Real-time inverse kinematics function generation using (GANs) and advanced computer vision for Robotics joints

This research presents a novel approach for real-time inverse kinematic function generation for robotic joints using Generative Adversarial Networks (GANs) integrated with advanced computer vision techniques. This project includes an automated [15] model that moves based on human hand movements. The system uses Modern computer vision techniques to precisely interpret human hand motions, which enables a robot to imitate hand movements in real-time, mainly in the X and Y axes. The proposed technique leverages GANs for modeling complex, high-dimensional kinematic transformations, allowing for accurate and effortless computation of joint configurations determined by end-effector positions. Thus, if a human hand performs a wave motion, the robotic arm will move from left to right, replicating the wave action. Using sensors, inverse kinematics, and AI/ML algorithms, the outcome shows significant improvements in motion accuracy and computational efficiency over usual inverse kinematic solutions, showcasing the model’s potential in human-robot interactions.