Smart manufacturing uses robots and artificial intelligence techniques to minimize human interventions in manufacturing activities. Inspection of the machine’ working status is critical in manufacturing processes, ensuring that machines work correctly without any collisions and interruptions, e.g., in lights-out manufacturing. However, the current method heavily relies on workers onsite or remotely through the Internet. The existing approaches also include a hard-wired robot working with a computer numerical control (CNC) machine, and the instructions are followed through a pre-program path. Currently, there is no autonomous machine tending application that can detect and act upon the operational status of a CNC machine. This study proposes a deep learning-based method for the CNC machine detection and working status recognition through an independent robot system without human intervention. It is noted that there is often more than one machine working in a representative industrial environment. Therefore, the SiameseRPN method is developed to recognize and locate a specific machine from a group of machines. A deep learning-based text recognition method is designed to identify the working status from the human–machine interface (HMI) display.

Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process.