Research On Warehouse Path Planning Strategy Based On Insect Intelligent

With the development of the logistics industry,it is an important part of the warehouse management to meet the customer's demands in accordance with the order requirements.The rapid completion of goods picking has become a main obstacle in the development of enterprises.At present,automated guided vehicle(AGV)is often used in automated warehouse to realize the automation and informatization of warehouse.The optimal operation algorithm of AGV is the popular research direction.In order to improve the efficiency of warehouse operation,save the time of goods picking,and solve the problems of high installation cost and time-consuming existing in the warehouse management based on the traditional centralized automatic control system,the path planning strategy of AGV in the warehouse,which is the key problem of automatic warehouse,is studied based on the insect intelligent system structure.First of all,based on insect intelligent,the warehouse daily management system is studied,the warehouse goods query,access and fault alarm functions are designed.several commonly used map modeling methods are analyzed,the warehouse two-dimensional grid map and the warehouse topology map are established,and the warehouse map environment is simplified.Secondly,a warehouse path planning method based on parallel ranking ant colony algorithm is proposed,which improves the solution speed and efficiency of the ant colony algorithm through the cooperative growth and co-evolution of multiple ant colonies.Then,the generated initial path is smoothed by reducing the intermediate nodes.Compared with other common algorithms,the experimental results show that theproposed algorithm can solve the path planning of single AGV faster,better and more stable,and the smoothed path has shorter length and fewer turns.Thirdly,the ant colony algorithm and routing information protocol(RIP)algorithm are proposed based on insect intelligence system structure for path planning.The experimental results show that the ant colony algorithm based on insect intelligence system structure is better and faster than the traditional algorithm when solving the path planning problem.After the initialization of warehouse data,the routing protocol algorithm can get the optimal path information directly.Compared with other algorithms,this method is more accuracy.Finally,the dynamic collision avoidance game model between multi-AGVs in the warehouse is studied.The virtual action method is used to solve the problem of multi-Nash equilibrium selection.The adjacent AGVs play games with each other and choose the best action from the action concentration.The experimental results show that this method can find the Nash solution among multi-AGVs faster,realize the behavior decision of AGVs,and then complete the dynamic collision avoidance of multi-AGVs.Compared with the existing algorithm,the proposed path planning algorithm can effectively reduce the path length when picking goods,avoid collision when multi-AGVs are running at the same time,which provides an effective method to solve the dynamic path planning problem of AGV in warehousing operations.