The soft actuator possesses the characteristics of flexibility, environmental adaptability, and human–machine interaction. Firstly, aiming to resolve the limitation of variable stiffness performance of a traditional pneumatic artificial muscle (PAM) actuator, based on the antagonistic mechanism of extensor and contractor muscles, a novel pneumatic soft actuator coupled of extensor and contractor muscles is proposed in this paper. The actuator can perform the compound action of elongation/contraction, and the stiffness of it can be controlled by adjusting the elongation and contraction forces. Secondly, based on the deformation principle of woven and elastic fabric layers, the mechanical characteristics model of the actuator is established and simulated. The mechanical properties of the actuator are tested under different pressures and deformation displacement and the variable stiffness characteristics of the actuator are verified. Finally, actuators are utilized to manufacture a soft mechanical manipulator, which can achieve variable stiffness in a fixed bending attitude.

For decades, underwater vehicles have been performing underwater operations, which are critical to the development and upgrading of underwater robots. With the advancement of technology, various types of robots have been developed. The underwater robotic snake is a bioinspired addition to the family of underwater robotic vehicles. In this paper, we propose an innovative underwater snake robot actuated by rigid propulsions and soft joints, which can improve the swimming efficiency and flexibility of the robot and reduce the probability of collision leading to damage. Existing math models of robotic snakes typically incorporate only planar motion, rarely considering spatial motion. So, we formulate a complete three-dimensional dynamic model for the robotic snake, which is extended by deriving expressions for the geometric Jacobians. This modeling approach is well suited since it provides compact matrix expressions and easy implementation. We use the constant curvature method to describe the configuration of the soft joint, use the Lagrangian method to obtain its dynamic characteristics, and focus on deriving the visco-hyperelastic mechanical energy of the soft material. Next, the local dynamics of soft members are extended as a nonholonomic constraint form for modeling the snake robot. Finally, the multi-modal swimming behavior of the robot has been verified by simulations, including forward and backward rectilinear motion, yaw turning, pitch motion, and spiral rising motion. The overall results demonstrate the effectiveness and the versatility of the developed dynamic model in the prediction of the robot trajectory, position, orientation, and velocity.

Compared with rigid robots, soft robots have better adaptability to the environment because of their pliability. However, due to the lower structural stiffness of the soft manipulator, the posture of the manipulator is usually decided by the weight and the external load under operating conditions. Therefore, it is necessary to conduct dynamics modeling and movement analysis of the soft manipulator. In this paper, a fabric reinforced soft manipulator driven by a water hydraulic system is firstly proposed, and the dynamics of both the soft manipulator and hydraulic system are considered. Specifically, a dynamic model of the soft manipulator is established based on an improved Newton–Euler iterative method, which comprehensively considers the influence of inertial force, elastic force, damping force, as well as combined bending and torsion moments. The dynamics of the water hydraulic system consider the effects of cylinder inertia, friction, and water response. Finally, the accuracy of the proposed dynamic model is verified by comparing the simulation results with the experimental data about the steady and dynamic characteristics of the soft manipulator under various conditions. The results show that the maximum sectional error is about 0.0245 m and that the maximum cumulative error is 0.042 m, which validate the effectiveness of the proposed model.

Efficiently salvaging shipwrecks is of the utmost importance for safeguarding shipping safety and preserving the marine ecosystem. However, traditional methods find it difficult to salvage shipwrecks in deep water. This article presents a novel salvage technology that involves multiple hydraulic claws for directly catching and lifting a 2500-ton shipwreck at 600 m depth. To ensure lifting stability, a semi-active heave compensation (SAHC) system was employed for each lifter to mitigate the effects of sea waves. However, the response delays arising from the hydraulic, control, and filtering systems resist the heave compensation performance. Predicting the barge motion to mitigate measuring and filtering delays and achieve leading compensation is necessary for the salvage. Therefore, a multivariate long short-term memory (LSTM) based neural network was trained to forecast the barge’s heave and pitch motions, exhibiting satisfactory results for the next 5 s. According to the results of numerical simulations, the proposed LSTM-based motion predictive SAHC system demonstrates remarkable effectiveness in compensating for shipwreck motion.

With the rapid advancement of the marine economy, conventional salvage equipment has become increasingly inadequate in meeting the operational demands of complex aquatic environments and deep-sea salvage operations. This study presents the preliminary design of a novel salvage catamaran and proposes a multi-level fuzzy comprehensive evaluation framework for hydrodynamic performance under multi-sea-state and multi-operational conditions. A hydrodynamic performance evaluation indicator system was established, integrating resistance and seakeeping criteria. Computational fluid dynamics (CFDs) simulations with overset grids were employed to calculate the resistance characteristics. Potential flow-theory-based analysis quantified motion responses under irregular waves. The framework effectively distinguishes performance variations across five sea states and two sets of loading conditions through composite scoring. Key findings demonstrate that wave-added resistance coefficients increase proportionally with a significant wave height (Hs) and spectral peak period (Tp), while payload variations predominantly influence heave amplitudes. A fuzzy mathematics-driven model assigned entropy–Analytic Hierarchy Process (AHP) hybrid weights, revealing operational trade-offs: Case1-Design achieved optimal seakeeping and resistance, whereas Case5-Light exhibited critical motion thresholds. Adaptive evaluation strategies were proposed, including dynamic weight adjustments for long/short-wave-dominated regions via sliding window entropy updates. This work advances the systematic evaluation of catamarans, offering a validated methodology for balancing hydrodynamic efficiency and operational safety in salvage operations.