Search Results (19)

Bilateral teleoperation systems have been widely used in many fields of robotics, such as industrial manipulation, medical treatment, space exploration, and deep-sea operation. Delays in communication, known as an inevitable issues in practical implementation, especially for long-distance operations and challenging communication situations, can destroy system passivity and potentially lead to system failure. In this work, we address the time-delayed three-channel teleoperation design problem to guarantee system passivity and achieve high transparency simultaneously. To realize this, the three-channel teleoperation structure is first reformulated to form a two-channel-like architecture. Then, the wave variable technique is used to handle the communication delay and guarantee system passivity. Two novel wave variable compensators are proposed to achieve delay-minimized system transparency, and energy reservoirs are employed to monitor and regulate the energy introduced via these compensators to preserve overall system passivity. Numerical studies confirm that the proposed method significantly improves both kinematic and force tracking performance, achieving near-perfect correspondence with only a single-trip delay. Quantitative analyses using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Dynamic Time Warping (DTW) metrics show substantial error reductions compared to conventional wave variable and direct transmission-based three-channel teleoperation approaches. Moreover, statistical validation via the Mann–Whitney U test further confirms the significance of these improvements in system performance. The proposed design guarantees passivity with any passive human operator and environment without requiring restrictive assumptions, offering a robust and generalizable solution for teleoperation tasks with communication time delay. Full article

The reliability and continuity of data are key issues in deep-sea sediment cone penetration testing. Cone penetration testing employs static force to uniformly insert rods into sediment, a process crucial for assessing its mechanics and layering. The clamping manipulator can perform this operation while accommodating sediment sensors of varying types and sizes. However, its requirement to reset post-penetration creates zero-velocity points that diminish test continuity and should be minimized. To address these limitations, this paper proposes a load-adaptive sediment rig that minimizes zero-velocity points, ensures data continuity, and contributes to sedimentology research. This paper analyzes the mechanical properties and layering patterns of sediment, along with the interaction mechanisms between sediment and mechanical structures. Subsequently, a mechanical structure–sediment-integrated model with adaptive control logic is established. Finally, real sediment data are introduced into the physical model for simulation experiments. The simulation results demonstrate that the load-adaptive rig reduces data breakpoints by 50% and increases the maximum single penetration stroke to 1.8 m. Additionally, the load-adaptive rig provides redundancy between penetration force and stroke, automatically reducing penetration force for greater stroke when encountering low-strength sediments and, conversely, sacrificing part of the stroke for greater force. These improvements significantly enhance the continuity of in situ detection data of sediment. Full article

The remotely operated vehicle (ROV), a vital deep-sea platform, offers key advantages, including operational duration via continuous umbilical power, high task adaptability, and zero human risk. It has become indispensable for deep-sea scientific research and marine engineering. To enhance surveys of cobalt-rich crusts (CRCs) on complex seamount terrains, the 4500-m-class Haima ROV integrates advanced payloads, such as underwater positioning systems, multi-angle cameras, multi-functional manipulators, subsea shallow drilling systems, sediment samplers, and acoustic crust thickness gauges. Coordinated control between deck monitoring and subsea units enables stable multi-task execution within single dives, significantly improving operational efficiency. Survey results from Caiwei Guyot reveal the following: (1) ROV-collected data were highly reliable, with high-definition video mapping CRCs distribution across varied terrains. Captured crust-bearing rocks weighed up to 78 kg, drilled cores reached 110 cm, and acoustic thickness measurements had a 1–2 cm margin of error compared to in situ cores; (2) Video and cores analysis showed summit platforms (3–5° slopes) dominated by tabular crusts with gravel-type counterparts, summit margins (5–10° slopes) hosting gravel crusts partially covered by sediment, and steep slopes (12–15° slopes) exhibiting mixed crust types under sediment coverage. Thicker crusts clustered at summit margins (14 and 15 cm, respectively) compared to thinner crusts on platforms and slopes (10 and 7 cm, respectively). The Haima ROV successfully investigated CRC resources in complex terrains, laying the groundwork for seamount crust resource evaluations. Future advancements will focus on high-precision navigation and control, high-resolution crust thickness measurement, optical imaging optimization, and AI-enhanced image recognition. Full article

