A typical structure of an underwater glider (UG) includes a pair of fixed wings, and the hydrodynamic force driving the glider forward as descending or ascending in the water is generated primarily by the fixed wings. In this paper, a simplified glider motion model was established to analyze the dynamics in an easier way, and whose simulation results do not differ from the original one. Also, in the paper, the effects of the wing position and wing shape on the UG to the motion were studied. Since no direct analytic approach cannot be performed, the case study of the effects of six different wing positions and three wing shapes on gliding performances which are gliding speed, gliding angle and gliding path were performed through computer simulation. The simulation results revealed that when the fixed wing is located far from the buoyancy center to the tail end, more traveling range is achieved with less energy. Also, effect of the shape difference of the wings were analyzed. Shape changes did not show much difference on the travelling performance of the UG. In addition to these, the transient mode of the UG was studied. To control this, the PID controller for the position of the mass shifter and piston were applied. By application of the PID controller to the linearized dynamics equations, it was shown that the transient behavior of the UG was quickly and steadily controlled.

Real-time command and data transfer are essential for autonomous underwater vehicle (AUV) motion control in underwater missions. Due to the limitations of underwater acoustic communication, which has a low data rate, this paper introduces a new control structure using underwater wireless optical communication (UWOC) to enable effective real-time command and data transfer. In this control structure, control inputs for the AUV attitude from outside of the water are transferred to the AUV for motion control, while its orientation data and visual images from the AUV camera are sent to the control station outside the water via the UWOC system. For demonstrating the performance of control action and data monitoring, an AUV is built with a constructed UWOC system, two vertical thrusters, and two horizontal thrusters. For attitude control of the AUV, an attitude heading reference system (AHRS) and a depth sensor are installed. Bi-directional communication in the UWOC system is achieved using a return-to-zero (RZ) modulation scheme for faster, longer-range data transfer. A signal processor converts sensor data received from the transmitted data. Finally, the hovering control performance of the AUV equipped with the UWOC system was experimentally evaluated in a water tank, achieving average root mean square errors (RMSEs) of 4.82° in roll, 2.49° in pitch, and 1.99 mm in depth, while simultaneously transmitting real-time motion data at 21.2 FPS with VGA-resolution images (640 × 480 pixels) at a communication rate of 1 Mbps.

The twin hybrid autonomous underwater vehicle (THAUV) is a novel type of unmanned underwater platform that consists of a twin torpedo-shaped hull and is actuated by two buoyancy engines and two thrusters proposed in this paper. The THAUV was designed to have faster speed generated by the two buoyancy engines and two thrusters. The two buoyancy engines on each hull and the airfoil are mainly responsible for the diving and surfacing motion, and the thrusters drive the THAUV along the horizontal plane. The THAUV is capable of carrying more instrumentation and energy than a conventional hybrid autonomous underwater vehicle (HAUV) with a single buoyancy engine such that the THAUV can perform more exploration tasks and operate for a longer period in a one-time operation. Different from other unmanned underwater vehicles (UUVs) with two airfoils or wings, the THAUV has a single airfoil connecting the twin hull such that it does not require connecting bars and additional airfoils. For this reason, the structure of THAUV is more compact and simpler. In this paper, a new compact THAUV is designed and CFD simulation is used to obtain the hydrodynamic parameters of THAUV operation in water. The motion model of the THAUV is also established and the operating parameters of the THAUV are obtained by simulation.

Monocular 3D object detection is a challenging task in autonomous systems due to the lack of explicit depth information in single-view images. Existing methods often depend on external depth estimators or expensive sensors, which increase computational complexity and complicate integration into existing systems. To overcome these limitations, we propose AuxDepthNet, an efficient framework for real-time monocular 3D object detection that eliminates the reliance on external depth maps or pre-trained depth models. AuxDepthNet introduces two key components: the Auxiliary Depth Feature (ADF) module, which implicitly learns depth-sensitive features to improve spatial reasoning and computational efficiency, and the Depth Position Mapping (DPM) module, which embeds depth positional information directly into the detection process to enable accurate object localization and 3D bounding box regression. Leveraging the DepthFusion Transformer (DFT) architecture, AuxDepthNet globally integrates visual and depth-sensitive features through depth-guided interactions, ensuring robust and efficient detection. Extensive experiments on the KITTI dataset show that AuxDepthNet achieves state-of-the-art performance, with ${AP}_{3D}$ scores of 24.72% (Easy), 18.63% (Moderate), and 15.31% (Hard), and ${AP}_{BEV}$ scores of 34.11% (Easy), 25.18% (Moderate), and 21.90% (Hard) at an IoU threshold of 0.7.

This paper conducts a comprehensive analysis of undulating and oscillatory movements in fish, utilizing numerical simulations to explore correlations among fin thrust and swimming speed. The study distinguishes itself through a unique approach by employing kinematic equations of motion control, specifically in oscillation and undulation, for computational fluid dynamics. Despite increasing energy loss with undulation, the study reveals a reduction in power demand with oscillation, underscoring its effectiveness in achieving desired speeds. The dynamics of undulating fins in aquatic and aerial locomotion remain insufficiently understood. The trade-off between more energy-consuming but highly propulsive movements or simpler and faster movements requires sophisticated design techniques to reduce volume. The geometry, developed using Rhino 6 software, incorporates precise fluid resistance calculations conducted with Ansys Fluent 19. Spanning flow velocities from 1 to 4 m/s were used for the simulation condition. Critical factors such as flexibility, viscosity, and shape change were meticulously examined for their impact on efficiency enhancement.

This paper presents an innovative approach that integrates a fixed-time control (FTC) algorithm with an improved sparrow search algorithm (ISSA) to enhance the trajectory tracking accuracy of a two-degree-of-freedom (two-DOF) robotic arm. The FTC algorithm, which incorporates barrier Lyapunov function (BLF) and adaptive neural network strategies, ensures rapid convergence, effective vibration suppression, and the robust handling of system uncertainties and input saturation. The ISSA, inspired by the foraging behavior of sparrows, improves search efficiency through dynamic weight adjustments and chaotic mapping, balancing global and local search capabilities. By optimizing control parameters, ISSA minimizes tracking errors. Simulation results demonstrate that the combined FTC and ISSA approach significantly reduces tracking errors and improves response speed compared to the use of FTC alone, underscoring its potential for achieving high-precision control in robotic arms and offering a promising direction for precise robotic control applications.