In other words, the robot PLC should have a higher sensing and perception function, which ensures that the exchange of signals with the other robots is independent (and event-driven). The robot's motion and behavior in the working space is interactive. In ref [11], a sensing method was proposed to enable the widespread adoption and development of IoT technology in the manufacturing industry to be close to the user. This has played an important role in the manufacturing industry. For example, radio frequency identification (RFID) has been applied to electromagnetic field effects to transmit data for automatic identification and tracking of tags attached to the target product. The RFID system is composed of a tag and a reader. The tag stores the information being read. In other words, tag data can also be obtained through decryption without requiring additional signals to be provided to other systems. Therefore, the RFID reader can indirectly track the physical movement state mode of the tag. Therefore, it can indirectly track the physical movement of the object to which the tag is attached. In industrial manufacturing procedures, RFID can be applied to the management of the supply chain, production scheduling, part/vehicle tracking, etc. The sensors, wireless networks, and sensor networks (wire-less sensor networks, WSNs) can also be configured out of space from a node structure. The node structure is used to sense the surrounding environment, and can then be manipulated and other node communication signals obtained. Sensor nodes operate in a self-organizing and decentralized manner to maintain the best connection status as much as possible. RFID and WSNs represent two complementary technologies. RFID can be used to discover and identify the locations of objects that are difficult to detect or distinguish using traditional sensor technology. However, they cannot monitor the state of the objects. In contrast, wireless sensor networks not only provide information about objects and environmental conditions but also support multi-hop wireless communication. Some WSNs may be equipped with actuators to perform the appropriate actions. Finally, RFID and WSNs could be combined to facilitate industrial development. In this study, we propose many controllers for constructing smart factories, including sensors, RFID, and WSN control methods. However, it is unfortunate that production line process objects are not fully monitored by lens imaging technology with artificial intelligence (AI) algorithms. In addition, the color of the production line with the recognition method has rarely been mentioned. Intelligent manufacturing technology, which is the core concept of the fourth industrial revolution (Industry 4.0), has received increasing attention worldwide. In recent years, technologies such as the IoT, big data analysis, AI, cloud computing, and cyber-physical systems, have been developed to promote intelligent manufacturing [12; 13; 14]. In this study, we discuss research related to intelligent manufacturing, including big data and AI, to optimize mechanical equipment, the integration of computer numerical controlled machine tools and robots, and the parameter design and optimization of intelligent factories. Previous studies have reported on an effective technical solution for integrating and controlling all joints, driven by hydraulic actuators for heavy robots, in which the robot controller is constructed based mainly on industrial PLC units. The PLC controller was designed to link the central control unit and all components of the entire forging station at the same time. In addition, a hybrid robot that establishes a new method for kinematics and dynamics has been proposed. The main contribution is to demonstrate the hybrid robot system with generalized velocity analytic relations, and finally, to demonstrate and verify the newly designed hybrid robot, which is feasible in kinematic and dynamic modelling results.