AGV Path Planning Based on Improved Genetic Algorithm

Abstract

In order to solve the AGV vehicle path planning problem, this paper uses the improved genetic algorithm to makes improvements in each step on the basis of the traditional genetic algorithm. First, real number coding and heuristic population initialization are performed. The fitness function adds obstacle avoidance and smoothness modules to enhance reliability. When selecting operators, elite strategy with three-stage selection and roulette wheel gambling are adopted, and adaptive cross mutation factors are used for evolutionary operation. Finally, environment construction and experiment simulation are carried out through MATLAB. The results show that the improved genetic algorithm can effectively improve the efficiency of the original algorithm and solve the path planning problem.