## 1 Introduction  
Unmanned underwater vehicles (UUVs) are generally divided into autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). AUVs are most frequently used for monitoring and scanning the ocean surface because of their high speed and autonomous capabilities [1]. Marine scientists routinely use ROVs to collect deep-sea data and samples. The avoidance of risks through human intervention in deep-sea research and exploration is an evident advantage of using ROVs, and remote operation allows numerous scientists and engineers to discuss incoming data and make collective decisions on operations from a shipboard control van [2]. In the development of UUVs, a suitable structural design and suitable motion control are the primary concerns, as is obtaining a general mathematical model for describing a complex, random, and nonlinear marine environment. Conventional control methods, such as proportional-integral-derivative (PID) control, are unsuitable, and finding a control method for an uncertain environment is necessary. At present, the commonly used methods for the navigation control of underwater vehicles are PID control, adaptive control, fuzzy logic control (FLC), and neural network control. To control UUVs, [PERSON] [3] applied fuzzy theory to the environmental adaptation problem and compared it with a traditional mathematical model of control for depth and heading control. [PERSON] [4] simulated a PID controller that met the expected depth control value. [PERSON] et al. [5] used an adaptive neural-fuzzy inference system for the depth control of an AUV. The system could adjust the fuzzy rule parameters by itself instead of requiring artificial adjustment. [PERSON] et al. [6] considered the most prominent advantages of FLC, further optimized by a genetic algorithm. [PERSON] [7] considered a controller composed of a linear-quadratic regulator (LQR) controller to maintain zigzag vertical movement for gliding and towing (proportional plus derivative) PD controllers to control elevator/rudder angles. [PERSON] et al. [8] designed an adaptive fuzzy stabilization controller for an underactuated surface vessel in the presence of unknown time-varying environmental disturbances and input saturation. [PERSON] et al. [9] reported the results of an experimental study conducted to compare the performances of different adaptive control methods for the depth control of a UUV, which represents a significant challenge compared with heading control due to increased noise, time delay, and thrust requirements. [PERSON] and [PERSON] [10] used an FLC method that enabled AUVs to navigate safely through obstacles to a goal, with the optimal path proven by their simulation results. [PERSON] et al. [11] devised a self-tuning fuzzy controller for application in submerged ROVs. In simulations, the vehicle exhibited a generally conservative and safe behavior during the training period, as is required in real-time on-site applications. Some studies have used scanning sonar to assist with the motion control of UUVs. [PERSON] et al. [12] and [PERSON] [13] used the artificial potential field concept to avoid obstacles. [PERSON] et al. [14] provided an obstacle avoidance algorithm that generated path elements to the trajectory planner, and the vehicle tracked the trajectory [12; 13; 14] using the potential field computation to avoid obstacles. Only computer simulations were performed. Once the pre-set obstacle had been detected, the vehicle directly moved around and passed the obstacle in order to complete the simulation. However, in a real environment the vehicle could not complete such movement without considering the vehicle control. [PERSON] and [PERSON] [15] presented a scanning sonar with a seabed gradient detection technique that could predict seabed and attitude changes. [PERSON] and [PERSON] [16] designed a vehicle-fixed occupancy grid method to plan an optimal path around obstacles [15; 16] using the image processing approach to identify the obstacles.