AGV Path Planning Based on Improved Fusion Ant Colony Algorithm
Aiming at the problems of the traditional Ant Colony algorithm in AGV pathfinding,such as slow con-vergence speed,too many corners and not smooth enough,this paper proposes an improved fusion Ant Colony algorithm based on Ant Colony System(ACS)algorithm.Firstly,the heuristic function of ant colony system was im-proved by the potential field target attraction function.Secondly,an improved adaptive pseudo-random transfer strategy was used to introduce adaptive volatile factors into pheromone update.Then,a cubic B-spline curve smoot-hing strategy was used for optimization.Finally,the simulation experiment was carried out in raster map.The experi-mental results show that the improved algorithm can shorten the path length and reduce the number of corners,and improve the convergence and path smoothness of the algorithm.Compared with the traditional ant colony algorithm,it can significantly improve the pathfinding efficiency.
Ant colony algorithmPath planningArtificial potential field method
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