Improved Ant Colony Algorithm for AGV Path Planning

Abstract

Given the shortcomings of the ant colony algorithm in the path planning process, such as low convergence speed and easiness of falling into local optimization, an improved ant colony algorithm (ACO) suitable for AGV path planning was proposed. The initial pheromone concentration was differentiated on the grid map according to the distance, which avoided the blind search in the early stage of the ant colony and sped up the convergence speed of the algorithm. The distance between the current grid and the grid to be selected and the distance between the grid to be selected, and the target grid were synthesized to improve the heuristic function to increase the direction of ant colony pathfinding. The dynamic heuristic factor was introduced to avoid the phenomenon of prematurity and falling into local optimization. It was proposed to label the direction of the adjacent grid of each grid, which increased the distance between the optimal path and obstacles, enhanced the security of the optimal path, avoided the occurrence of the dead corner phenomenon, and improved the robustness of the algorithm. The simulation results show that in the same environment, the improved algorithm's search efficiency and iterative stability are better than that of basic ACO algorithms in AGV path planning.