Mathematical Modelling and Simulations of Screw Transformations used in Robotics with the Help of AI and ML Concepts

The paper explores the application of mathematical modeling and simulations in the domain of screw transformations utilized in robotics, leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) concepts. Screw transformations, fundamental to robotic kinematics and dynamics, provide a unified framework for describing rotational and translational movements. This research focuses on developing sophisticated mathematical models that accurately represent these transformations, enhancing the precision and efficiency of robotic systems. By integrating AI and ML, the study aims to optimize the computational processes involved, enabling real-time adaptive adjustments and predictive analytics for improved robotic performance. The simulations demonstrate the practical implementation of these models, showcasing their potential in various robotic applications, including automation, navigation, and manipulation tasks. The results indicate significant advancements in the accuracy and responsiveness of robotic systems, highlighting the transformative impact of AI and ML in enhancing traditional mathematical approaches to screw transformations in robotics. The work presented in this paper is the result of the AAT Assignment given to the undergraduate student that was being solved by him under the guidance of the professors in the final year ‘AI and Robotics’ course.