With the in-depth integration of research across multiple disciplines, such as biomimetics, robotics, and sensing technology, significant advancements have been made in swarm robotics technology, which has been applied in areas including drone swarms, mobile robot swarms, and underwater robot swarms. However, due to the limitations of underwater communication technologies, underwater robot swarms have lagged behind aerial and ground swarms in their development. This paper primarily explores the applications and advancements of swarm intelligence (SI) in multiple underwater robot swarms. Inspired by the behavior of animal swarms, researchers have translated this concept into the design and control strategies of underwater robot swarms. This approach draws on the self-organization, robustness, and adaptability inherent in collective behaviors, significantly enhancing the performance of underwater robot swarms. This paper provides a comprehensive review of the current research status of bio-inspired swarming of multiple underwater robots, including the design and classification of swarm underwater robots, SI algorithms and their applications in multiple underwater robots, and communication mechanisms for underwater robots. Furthermore, this paper highlights critical technical challenges that need to be addressed in research, along with proposed solutions, and discusses the vast application prospects of bio-inspired underwater swarming in military and civilian fields, providing clear directions for future research.

Measurement While Drilling (MWD) is the most commonly used real-time information acquisition technique in offshore intelligent drilling, its power supply has always been a concern. Triboelectric nanogenerators have been shown to harvest low-frequency vibrational energy in the environment and convert it into electricity to power small sensors and electrical devices. This work proposed a cantilever-beam-based triboelectric nanogenerator (CB-TENG) for transverse vibration energy harvesting of a drill pipe. The CB-TENG consists of two vibrators composed of spring steel with PTFE attached and Al electrodes. The structurally optimized CB-TENG can output a peak power of 2.56 mW under the vibration condition of f = 3.0 Hz and A = 50 mm, and the electrical output can be further enhanced with the increased vibration parameters. An array-type vibration energy harvester integrated with eight CB-TENGs is designed to fully adapt to the interior of the drill pipe and improve output performance. The device can realize omnidirectional vibration energy harvesting in the two-dimensional plane with good robustness. Under the typical vibration condition, the short-circuit current and the peak power can reach 49.85 μA and 30.95 mW, respectively. Finally, a series of demonstration experiments have been carried out, indicating the application prospects of the device.

This paper presents a visual localization system for underwater robots, aimed at achieving high-precision relative positioning and dynamic target tracking. A 3D AprilTag reference structure is constructed using a cubic configuration, and a high-resolution camera module is integrated into the AUV for real-time tag detection and pose decoding. By combining multi-face marker geometry with a fused state estimation strategy, the proposed method improves pose continuity and robustness during multi-tag transitions. To address pose estimation discontinuities caused by viewpoint changes and tag switching, we introduce a fusion-based observation-switching Kalman filter, which performs weighted integration of multiple tag observations based on relative distance, viewing angle, and detection confidence, ensuring smooth pose updates during tag transitions. The experimental results demonstrate that the system maintains stable pose estimation and trajectory continuity even under rapid viewpoint changes and frequent tag switches. These results validate the feasibility and applicability of the proposed method for underwater relative localization and tracking tasks.

For biological groups, the behaviors of individuals will have an impact on the alignment efficiency of the collective movement. Motivated by Vicsek’s pioneering research on self-organized particles and other related works about flocking behaviors, we propose two mathematical models based on the local information of individuals to include more realistic details in the interaction mechanism between individuals and the rest of the group during the flocking process. The local information of the individual refers to the local consistency, representing the degree of alignment with its neighbors. These two models are the self-introspection model, where the process of orientation adjustment of one individual is ruled by the degree of local consistency with the neighborhood, and the credit evaluation model, where the average orientation of the neighborhoods is weighed using the local consistency of the interacting individuals. Different metrics are calculated to analyze the effects of the model parameters and flocking parameters on groups. Simulation calculations indicate that the two improved models have certain advantages in terms of alignment efficiency for the group. Finally, the optimal model parameters are determined, and the effects of random noise on groups with a single behavior and mixed behaviors are analyzed. The results confirm that individuals with mixed behaviors still possess robustness against noise. This research would contribute to the further interdisciplinary cooperation that involves biology, ethology, and multi-agent complex systems.