Multi-AGV Path Planning And Scheduling In The Intelligent Manufacturing Workshop

In recent years,with the rapid development of intelligent factories,the application and management of logistics system are particularly important.The AGV(Automated Guided Vehicle)used in manufacturing workshop can realize the automatic handling of materials,so as to improve the productivity of enterprises.However,the multi-AGV path planning and task scheduling technologies involved are the main application problems faced by manufacturing enterprises.These enterprises urgently need to optimize the scheduling of the whole logistics system to improve the operation level of the workshop,to ensure timely delivery.Therefore,the intelligent manufacturing workshop scheduling technology combined with multi-AGV path planning is the key technology for manufacturing enterprises to implement intelligent manufacturing and plays an important role.In this thesis,the application of multi-AGV in digital intelligent manufacturing workshop is studied.Firstly,the application of multi-AGV path planning and scheduling technology in intelligent workshops at home and abroad is analyzed.Aiming at the problem of multi-AGV path coordination in a workshop environment faced by manufacturing enterprises,the corresponding planning methods are pointed out and analyzed.The coordination problem between AGV is solved by introducing a revised prioritized planning algorithm,which also improves the security of AGV avoidance.Moreover,the remaining battery power of AGV is used to assign priority,which effectively improves the battery efficiency of AGV.Secondly,the prioritized planning algorithm needs to call other algorithms to solve the optimal route of a single AGV.In this thesis,an improved ant colony algorithm is proposed to solve this problem.In this algorithm,a random amount of pheromone is distributed in the map and the amount of pheromone is updated according to a fitness value.As a result,the computational efficiency of the ant colony algorithm is improved.Moreover,a mutation operation is introduced to mutate the amount of pheromone in randomly selected locations of the map,by which the problem of local optimum is well overcome.Simulation results and a comparative analysis showed the validity of the proposed method.In view of the path planning method adopted above,a scheduling model adapting to this workshop is analyzed and built.In order to make the solution of the scheduling model more suitable to the actual path planning results,this thesis has made a series of improvements on the scheduling model and algorithm.First,a multi-objective optimization model is established according to both the number of depots and the AGV’s battery consumption,so that the result of task allocation is more reasonable.Then,according to the area,distribution,shape characteristics of obstacles and the number of depots contained in the environment,this thesis derives a new coefficient which is constructed as the weighted value of the distance between workstations to improve the robustness of the model.The modified genetic algorithm(GA)is used to obtain the scheduling results.A series of experiments and simulations are carried out in combination with the previous path planning and scheduling model,which verifies that the proposed scheduling method is more reasonable.