Research on Path Planning of AGV Based on Improved Ant Colony Optimization Algorithm

Abstract

Path planning is a key problem in the motion control of mobile robot. In order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency, this paper puts forward a matrix yard storage mode, which is transformed into grid map model, and then uses ant colony optimization algorithm to plan the path of AGV. Aiming at the shortcomings of traditional ant colony optimization algorithm (ACO) in global path planning, such as slow convergence speed and weak optimization ability, an improved ant colony path planning algorithm is proposed. Firstly, the grid map is established, and the fruit fly optimization algorithm (FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm, and then the ant colony optimization algorithm is used for global path planning. At the same time, in order to solve the problem of many path turning angles and large cumulative turning angles in the planning, the path smoothing is carried out. The simulation results show that the improved algorithm has fewer turns and smoother path, and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.