Research on AGV Intelligent Path Planning Based on Genetic Mechanism Improved Ant Colony Algorith

AGV intelligent path planning has always been an important problem, among which ant colony algorithm, inspired by ant colony foraging behavior, has been widely used, but its convergence speed is slow in the early stage and it is easy to fall into local optimal problems. Therefore, this paper proposes an improved ant colony algorithm based on genetic mechanism, which uses genetic algorithm to update the initial pheromone distribution in the early stage and ant colony algorithm to find the optimal path in the later stage. At the same time, new pheromone updating rules and pheromone threshold limiting rules are proposed. Finally, simulation experiments are carried out with maltlab. The experimental results show that the proposed algorithm is easier to obtain the global optimal path, reduces the probability of the algorithm falling into the local optimal solution, reduces the number of turns and reduces the cost of AGV.