In order to solve the scheduling and path planning problems of multi-AGVs in an intelligent manufacturing workshop, it is necessary to consider loading, unloading, and transporting the workpiece of each AGV at the same time. A step task scheduling and path optimization mode of AGV is proposed. The process is as follows: Firstly, a mathematical model algorithm and a material transportation task allocation algorithm based on the urgency degree of workpiece processing were established for the optimization objective, and all workpiece transportation task sequences between shelves and processing equipment were assigned to the corresponding AGV to generate the initial feasible path of each AGV. Then, the AGV collision detection and anti-collision algorithm are designed to plan the global collision-free walking path of multi-AGVs in the workshop, and the path can be dynamically adjusted according to the delivery task. The model is solved by a heuristic algorithm ant colony algorithm and MATLAB coding. Finally, an example is given to verify the effectiveness of the method, which can effectively solve the task allocation of multi-AGVs and avoid collision path planning based on the transportation task sequence, and improve the work efficiency of AGV. This research can provide a theoretical basis and practical reference for realizing multi AGVs collaborative scheduling by using AGV automated material transport system in an intelligent production workshop.
Due to the shortcomings of GA in path planning, there are too many control variables and it is easy to fall into local areas. We introduce the idea of a gene bank and store the new chromosomes generated each time in the gene bank. The idea of linear regression is proposed to predict the probability of crossover and mutation of the next generation. At the same time, a method of increasing chromosome diversity is proposed, which avoids the problem that it is difficult to set the control variable parameters reasonably and fall into local optimum when the GA is applied in path planning. To overcome the shortcomings of ACO in path planning, an ACO with improved heuristic function and transition probability was proposed, and the method of the real-time environment was given. Through the traveling salesman problem, the improved GA and ACO are combined to apply to path planning. The experimental results show that the fusion of GA and ACO in this paper is superior to ordinary GA and ACO in all aspects of path planning.
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