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units. Full article

With the advancement of deep-sea exploration, the demand for underwater manipulators capable of long-duration heavy-duty operations has intensified. Water-hydraulic systems exhibit less viscosity variation with increasing depth than oil-based systems, offering better adaptability to deep-sea conditions. Using seawater as the driving medium inherently eliminates issues such as oil contamination by water, frequent maintenance limiting underwater operation time, and environmental pollution caused by oil leaks. This paper introduces a deep-sea manipulator directly driven by seawater from the deep-sea environment. To address the challenges of weak lubrication and high corrosion associated with water hydraulics, a reciprocating plunger seal was adopted, and a water-hydraulic actuator was developed. The installation positions of actuator hinges and maximum output force requirements were optimized using particle swarm optimization (PSO), effectively reducing the manipulator’s self-weight. Through kinematic and inverse kinematic analyses and joint performance tests, a six-degree-of-freedom water-hydraulic manipulator was designed with a maximum reach of 2.5 m, a lifting capacity of 5000 N, and end-effector positioning accuracy within 18 mm. Full article

In an effort to enhance the efficiency and accuracy of deep-sea manganese nodule grasping behavior by a manipulator, a novel approach employing an improved YOLOv5 algorithm is proposed for the extraction of the shortest paths to manganese nodules targeted by the manipulator. The loss function of YOLOv5s has been improved by integrating a dual loss function that combines IoU and NWD, resulting in better accuracy for loss calculations across different target sizes. Additionally, substituting the initial C3 module in the network backbone with a C2f module is intended to improve the flow of gradient information while reducing computational demands. Once the geometric center of the manganese nodules is identified with the improved YOLOv5 algorithm, the next step involves planning the most efficient route for the manipulator to pick up the nodules using an upgraded elite strategy ant colony algorithm. Enhancements to the ACO algorithm consist of implementing an elite strategy and progressively decreasing the number of ants in each round. This method reduces both the number of iterations and the time required for each iteration, while also preventing the occurrence of local optimal solutions. The experimental findings indicate that the improved YOLOv5s detection algorithm boosts detection accuracy by 2.3%. Furthermore, when there are fewer than 30 target planning points, the improved algorithm requires, on average, 24% fewer iterations than the ACO algorithm to determine the shortest path. Additionally, the speed of calculation for each iteration is quicker while still providing the optimal solution. Full article

Underwater manipulation is one of the most significant functions of the deep-sea crawling and swimming robot (DCSR), which relies on the high-accuracy control of the body posture. As the actuator of body posture control, the position control performance of the underwater mechanical leg (UWML) thus determines the performance of the underwater manipulation. An adaptive super-twisting sliding mode control method based on the extended state observer (ASTSMC-ESO) is proposed to enhance the position control performance of the UWML by taking into account the system’s inherent nonlinear dynamics, uncertainties, and the external disturbances from hydrodynamics, dynamic seal resistance, and compensation oil viscous resistance. This newly designed controller incorporates sliding mode (SMC) feedback control with feedforward compensation of the system uncertainties estimated by the ESO, and the external disturbances of the hydrodynamics by fitting the parameters, the dynamic seal resistance, and the compensation oil viscous resistance to the tested results. Additionally, an adaptive super-twisting algorithm (AST) with integral action is introduced to eliminate the SMC’s chattering phenomenon and reduce the system’s steady-state error. The stability of the proposed controller is proved via the Lyapunov method, and the effectiveness is verified via simulation and comparative experimental studies with SMC and the adaptive fuzzy sliding mode control method (AFSMC). Full article

