AGV Path planning based on improved adaptive genetic algorithm

Abstract

In order to solve the path planning problem of Automated Guided Vehicle (AGV) transporting goods and packages in warehouse logistics, this paper studies the Algorithm of the path planning problem, an improved adaptive genetic algorithm is presented to solve the path optimization problem. In order to avoid falling into local optimum, an improved self-adaptive crossover method is proposed. In order to avoid the conflict of AGV in the process of path planning, the concept of Congestion Coefficient is introduced into the design of fitness function, reduce the AGV in the path optimization process of conflict. The mathematical model of AGV to accomplish multi-task is established, and the comparative experiment is set up. The experimental results show that the improved adaptive genetic algorithm can improve the optimization performance by 4.2% in solving the optimal path problem, and improve the convergence speed by 4.2%, the improved algorithm has obvious advantages.