Over the past few decades, unmanned surface vehicles (USV) have drawn a lot of attention. But because of the wind, waves, currents, and other sporadic disturbances, it is challenging to understand and collect correct data about USV dynamics. In this paper, the Modified backpropagation neural network (BPNN) is suggested to address this issue. The experiment was conducted in the Qinghuai River, and the receiver collected the data. The modified BPNN outperforms the conventional BPNN in terms of ship trajectory forecasting and the rate of convergence. The updated BPNN can accurately predict the rotational velocity during the propeller’s acceleration and stability stages at various rpms.

Ship trajectory tracking controllers based on deep reinforcement learning (DRL) are widely applied in various fields such as autonomous driving and robotics due to their strong adaptive learning capabilities and optimization decision-making ability. However, ship trajectory control faces challenges such as long training cycles and poor convergence performance. These issues are primarily caused by the unreasonable design of algorithm models and reward functions, which limit the performance optimization and energy efficiency improvements in real-world navigation. In this paper, we propose a ship trajectory tracking control algorithm based on deep reinforcement learning. The proposed algorithm introduces maximum entropy theory and experience replay techniques. Additionally, it enhances the reward function module by adding reward terms and fitting weight designs. A three-dimensional simulation environment is constructed to validate the proposed method. The results demonstrate that the controller designed in this study outperforms traditional DRL controllers in terms of fast convergence, convergence stability, and final reward values. The controller meets the requirements for tracking conventional trajectories and shows stable and efficient performance in both wide-area water search experiments and river channel traversal experiments. These experimental results provide valuable insights for future research directions.

In order to solve the problem of many constraints and complex navigation environment in the path planning of unmanned surface vehicle(USV), an improved Sparrow search algorithm com-bining Cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialization stage, Cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimize the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late it-eration of the algorithm, the standard deviation of fitness is introduced to determine whether the population is trapped in the local optimum. If true, the Gaussian random walk strategy is used to perturb the optimal individual and assist the algorithm to escape the local optimum. Thirdly, the chosen water environment is modeled, and the navigation information of the original inland electronic navigation chart(ENC) is preprocessed, gridized, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in inland ENC grid environment. The experimental results show that, compared with the traditional sparrow search algorithm, the average fitness value of the path planned by improved sparrow search algorithm is reduced by 14.8%, and the variance is reduced by 49.9%. The path planned by the algorithm is of good quality and high stability, and combined with ENC, it can provide a reliable path for USV.