This paper presents the design and mathematical model of an innovative smart crane, CHAYA-SC, based on the principle of a cable-driven parallel manipulator, as well as its stabilization. This crane is mounted on the airship hold and intended for handling at altitude. Our objective is to design a precise light crane that can be used for container loading or unloading, particularly in deep-sea ports. Thus, the model developed includes the oscillations as well as the transverse and longitudinal vibrations of the heavy cable supporting the load to be handled. The highly nonlinear partial differential equations (PDE) and ordinary derivative equations (ODE) that govern the motion of the system are obtained via the Lagrange equations coupled with a modal synthesis. So that the mathematical model of the system is compatible with control and real time, we developed a simplified dynamic model which proved to be equivalent to the complete model. As a first validation of the modelling, a simple control vector is applied to stabilize the airship and its load under the effect of a squall. Numerical simulations are presented at the end of the paper to show the relevance of the design. Full article

The force tracking control of deep-sea hydraulic manipulator systems with long transmission pipelines is disposed via fuzzy adaptive backstepping control based on an extended state observer in this paper. The pipeline model is established and then used to estimate the pressures in cylinder chambers, which are used to obtain the output force. In this process, the velocity of the piston, which is unmeasured, is needed, and an extended state observer is constructed to estimate the unmeasurable velocity signal. To cope with parameter uncertainties caused by changes in working depth, an adaptive algorithm is combined with the backstepping controller. Fuzzy logic is employed to design self-tuners that can automatically adjust the control parameters to guarantee force control performance from shallow seas to deep seas. The experimental results illustrate the success of the proposed control method. Comparative experimental results demonstrate that the extended state observer-based fuzzy adaptive backstepping controller has a relatively better tracking performance in different working conditions. Full article

Six-axis force/torque sensors are widely installed in manipulators to help researchers achieve closed-loop control. When manipulators work in comic space and deep sea, the adverse ambient environment will cause various degrees of damage to F/T sensors. If the disability of one or two dimensions is restored by self-restoration methods, the robustness and practicality of F/T sensors can be considerably enhanced. The coupling effect is an important characteristic of multi-axis F/T sensors, which implies that all dimensions of F/T sensors will influence each other. We can use this phenomenon to speculate the broken dimension by other regular dimensions. Back propagation neural network (BPNN) is a classical feedforward neural network, which consists of several layers and adopts the back-propagation algorithm to train networks. Hyperparameters of BPNN cannot be updated by training, but they impact the network performance directly. Hence, the particle swarm optimization (PSO) algorithm is adopted to tune the hyperparameters of BPNN. In this work, each dimension of a six-axis F/T sensor is regarded as an element in the input vector, and the relationships among six dimensions can be obtained using optimized BPNN. The average MSE of restoring one dimension and two dimensions over the testing data is $1.1693\times {10}^{-5}$ and $3.4205\times {10}^{-5}$, respectively. Furthermore, the average quote error of one restored dimension and two restored dimensions are $8.800{\times 10}^{-3}$ and $8.200{\times 10}^{-3}$, respectively. The analysis of experimental results illustrates that the proposed fault restoration method based on PSO-BPNN is viable and practical. The F/T sensor restored using the proposed method can reach the original measurement precision. Full article

With persistent ocean exploration, the complexity of deep-sea intervention is gradually increasing. The deep-sea manipulator is the primary tool to complete complex intervention. The manipulator dexterity determines the complexity of the task it can perform. First, a dynamic dexterity evaluation method is proposed based on the kinematics and dynamics characteristics of the deep-sea manipulator. This method takes into account the dynamic torque boundary and Jacobian mapping constraint, which are different from terrestrial manipulators. The concepts of the dynamic dexterity ellipsoid and dynamic dexterity measure are defined. Second, the effect of viscosity resistance on dexterity is analyzed. The viscosity resistance is optimized by selecting the most suitable compensation oil. Finally, the methods of dynamic dexterity evaluation and viscosity resistance optimization are verified by a simulated deep-sea experiment. The method proposed in this paper effectively improves the dynamic dexterity of the deep-sea manipulator by optimizing the viscosity resistance. The proposed method can also be used to evaluate and improve the dexterity of other underwater manipulators. Full article

