Research on AGV Path Planning Based on Gray wolf Improved Ant Colony Optimization

Abstract

To promote the performance of AGV to optimize the path in the obstacle environment, the paper proposes an improved ant colony algorithm combined with gray wolf optimization. First, Pre-search the path by the grey wolf algorithm, then the obtained optimal solution based on the grey wolf algorithm is introduced into the pheromone model of the ant colony algorithm to solve the invalid search caused by the lack of pheromone during the primary period. Second, modify the heuristic information, add corner constraints to the heuristic function to reduce the redundancy of paths. Third, the heuristic factors are updated adaptively, both of the factors dynamically adjust the importance of each other. Moreover, the conversion rate is introduced into the pseudo-random strategy to adjust the balance between certainty and randomness, which accelerates the convergence of the algorithm. The simulation data shows that the hybrid algorithm possesses good merit-seeking ability and has significant advantages in improving the path quality.