Research On Path Planning Algorithm Of Material Conveying In Electronic Components Warehouse

With the advent of Industry 4.0,electronic product manufacturers have become an important part of economic development.The scale of the enterprise has expanded and the demand for production has increased.As the current path planning effect is not fast and accurate enough,it cannot fully adapt to environmental changes.Ant Colony Algorithm(ACA)has a wide application prospect in AGV path planning because of its strong robustness and excellent path finding ability.However,in the actual design process,the basic ant colony algorithm is very easy to fall into the local optimal solution,and the convergence speed is slow.So this paper discusses the problem of AGV path planning using ant colony algorithm,and optimizes the result of path searching by improved ant colony algorithm.The major tasks include:Firstly,this paper introduces the development of AGV and its path planning technology.Through analyzing the advantages and disadvantages of various algorithms and the research object and applicable space of this paper,the AGV path planning problem is finally solved by ant colony algorithm.The environmental model was built by grid method.The basic ant colony algorithm is simulated to verify its adaptability in AGV path planning and analyze its defects in path planning.The development of ant colony algorithm on AGV path planning and the way to improve it are presented.Secondly,based on the research of ant colony system,the state transition probability calculation method of ant colony algorithm is improved.The initial pheromone distribution is changed to non-uniform.The concept of Immune Genetic Algorithm(IGA)is fused and added to the part of the iteration of ACA.In the individual evaluation function,the turning term and the node obstacle term are added,which makes the comprehensive evaluation of a good path more in line with the expectation.The computational logic of crossover operator,mutation operator and immune operator is improved,and the combination of them and ant colony algorithm can solve the path planning problem more effectively.The Path planning process of AGV based on improved ant colony algorithm is introduced and verified by simulation.Finally,the dynamic path planning of AGV is studied by combining the improved ant colony algorithm with the rolling window.In this paper,the dynamic collision and its avoidance strategy are introduced.The parameter range of the improved ant colony algorithm is analyzed,and the dynamic planning steps of AGV are given and verified by simulation.Experimental results show that the improved algorithm can overcome the shortcomings of slow convergence and local optimization,and still has strong robustness with the increase of the complexity of the environment.