algorithm to train the agent. To overcome the limitations of sensing the soft robotic fish’s states by designing complex embedded sensors, overhead image-based observations are generated and input to convolutional neural networks (CNNs) to deduce the curvature dynamics of the soft robot. A linear quadratic regulator (LQR) based multi-objective reward is proposed to reinforce the learning feedback of the agent during training. The DDPG-based control design is simulated and the corresponding results are presented.

This report describes a numerical and experimental study of a posture control device based on a movable float for portable underwater robots. We numerically analyzed the static stability using a stability curve and allowable spatial range of a center-of-gravity shift caused by a payload shift or manipulator configuration. Further, we proposed a feedback controller based on direct pitch and roll signals to change and maintain robot posture. We tested the feedback control using a numerical simulator and conducted experiments in a water tank using two portable underwater robots to demonstrate the effectiveness of the movable float device and proposed controller. The results of the field experiments showed that the device and proposed controller can be employed for effective underwater operations of portable underwater robots. [ABSTRACT FROM AUTHOR]

A review of robotic systems is carried out. The paper analyzes applied hardware and software solutions and summarizes the most common block diagrams of control systems. The analysis of approaches to control systems scaling, the use of intelligent control, achieving of fault tolerance, reducing the weight and size of control system elements belonging to various classes of robotic systems is carried out. The goal of the review is finding common approaches used in various areas of robotics to build on their basis a uniform methodology for designing scalable intelligent control systems for robots with a given level of fault tolerance on a unified component base. This part is dedicated to extreme robotics and the generalization of the conclusions for the whole review. The paper notes the possibility and necessity of developing common approaches to the creation of robots of various sizes and purposes. It is noted that, in contrast to many foreign countries, in the Russian Federation developers must consider the serious limitations on the electronics components, which do not allow for the free conversion of technologies between civilian, military and space robotics. In this regard, it is proposed to analyze the interchangeable Russian and foreign microelectronic components, to find technical solutions in the field of the control systems and communication between them, which could be implemented both in foreign and Russian microelectronics, and to create on their basis a conceptual model of scalable intelligent control system with a required level of fault tolerance. The model should be based on a unified set of components.

In this work, we present the design, implementation, and simulation of a topology-based navigation system for the UX-series robots, a spherical underwater vehicle designed to explore and map flooded underground mines. The objective of the robot is to navigate autonomously in the 3D network of tunnels of a semi-structured but unknown environment in order to gather geoscientific data. We start from the assumption that a topological map has been generated by a low-level perception and SLAM module in the form of a labeled graph. However, the map is subject to uncertainties and reconstruction errors that the navigation system must address. First, a distance metric is defined to compute node-matching operations. This metric is then used to enable the robot to find its position on the map and navigate it. To assess the effectiveness of the proposed approach, extensive simulations have been carried out with different randomly generated topologies and various noise rates.