A Convergent Approach of Mathematical Modelling and Deep Learning for Inverse Kinematics Solutions in Stewart Platform Parallel Robots

The Stewart Platform, a sophisticated robotic mechanism known for its precision positioning and orientation in six degrees of freedom (6-DOF), is utilized as a testbed for the proposed solution. The unique design of the platform, combining translational and rotational movements, makes it suitable for applications that demand high accuracy and rapid position and orientation adjustments. It is widely used in sectors like flight simulators, animatronics, underwater research, and medical devices. This research presents an innovative solution for the inverse kinematics problem of the Stewart platform parallel robot by synergistically integrating mathematical modeling and deep learning techniques. The study introduces a fusion of fully connected neural networks and mathematical models, harnessing the computational prowess and adaptability of deep learning and the precision of established mathematical theories.