Inverse kinematics solutions for serial robots using support vector regression

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.