Integrating long short-term memory for optimal control of 6-DOF welding robot arm

Abstract

In the realm of mechatronics, robots stand out as emblematic manifestations of societal progress. The synergy between the evolution of robotic technologies and artificial intelligence (AI) has been pivotal in refining performance and elevating the automation capabilities of robotic arms. This study delves into the integration of the long short-term memory (LSTM) AI model in the control of the inverse dynamics of a six-degree-of-freedom welding robot arm, employing a velocity-based motion control methodology. Noteworthy for its innovative operational approach, this method is widely employed across diverse models of industrial robotic arms. The research findings underscore the superior optimization results achieved by the LSTM model during the accumulation of control signals, surpassing previous studies in the same domain. Anticipated as a catalyst for substantial improvements in the efficiency of welding robot operations, this model heralds a promising avenue for future advancements in optimization techniques.