Path Planning of Unmanned Surface Vehicle Based on Improved Sparrow Search Algorithm

In order to solve the problem of many constraints and a complex navigation environment in the path planning of unmanned surface vehicles (USV), an improved sparrow search algorithm combining cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialisation stage, cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimise the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late iteration 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 modelled, and the navigation information of the original inland electronic navigation chart (ENC) is preprocessed, gridised, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in an 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.