Abstract: In order to solve the autonomous navigation problem of AUV, an improved ant colony optimization algorithm is used to study the path planning problem of AUV in complex underwater environment. Firstly, an underwater three-dimensional environment model is established based on the grid method. In this model, each ant uses the combination of layered forward and grid plane method to search the path. The energy consumption model and path planning mathematical model of AUV are determined by the speed of AUV and the force on the bottom. Based on the traditional ant colony algorithm, Dijkstra algorithm is used to improve the initial pheromone allocation. Considering the effect of underwater flow, the energy consumption of different path points is different, so a new heuristic function is constructed to eliminate the influence. The convergence speed and solution quality of the algorithm are optimized by pheromone replacement based on linear regression. Finally, based on the path planned by the improved ant colony algorithm, Bessel curve is used to improve the smoothness of the path, so as to facilitate AUV to track the path. The experimental results show that the improved ant colony algorithm has strong global search ability, the convergence speed is significantly faster, and the path planning is obviously better than the traditional ant colony algorithm and genetic algorithm, which is suitable for the path planning of underwater vehicles.

Key words: AUV path planning, autonomous underwater vehicle, ant colony algorithm, pheromone, underwater environment modelling, Dijkstra algorithm, linear regression model