An Artificial Neural Network Approach for Solving Inverse Kinematics Problem for an Anthropomorphic Manipulator of Robot SAR-401
Vadim Kramar, Oleg Kramar, Aleksey Kabanov
Abstract
The paper proposes a new design of an artificial neural network for solving the inverse kinematics problem of the anthropomorphic manipulator of robot SAR-401. To build a neural network (NN), two sets were used as input data: generalized coordinates of the manipulator and elements of a homogeneous transformation matrix obtained by solving a direct kinematics problem based on the Denavi–Hartenberg notation. According to the simulation results, the NN based on the homogeneous transformation matrix showed the best accuracy. However, the accuracy was still insufficient. To increase the accuracy, a new NN design was proposed. It consists of adding a so-called “correctional” NN, the input of which is fed the same elements of the homogeneous transformation matrix and additionally the output of the first NN. The proposed design based on the correctional NN allowed the accuracy to increase two times. The application of the developed NN approach was carried out on a computer model of the manipulator in MATLAB, on the SAR-401 robot simulator, as well as on the robot itself.