Research On AGV Path Planning Algorithm Based On Group Intelligence Optimization

With the development of society and industrial technology,AGV has become one of the most important means of local logistics transportation in flexible industrial system and automation management system.By optimizing the AGV path planning,the working time cycle can be shorten,the work efficiency of the workshop can be improved,so the research is of great significance.The algorithm of path planning algorithm for automatic guided vehicle is one of the key technologies of AGV,which is characterized by non-linearity,complexity and constraint.Over the years,mathematical model based on the traditional algorithm Such as taboo,grid method,artificial field method and so difficult to achieve the desired results,there are poor robustness,poor precision,poor efficiency and other issues,however,the group Intelligent optimization algorithms based on the behavior of social insects simulation such as ant colony algorithm,cat group algorithm,etc.,with strong robustness,global optimization,parallelism and so on.Therefore,the main work of this thesis is to study the path planning algorithm of automatic guided vehicle based on group intelligence optimization.According to AGV application requirements,AGV path planning is divided into single target path planning and multi-objective path planning.By considering the conversion to the traveling salesman problem for robot path planning model in many papers,the path planning characteristics of the multi-objective AGV car are analyzed and the path planning is transformed into the TSP.Based on the group intelligence optimization algorithm,three algorithms are proposed to solve the problem of path planning of AGV system.Firstly,a discrete cat algorithm is proposed to solve the AGV path planning problem based on the idea of differential evolution algorithm to solve the TSP.By introducing the position-order coding,the cat algorithm is extended to the discrete domain so that it can be used to solve the TSP and the influence of the parameter setting on the algorithm is further analyzed.Secondly,two improved ant colony algorithms are proposed to solve the AGV path planning: one is the multi-population differential ant colony algorithm based on the global optimization of differential evolution algorithm.The improved algorithm is proposed to solve the problem of local optimization and convergence of ant colony.The simulation results show that the algorithm can improve the efficiency of the ant colony algorithm effectively;The ant colony algorithm based on the cat search is introduced to realize the local search of the ant colony itself around the current solution set.The simulation results show that the proposed algorithm can improve the efficiency of the ant colony algorithm,effectively,improve the accuracy of the algorithm.At the same time,the conflict problem in the path planning of multi-AGV system is analyzed,and proposes a solution which is named the local quadratic path planning based on ant colony algorithm to solve the cross conflict.Based on the ant colony algorithm for each AGV car to have pre-path planning,by detecting the cross grid to determine whether there is a conflict,and if there is a conflict,planning local secondary path for the lower priority car.The simulation results show that the proposed scheme can be used to solve the cross conflict problem in single goal multi-AGV system path planning.