Artificial Neural Networks for inverse kinematics problem in articulated robots

Abstract

The inverse kinematics problem in articulated robots implies to obtain joint rotation angles using the robot end effector position and orientation tool. Unlike the problem of direct kinematics, in inverse kinematics there are no systematic methods for solving the problem. Moreover, solving the inverse kinematics problem is particularly complicated for certain morphologies of articulated robots. Machine learning techniques and, more specifically, artificial neural networks (ANNs) have been proposed in the scientific literature to solve this problem. However, there are some limitations in the performance of ANNs. In this study, different techniques that involve ANNs are proposed and analyzed. The results show that the proposed original bootstrap sampling and hybrid methods can substantially improve the performance of approaches that use only one ANN. Although all of these improvements do not solve completely the inverse kinematics problem in articulated robots, they do lay the foundations for the design and development of future more effective and efficient controllers. Therefore, the source code and documentation of this research are also publicly available to practitioners interested in adapting and improving these methods to any industrial robot or articulated robot.