This paper investigates the station-keeping control of autonomous and remotely-operated vehicles (ARVs) for free-floating manipulation under model uncertainties and external disturbances. A modified adaptive generalized super-twisting algorithm (AGSTA) enhanced by adaptive tracking differentiator (ATD) and reduced-order extended state observer (RESO) is proposed. The ATD is used to obtain the smooth reference signal and its derivative. The RESO is used to estimate and compensate for the model uncertainties and external disturbances in real-time, which enhances the robustness of the controller. The modified AGSTA ensures the fast convergence of the system states and maintains them in a predefined neighborhood of origin without overestimating control gains. Besides, the proposed new variable gain strategy completely avoids the control gains vibrating near the set minimum value. Thanks to the RESO, the proposed controller is model-free and can be easily implemented in practice. The stability of the closed-loop system is analyzed based on Lyapunov’s direct method in the time domain. Finally, the proposed control scheme is applied to the station-keeping control of Haidou-1 ARV, and the simulation results confirm the superiority of the proposed control scheme over the original AGSTA.

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.

Understanding how intruders interact with granular media is critical for both fundamental physics and engineering applications. In this work, we investigate how different disturbance strategies —internal vibration and lateral wheels— modulate drag and lift forces on a body towed through a granular bed. A modular test platform was developed to compare six configurations under identical conditions: a smooth baseline, vibration, locked wheels, active wheels, passive wheels, and a vibration-assisted passive condition. Direct force measurements reveal that vibration significantly reduces drag and decreases the magnitude of reverse lift by fluidizing grains and weakening force chains. In contrast, wheel-based strategies operate through surface interaction: locked wheels amplify both drag and reverse lift, active wheels provide only partial relief relative to the locked case and still exceed baseline forces, while the passive wheels achieve modest drag reduction and exert only a minimal influence on reverse lift through their adaptive alignment with the grain flow. These results demonstrate two distinct pathways for resistance reduction: energetic fluidization and passive compliance. Beyond their engineering relevance, the findings provide new physical insights into how granular compaction, force-chain stability, and structural adaptation govern drag and lift forces in particulate media.

Using cable transmission in underwater manipulators helps to reduce the mass and rotational inertia of distal moving components, but the control performance of cable-driven joints is affected by flexible cable transmission, equivalent joint-side friction, hydrodynamic effects, and external disturbances. This paper proposes a control method combining time-delay estimation (TDE) with gain-scheduled sliding mode control (GSMC) for a cable-driven joint of an underwater manipulator. TDE uses delayed control-input and joint-acceleration data to estimate and compensate for the lumped dynamic term in the equivalent joint model online. GSMC employs a composite sliding surface and an error-dependent gain-scheduling mechanism to suppress trajectory-tracking errors in the presence of the TDE estimation residual. In joint-level MATLAB/Simulink R2024b simulations, smooth-step, sinusoidal-trajectory-tracking, and ablation results under predefined combined-uncertainty conditions, together with the results of 50 paired Monte Carlo runs, show that TDE-GSMC achieves the lowest major tracking-error indices among the four methods for the smooth-step and $0.35\mathrm{Hz}$ sinusoidal trajectories and also yields the lowest mean tracking error and 95th percentile of the disturbance peak in the Monte Carlo simulations; the ablation results further characterize the performance differences among the tested controller configurations.