The Optimization of path Planning for a Picking Robot Based on an Improved Ant Colony Algorithm

Regarding the issue that the convergence speed of traditional ant colony algorithms is often slow, the quality of the solution needs to be improved, and it is easy to fall into local optimal solutions. This article proposes an improved ant colony algorithm. Based on the traditional ant colony algorithm, combined with the suboptimal solution path obtained by optimizing the A* algorithm, introducing heuristic factors and pheromone volatilization factors and adjusting the deadlock update strategy to reduce the number of turns and avoid entering a deadlock state. Speed up the convergence speed of the algorithm and improve the quality of the solution. Experimental results show that compared with traditional ant colony algorithms, the improved ant colony algorithm in this paper can effectively reduce the number of turns while reducing the shortest path length and has strong stability.