Research on Applications of Deep Learning in Robotic Automation for Assembly Lines

In contemporary manufacturing, the utilisation of robotic automation within assembly lines represents a pivotal avenue for enhancing production efficiency and accuracy. This paper puts forth an innovative deep learning model that fuses convolutional neural network (CNN) with reinforcement learning, with the objective of enhancing the precision and efficacy of robots in assembly operations. The model employs a convolutional neural network to facilitate real-time visual recognition and segmentation of assembly components, thereby enabling the robot to accurately identify and locate components in complex environments. Concurrently, reinforcement learning enables the robot to ascertain the optimal operational strategy through interaction with the environment. This novel approach, which unifies visual perception and decision-making capabilities within a deep learning framework, endows robots with the capacity to adapt to alterations in the assembly process, respond to unforeseen impediments, and enhance operational efficiency. Experimental analysis indicates that, in comparison with the conventional robot control method, the assembly time of the model is reduced by 30% and the accuracy is enhanced by 20 %.