Investigation of a Hybrid Kinematic Calibration Method for the “Sina” Surgical Robot

Abstract

Calibrating the inverse kinematics of complex robots is often a challenging task. Finding analytical solutions is not always possible and the convergence of numerical methods is not guaranteed. The model-free approaches, based on machine learning and artificial intelligence, are fast and easy to work, however, they need a huge amount of experimental training data to provide acceptable results. In this article, we proposed a hybrid method to benefit the advantage of both model-based and model-free approaches. The forward kinematics of the robot is calibrated using a model-based approach, and its inverse kinematics using a neural network. Hence, while there is no need to solve the nonlinear inverse kinematic equations, the training data of the neural network is generated artificially by the calibrated forward kinematic model. The implementation of the proposed methodology on slave robot of the “Sina” surgical system revealed reasonably good results. The accuracy improved by ${\bf 53\%}$ and ${\bf 43\%}$ for position and orientation, respectively, after calibration.