| At present,the level of automation in the manufacturing industry is not high,and most of them are at the level of semi-automation.With the continuous increase in labor costs,enterprises must carry out automation reforms to reduce the production cost of products and improve the price advantage of their products.AGV(Automated Guided Vehicle)is a product that integrates machinery,communication,automatic control and other technologies in one,Through wireless network technology,multiple AGVs are combined into a workshop logistics system to realize automatic transfer of workpieces.On this basis,automatic assignment of tasks such as workpiece processing,assembly,and transfer can be achieved through scheduling algorithms.The scheduling algorithm is a key part of connecting all links,through the optimization of the scheduling algorithm,not only can it realize automated production and monitoring,but also optimize the allocation of production resources,maximize the use of resources.This article takes the workpiece processing of the automated production line as the background,uses visual guidance AGV to complete the workshop's transportation tasks,and uses the scheduling algorithm to realize the reasonable use of the workshop's AGV,processing machinery and other limited resources,and finally finds a feasible path for AGV without conflicts.(1)First,the AGV experimental platform is built.On this basis,this article uses the Lab VIEW platform to process the image information collected by the camera,through image correction,segmentation,morphological processing,semantic extraction,and other operations.Obtained the trajectory deviation information during the operation of the AGV.Through experimental analysis,it was found that the platform can quickly identify the trajectory deviation of the AGV and meet the requirements of real-time computing.(2)According to the operating environment of AGV,the motion model and error model of AGV are established,then the fuzzy control algorithm is used to complete the deviation rectification of AGV.Finally,the simulink platform is used to build the motion model of the AGV.simulation experiments prove that the algorithm can achieve very good control effects.(3)According to the production environment of the workpieces on the production line,an electronic map of AGV movement is established,and a solution model for processing tasks and transportation tasks is established,then genetic algorithms are usedto find the optimal allocation scheme.However,the traditional genetic algorithm is easy to fall into the local optimal,so this paper uses an improved genetic algorithm to search for the optimal solution,introduces dynamic crossover and mutation probability,and when the algorithm performs crossover operation,the individuals are first sorted according to the adaptation value,then perform crossover operations in sequence.Through simulation experiments found that the task completion time obtained by the improved algorithm is146.Compared with the completion time 198 obtained by the traditional algorithm,it can be known that the improved algorithm has better optimization performance.(4)A method based on regional transit time control is adopted to solve the conflict problem of multiple AGV.On the premise of not affecting machine processing and other tasks performed by the AGV,the multi-AGV conflict problem is solved by adjusting the time of the AGV through each area,Finally,the particle swarm optimization is used to solve the problem,which proves that the method can find a path without path conflict. |
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