The lateral line plays a crucial role as a sensory organ in fish for detecting and interpreting flow field environments. Taking inspiration from the lateral line of fish, this study investigates the implementation of an artificial lateral line system on a biomimetic fish-shaped Unmanned Underwater Vehicle (UUV). To ensure accurate positioning of the UUV while considering physical dimensions, cost, and environmental friendliness (by avoiding the use of sonar), we propose a fusion method based on factor graph optimization that combines data from an Inertial Measurement Unit (IMU) and the artificial lateral line system (ALLS). This method integrates velocity information obtained from the artificial lateral lines with attitude information provided by the IMU, enabling the UUV to achieve precise self-positioning at a relatively low cost. Water tank experiments demonstrate the significant improvement in the UUV’s trajectory estimation ability achieved by our proposed method.

This paper presents an improved motion controller based on the backstepping method to address nonlinear control challenges in unmanned aerial–aquatic vehicles (UAAVs), enabling them to navigate between two different media. The nonlinear control approach is applied to UAAV motion control, incorporating filters to improve stability. The study designs motion controllers for three UAAV phases: underwater, in the air, and transitioning between media. Fluid simulations of the emergence process of the UAAV for future field experiments were conducted. By fine-tuning the simulations, a comprehensive understanding of the vehicle’s performance is obtained, offering crucial insights for the development of subsequent control systems. Simulation results confirm the controller’s ability to achieve target trajectory tracking with control system responses that meet practical requirements. The controller’s performance in attitude control and trajectory tracking is verified in underwater gliding, transmedia transitions, and airborne phases, demonstrating its effectiveness.

The lateral line in fish serves as an essential organ for perceiving the aquatic environment. Drawing inspiration from this biological system, the artificial lateral line system (ALLS), a type of underwater robot sensor, holds significant promise. Understanding the interaction between surface vessels and underwater ecosystems is crucial for environmental monitoring. The wake of a ship can have significant effects on marine life and sediment transport, and measuring these effects from underwater provides valuable data for ecological studies. The primary research objective of this study is to detect ship-generated waves using ALLS. To achieve this, we introduce a pipeline based on the improved multiple signal classification algorithm for detecting these waves with ALLS. The algorithm's effectiveness is verified through computational fluid dynamics simulation experiments. These experiments analyze the impact of the number of array elements, spacing between elements, and distance from the lateral line to the vessel's trajectory on the algorithm's performance. The experimental results reveal that increasing the number of array elements or spacing improves the algorithm's perception accuracy up to a certain extent. Conversely, as the distance from the lateral line to the vessel's trajectory increases, the accuracy of the algorithm decreases. Additionally, the proposed algorithm undergoes validation through water tank experiments, affirming the conclusions derived from the simulation experiments. By providing these research findings, this article offers valuable insights into advancing the understanding of surface moving target perception based on ALLS, which is essential for both technological development and ecological monitoring.