Recently, unmanned surface vehicles (USV) are being actively developed. For USVs to be put to practical use, it is necessary to secure safety by preventing collisions with general ships. To this end, USVs must avoid opposing vessels based on international regulations for preventing collisions at sea (COLREGs, 1972). This paper proposes an algorithm for USVs to avoid collisions with opposing vessels based on COLREG rules. The proposed algorithm predicts dangerous situations based on distance to closest point of approach (DCPA) and time to closest point of approach (TCPA). It allows USVs to avoid opponent ships based on the dynamic window approach (DWA). The DWA has been improved to comply with COLREGs, and we implemented a simulation and compared the standard DWA with the COLREG-compliant DWA (CCDWA) proposed in this paper. The results confirm that the CCDWA complies with COLREGs.

In this paper, we will propose the controller of a 2-DOF head in a snake robot for effective image data reading when the snake robot is driving, and present an error-based adaptive robust radial base function neural network back-stepping controller. The snake robot head system is a nonlinear system, and there are unmeasurable disturbances that occur while driving. To solve this problem, we use back-stepping controller and radial basis function neural network to compensate for unmeasurable disturbances to improve the steady state. In order to further compensate for the network approximation error that occurs during training or large signal changes, we use adaptive coefficients and error functions to approximate the network approximation error and design adaptive robust terms. Compared to the previous controller, the proposed controller can actively compensate for large signal changes and has the advantage of not generating residual input in a steady state. The proposed controller is based on Lyapunov function candidate to design an adaptive law and prove the system stability. The stability of the control system is proven through Lyapunov analysis and bounded. The proposed controller compared and verified the controller performance for two inputs through simulation and presented the efficiency of the controller.

This paper proposes a quadrotor system control scheme using an intelligent–proportional–integral–differential control (I-PID)-based controller augmented with a radial basis neural network (RBF neural network) and the proposed adaptive robust term. The I-PID controller, similar to the widely utilized PID controller in quadrotor systems, demonstrates notable robustness. To enhance this robustness further, the time-delay estimation error was compensated with an RBF neural network. Additionally, an adaptive robust term was proposed to address the shortcomings of the neural network system, thereby constructing a more robust controller. This supplementary control input integrated an adaptation term to address significant signal changes and was amalgamated with a reverse saturation filter to remove unnecessary control input during a steady state. The adaptive law of the proposed controller was designed based on Lyapunov stability to satisfy control system stability. To verify the control system, simulations were conducted on a quadrotor system maneuvering along a spiral path in a disturbed environment. The simulation results demonstrate that the proposed controller achieves high tracking performance across all six axes. Therefore, the controller proposed in this paper can be configured similarly to the previous PID controller and shows satisfactory performance.

Image stabilization is important for snake robots to be used as mobile robots. In this paper, we propose an adaptive robust RBF neural network nonsingular terminal sliding mode control to reduce swinging in the snake robot’s head while it is being driven. To avoid complex dynamic problems and reduce interference during driving, we propose a 2-DOF snake robot’s head system. We designed the control system based on the nonsingular terminal sliding mode control, which ensures a fast response and finite time convergence. To reduce chattering, we incorporated an RBF neural network that can compensate for disturbances. Additionally, we included an adaptive robust term to address the disadvantages of neural network-based control. The adaptive robust term generates control inputs based on the error and is used in conjunction with the reverse saturation function to eliminate chattering. The update law of the neural network and the adaptive robust term is designed based on Lyapunov’s theory. We proved the stability of the proposed controller by investigating finite time convergence before and after the reverse saturation function operation section. Finally, we verified the performance of the proposed controller through computer simulation. The simulation evaluates the controllers using a sinusoidal reference signal similar to snake robot movement and a mixed reference signal considering the controller’s waste case. The proposed controller has excellent tracking performance and improved chattering compared with the previous controller.

In this study, a motion control problem for the vessels towed by tugboats or towing ships on the sea is considered. The towed vessels, such as barge ships, are used for several purposes. Generally, these vessels have no power propulsion system and are towed using ropes and towing vessel (tugboats). The basic mathematical model of the towed vessel in which three active rudders are attached was introduced from a previous study. Owing to the dependency of the motions of the towed vessel to the towing ship, a method is suggested to cope with the undesirable disturbance and improve the tracking performance. For the simulation study, a model of the towed vessel with a towing ship is made, and necessary physical parameters are identified from the experiment. For the defined and linearized model, a control system is designed, and the control performance is also evaluated. A simulation study is conducted and the effectiveness of the proposed control strategy is verified

Owing to environmental constraints, it is challenging to stably conduct various missions or surveys of the seabed for a prolonged period in the marine environment. To address this challenge, several devices and technologies are being developed. In this study, we aimed to develop an unmanned underwater vehicle (UUV)—specifically, a towed underwater platform—that can be loaded and unloaded via joint operation with an unmanned surface vehicle, which can be connected to a wired cable to obtain a stable power supply and high-speed communication. In addition, various sensors for detection are employed to investigate the marine environment and conduct missions. Furthermore, we operated the developed UUV in actual waters, reviewed the results, and examined its practical operability.