Robot Calibration

Robot calibration aims to improve the accuracy of a robot's positional control by precisely determining its kinematic parameters. Current research emphasizes developing more efficient and autonomous calibration methods, focusing on algorithms like adaptive moment estimation (Adam) and Levenberg-Marquardt, often incorporating multi-plane constraints or unscented Kalman filters to handle noise and improve convergence. These advancements are crucial for enhancing the precision and reliability of robots in various applications, from industrial manufacturing and collaborative robotics to virtual reality haptic feedback systems, ultimately increasing productivity and safety.