Navigation of Automated Guided Vehicles (AGVs) in factory environments is a vital application scenario of autonomous systems. However, due to the complexity and variability of these environments, designing collision-free navigation strategies is highly challenging. To address this, this paper introduces a framework that leverages Deep Reinforcement Learning (DRL) for AGV navigation within factory environments. We prioritize safety throughout both the training phase and the execution of navigation strategies. Specifically, we employ a Signed Distance Function (SDF) to accurately represent the spatial relationship between the AGVs and obstacles, and integrating these constraints within the Markov Decision Process (MDP). The proposed method is evaluated in GAZEBO simulating environments, where AGVs navigate safely to reach designated storage racks. The experimental outcomes reveal that the proposed method is able to successfully learn obstacles avoiding navigation, exhibiting strong generalization capabilities across various environment settings.