The valve-controlled hydraulic cylinder system (VCHCS) is commonly utilized in the underwater manipulator, which is the most important tool for subsea tasks. Hydraulic oil viscosity is very sensitive to pressure. Therefore, when working at different depths under different ambient pressures in the sea, the hydraulic oil viscosity and the pipeline pressure loss in the deep-sea VCHCS vary greatly, which seriously affects the energy efficiency of the system. In addition, the control accuracy of the deep-sea VCHCS is also influenced by changes in the hydraulic oil viscosity and the pipeline pressure loss. In order to realize energy-saving control, this research introduces a proportional relief valve and develops a variable pump pressure control strategy. At the same time, a variable gain proportional-integral-derivative (PID) algorithm is designed to achieve precise control. A co-simulation model of the deep-sea VCHCS is then established, and many simulation analyses are carried out. Compared with traditional PID control with a constant pump pressure, the proposed method presents advantages such as lower energy consumption, better control accuracy, better resistance to load impact, and accuracy consistency under different working depths. Among them, when working at 11 km depth in the sea, the proposed method is capable of saving energy by 36.5% for the multi-step movement, by 30% for the harmonic movement, and by 47% for the complex movement. The present work in this research provides a solution that can realize energy saving and precise control of the deep-sea VCHCS at the same time in the wide span of depth in the sea. Full article

The valve-controlled hydraulic cylinder system (VCHCS) is commonly used for actuators such as manipulators in deep-sea equipment, whose working performance is crucial to subsea tasks. Affected by the ambient pressure introduced by the pressure compensator, the viscosity of the hydraulic oil increases significantly. On this basis, the viscosity changes further when flowing in the slender pipeline, making the pipeline pressure loss substantially increase and subsequently affecting the working performance of the deep-sea VCHCS. Aiming at this issue, a detailed nonlinear mathematical model of the deep-sea VCHCS is established, in which the viscosity-pressure characteristics of the hydraulic oil is considered to take the viscosity changes in the pipeline into account. Besides, the hydrodynamic effects are also included in the model. Then the corresponding numerical simulation model of the deep-sea VCHCS is established, and its working performance at different depths is simulated and analyzed. When the depth is 11km, the extension and retraction movements are delayed by 52.50% and 43.12% respectively. The root cause of the delay is then analyzed and discussed. Finally, the parameters that affect the working performance are studied, and suggestions to reduce or eliminate the delay phenomenon are given. The results can provide theoretical support for the performance optimization of the deep-sea VCHCS. Full article

In order to develop a gripping system or control strategy that improves scientific sampling procedures, knowledge of the process and the consequent definition of requirements is fundamental. Nevertheless, factors influencing sampling procedures have not been extensively described, and selected strategies mostly depend on pilots’ and researchers’ experience. We interviewed 17 researchers and remotely operated vehicle (ROV) technical operators, through a formal questionnaire or in-person interviews, to collect evidence of sampling procedures based on their direct field experience. We methodologically analyzed sampling procedures to extract single basic actions (called atomic manipulations). Available equipment, environment and species-specific features strongly influenced the manipulative choices. We identified a list of functional and technical requirements for the development of novel end-effectors for marine sampling. Our results indicate that the unstructured and highly variable deep-sea environment requires a versatile system, capable of robust interactions with hard surfaces such as pushing or scraping, precise tuning of gripping force for tasks such as pulling delicate organisms away from hard and soft substrates, and rigid holding, as well as a mechanism for rapidly switching among external tools. Full article

The collection of delicate deep-sea specimens of biological interest with remotely operated vehicle (ROV) industrial grippers and tools is a long and expensive procedure. Industrial grippers were originally designed for heavy manipulation tasks, while sampling specimens requires dexterity and precision. We describe the grippers and tools commonly used in underwater sampling for scientific purposes, systematically review the state of the art of research in underwater gripping technologies, and identify design trends. We discuss the possibility of executing typical manipulations of sampling procedures with commonly used grippers and research prototypes. Our results indicate that commonly used grippers ensure that the basic actions either of gripping or caging are possible, and their functionality is extended by holding proper tools. Moreover, the approach of the research status seems to have changed its focus in recent years: from the demonstration of the validity of a specific technology (actuation, transmission, sensing) for marine applications, to the solution of specific needs of underwater manipulation. Finally, we summarize the environmental and operational requirements that should be considered in the design of an underwater gripper. Full article