This paper proposes a support vector machine (SVM)-based AGV scheduling strategy that enhances the scheduling efficiency of automated guided vehicles (AGVs) in intelligent factories. The developed scheme optimizes the task area division process to endow the AGVs with the ability to avoid obstacles in complex dynamic environments. Specifically, given the two AGV motion cases, i.e., towards a single target point and multiple target points, the optimal path was determined utilizing the exhaustive and the Q-learning methods, while path optimization was realized by utilizing different schemes. Based on the shortest path obtained, a nonlinear programming model with the shortest time as the objective was built, and the AGV’s turning path was proved to be optimal by the non-dominated sorting genetic algorithm (NSGA-II). Several simulation tests and calculation results validated the proposed method’s effectiveness, highlighting that the developed scheme is a rational solution to the obstacle congestion and deadlock problems. Moreover, the experimental results demonstrated the proposed method’s superiority in path planning accuracy and its ability to respond well in complex dynamic environments. Overall, this research provides a reference for developing and applying AGV cluster scheduling in real operational scenarios.
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