Global path planning for automated guided vehicle based on improved ant colony algorithm
                                              LIANG Jian gang, LIU Xiao ping, WANG Gang, HAN Song
                    (School of Automation, Beijing University of Posts and Telecommunication, Beijing 100876, China)

Abstract: Aiming at the problems of slow convergence speed and trapping into local minimum of global path planning for automated guided vehicle by traditional ant colony algorithm, a global path planning for AGV based on improved ant colony algorithm was proposed. At first, the environment models with obstacle were established as the basis for path planning by MAKLINK graph. Secondly, the improved ant colony algorithm was combined with dynamic weight goaloriented principle, then a new heuristic function was designed, to improve the probability of selecting the closer path to the target point, and reduce the probability of selecting the non shortest path. The pheromone was updated with the strategy of dynamic adjustment of pheromone decay parameter for improving the search efficiency. Finally, the improved ant colony algorithm was compared with the traditional ant colony algorithm by simulation experiment. The results indicate that compared to traditional ant colony algorithm, improvements can increase the convergence speed by nearly one time and improve path planning efficiency.

Key words: ant colony algorithm; path planning; automated guided vehicle（AGV）; MAKLINK graph