Jianggui Han

Multi-robot formations can extend sensing coverage and improve operational resilience, but their practical value depends on coordinated navigation through cluttered environments. Under mapless local sensing, existing methods face conflicting demands for obstacle avoidance, collective progress and formation recovery. This study presents Coupled ESRM–MFAM, a deterministic bio-inspired controller for six differential-drive robots. An Elastic Skin Reflex Model (ESRM) provides local obstacle-surface response, while a Muscle Fibre Actuation Model (MFAM) generates collective driving forces. Their direct combination, together with teammate-safety handling, integrates environmental response and formation coordination within a local control loop. Across 800 geometry-certified environments, the controller achieved a task completion rate of 75.00% within 120 s, compared with 56.50% for VFH+, the strongest tested baseline adaptation. This advantage remained significant after Holm correction and was reproduced on a separately generated 400-environment evaluation set. Component ablations supported the contributions of environmental response, collective actuation and teammate safety to timely completion. ROS2/Gazebo measurements further showed that six-robot controller computation remained within the budget required for 10 Hz control. These results support coupled environmental response and collective actuation as a practical approach to timely formation navigation under the tested local-sensing conditions.

A series of temperature impact tests were conducted on submersible rudder materials in a polar low-temperature environment. By using an impact testing machine and a scanning electron microscope, the impact performance of samples at different temperatures was tested, and the fracture morphologies were observed. A systematic study was conducted on the low-temperature impact mechanical behavior of two base materials and their welded joints. The results indicate that within the temperature range of −60 to 20 °C, the rudder base material 925A exhibits excellent low-temperature impact toughness; as the test temperature decreases, the fluctuation range of its impact absorption energy remains relatively small. At −60 °C, the shear lip region of the sample fracture forms a uniform and dense fine dimple structure, endowing the material with the highest crack propagation energy. As the temperature increases, the crack propagation energy tends to decrease. The impact test temperature has a significant zoning effect on the fracture morphology. At room temperature (20 °C), the fiber zone of the fracture presents a fine and deep dimple morphology, corresponding to a higher crack initiation energy. The experimental data obtained from this study provides theoretical and data support for the engineering application of submersible rudder materials under polar low-temperature conditions.

Underwater laser detection and communication in polar regions have attracted increasing research interest. The stochastic roughness of the water–ice interface causes the broadening of the incident beam angle and spatial energy uncertainty, posing critical challenges for cross-medium optical transmission. This study presents a theoretical model that couples seawater volumetric scattering attenuation with rough-interface effects in upward laser transmission. The water–ice interface is discretized using Beckmann–Gaussian microfacet geometry. From this, the analytical relationship between local surface normal distributions and Fresnel reflection/transmission coefficients is derived. This enables the quantitative characterization of the process by which interface roughness modulates optical transmission. A full-link energy normalization framework is established. It encompasses normalized seawater-segment power, interface transmission efficiency, and normalized sub-ice received power. Monte Carlo photon tracing is used to simulate scattering in polar seawater and stochastic coupling at the rough interface. The results are experimentally validated. The key findings are as follows: Rising seawater temperature increases absorption but reduces scattering resulting in enhanced overall attenuation and degraded energy transfer efficiency. Interface roughness broadens the local angle-of-incidence distribution, and beyond 30°, energy transfer efficiency drops markedly. As propagation distance increases, normalized received power decreases, the beam spot diameter increases, and collimation quality deteriorates. The simulation results show strong agreement with the experimental measurements. These findings provide theoretical support for sub-ice laser signal acquisition and polarization modulation in polar environments, offering practical guidance for system design in underwater optical communication applications.

To better address the operational requirements for emergency underwater ship maintenance, this study proposes the use of an underwater robotic arm instead of divers for cleaning submerged hull sections. Experimental analyses are conducted to validate the stability and feasibility of the constructed underwater robotic arm cleaning system. Initially, hydrodynamic analysis of the robotic arm was performed using the Morison equation. Through fluent dynamic simulations, the hydrodynamic moments on each robotic arm during cleaning operations were obtained, confirming that stress under typical seawater flow velocities remained within the rated limits. Subsequently, dynamic simulations were carried out to determine the joint driving torques in a fluid environment, quantify the influence of the hydrodynamic resistance on the joint torque, and verify the accuracy of the fluid dynamics model. Finally, motion control and underwater cleaning experiments were implemented on the system. Experimental results further corroborated the correctness of the fluid model and operational environment analysis, demonstrating the expected cleaning performance and providing both data and experimental support for practical underwater maintenance during long-distance ship voyages.

Gear faults are a frequent cause of rotating machinery breakdowns. There are two open issues in the current intelligent diagnosis model of gear faults. (1) Shallow models demand fewer data but necessitate feature extraction from raw signals, relying on prior knowledge. (2) Deep networks can adaptively extract fault features but require large datasets to train hyperparameters. In this paper, a novel fusion model, called CBAM-TCN-SVM, is proposed for intelligent gear fault diagnosis. It consists of a temporal convolutional network module (TCN), a convolutional block attention module (CBAM), and a support vector machine (SVM) module. More specifically, the frequency-domain sequence data are fed into the CBAM-TCN model, which effectively extracts deep fault features via multiple convolutional layers, channel attention mechanisms, and spatial attention mechanisms. Then, the SVM classifier is employed for intelligent classification. The fusion model combines the advantages of deep networks and shallow classifiers, addressing the issues that arise when the accuracy of fault diagnoses is constrained by the data scale and feature extractions rely on prior knowledge. The experiments result in the proposed method achieving a classification accuracy of 98.3% and demonstrate that it is a feasible approach for predicting gear faults.