Robot-assisted welding has become a key enabling technology in modern manufacturing due to its capability to enhance welding precision, productivity, and operational safety compared with conventional manual welding processes. This review provides a comprehensive analysis of robot-assisted welding technologies within the framework of Industry 4.0 manufacturing systems. A systematic literature investigation based on the PRISMA methodology was conducted, leading to the detailed examination of 169 research articles covering robot configurations, kinematic modeling techniques, trajectory planning approaches, vision-based sensing systems, and advanced control strategies. The analysis reveals that articulated robotic manipulators with six or more degrees of freedom dominate industrial welding applications due to their superior dexterity and workspace flexibility. The study also highlights the growing importance of vision-guided seam tracking, multi-sensor monitoring, and intelligent control techniques for improving weld quality and process stability. Furthermore, the integration of artificial intelligence, deep learning, and optimization algorithms has significantly improved defect detection, trajectory planning, and adaptive welding control. Despite these advancements, several challenges remain, including limited adaptability of traditional teach-and-playback programming, difficulties in real-time process monitoring, and the need for robust multi-robot coordination in dynamic manufacturing environments. The findings demonstrate that the convergence of robotic welding with Industry 4.0 technologies such as IoT, big-data analytics, and intelligent automation will play a crucial role in developing autonomous and self-optimizing welding systems. This review synthesizes current research trends, identifies key technological gaps, and provides future research directions to support the development of intelligent robotic welding systems for next-generation manufacturing.
Shirsendu Das (Wed,) studied this question.