A new bilevel algorithm for UUV global path planning

Global path planning is one of the key technologies in unmanned underwater vehicle (UUV) intelligent control. At present, research on UUV global path planning technology tends to choose long-distance and large-scale 3D space as the research environment, which leads to a sharp increase in the amount of data and search range for 3D spatial path planning. Therefore, an efficient and relatively small data volume 3D spatial path planning method is an urgent problem that needs to be solved for UUV engineering applications. To solve this problem, a new bilevel path planning algorithm for UUV is proposed. In the upper level of the algorithm, a Max Min Ant System-Elite Genetic (MMAS-EGA) algorithm is put forward, which is a hybrid ant colony optimization/genetic algorithm, in order to improve the convergence speed of the algorithm. In the lower level of the bilevel algorithm, a function optimization algorithm and the MMAS algorithm are used to minimize the number of variables to be optimized. To verify the effectiveness of the algorithm, we conducted simulation experiments in a three dimensional environment. The simulation results in the three-dimensional environment show that, compared with the existing bilevel algorithm, the time to search the global optimal solution is reduced by 9%, and the number of iterations is reduced by 4.4%. Furthermore, the new algorithm we proposed is more efficient and suitable for global path planning for different tasks.