An Enhanced Step-Size Gaussian Damped Least Squares Method Based on Machine Learning for Inverse Kinematics of Redundant Robots

An existing problem in the robotic field is to solve the inverse kinematics (IK) problem of redundant robot with high speed and high precision. A novel IK optimization method based on the Gaussian Damped Least Squares (GDLS) is proposed in this paper. A significant contribution of this method is to make the iteration converge in a faster and more accurate way by introducing an optimal enhanced step-size coefficient. The machine learning model can be trained with 10
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data points in the reachable region of the robot, and the optimal enhanced step-size coefficient in each solving process can be predicted by the model. The accuracy and stability of the algorithm proposed are verified through an example of an arbitrary 7R redundant robot. The average number of iterations is less than 10, with super high solving speed. Furthermore, the algorithm also has better convergence, which can reach 96.23% when the error threshold is 0.01 mm. The common IK methods are evaluated in this paper, and the results show that the optimized method has good performance in convergence, accuracy and speed.