Inverse kinematics solutions for serial robots using support vector regression

Abstract

Serial kinematic chains are widely used in robotics and computer animation among other fields. Many manipulators do not have closed-form solutions to the inverse kinematics problem, which is of great importance for many applications. In this paper we introduce a fast and accurate procedure which yields all joint angle solutions for a given manipulator or limb posture (position and orientation) and certain swivel angle. By means of a spatial decomposition method, the procedure involves finding accurate models which represent the behavior of the robot or limb in a given workspace region. We propose Support Vector Machines, a very popular machine learning method, as the method that models such behaviors. The performance of the method is tested on the Robotic Research Arm K-1207. The results confirm that the method finds accurate solutions and can be used on real world applications with real-time requirements.