Control of a Biped Robot With Support Vector Regression in Sagittal Plane

Abstract

This paper describes the control of an autonomous biped robot that uses the support vector regression (SVR) method for its sagittal balance. This SVR uses the zero moment point (ZMP) position and its variation as input and the torso correction of the robot's body as output. As the robot model used segments the robot into eight parts, it is difficult to use online. This is the main reason for using the artificial intelligence method. The SVR was trained with simulation data that was previously tested with the real robot. The SVR was found to be faster (with similar accuracy) than a recurrent network and a neuro-fuzzy control. This method is more precise than the model based on an inverted pendulum. The design of the feet is considered in terms of accommodating the force sensors used to estimate the center of pressure (CoP). The SVR was tested in the real robot using joint trajectories that are similar to those of human beings, and the results are presented.