Research On Multi-Storage AGVS Scheduling And Path Planning Based On Charging Cost

Multi-storage AGV(MSAGV)is Automatic Guide vehicles with multiple storage Spaces.It can load multiple bins at the same time,which can complete multiple sorting tasks in a single departure.With the advantages of high vehicle utilization and transportation efficiency,it is especially suitable for high-density warehousing and logistics scenarios.Aiming at the problem of path planning in MSAGVS transportation system,the existing planning algorithm fails to consider the charging cost,because MSAGVs need to drive out of the storage area when charging,which leads to high charging cost and waste of transportation efficiency of the system.The high cost of charging means that the vehicle needs to spend more power on the charging path than on the path used for fetching tasks,reducing the vehicle utilization rate.Secondly,for MSAGV conflicts in the transportation system,currently most of them are solved by stopping and waiting strategy.However,for deadlock conflicts,stopping and waiting will lead to congestion,which will lead to paralysis of the transportation system.This paper mainly studies the MSAGV scheduling and route planning from the aspect of lifting the vehicle power limit,establishes the multi-task combination model based on no charge constraint,proposes the task group scheduling strategy based on hybrid genetic algorithm and solves the route conflict problem of each MSAGVS,and finally realizes the overall goal of transporting multiple tasks in the MSAGVS transportation system.The main research contents are as follows:(1)In order to establish a map model for the MSAGVS transportation system and determine the driving route of each MSAGV considering the charging cost,this paper uses the grid method to establish a single channel bidirectional electronic map of the actual working environment.In order to timely charge MSAGVs,multiple charging points are set up in the working environment according to the number of MSAGVs and the scale of storage area.Secondly,combining the MSAGV timely nearby charging idea with the traditional A~*pathfinding algorithm,A~*pathfinding algorithm considering the charging constraint is proposed.The charging route designed by the algorithm is basically consistent with the driving route of the vehicle without charging,that is,the extra charging cost is 0.(2)In order to determine the tasks to be acquired by each MSAGV and the order of acquisition,in this paper,aiming at the multi-task combination model,ignoring its power limitation problem,a multi-task combination model was established with the total traveling distance of MSAGV,the longest traveling distance of MSAGV and the number of vehicles involved in transportation as targets,and the task group reconstruction was realized under the batch combination strategy.Then,a chromosome combining task and vehicle number was put forward,and the task responsible for each vehicle was sorted in order of execution by combining Dijkstra.Then,the CRITIC weight method was used for evaluation,screening,and cross mutation.Finally determine the task execution content of each vehicle and the order of task acquisition.Experimental simulation results show that compared with the original genetic algorithm,the hybrid genetic algorithm proposed in this paper greatly improves the quality of understanding and speeds up the running speed of the algorithm.(3)Aiming at the problem that MSAGV has too many turns and is easy to enter the congested section in static path planning,this paper improves algorithm A considering charging constraint.The optimized algorithm can effectively reduce the number of vehicle turns,thus improving the transportation efficiency.At the same time,the conflicts are classified and the corresponding solutions are proposed.