Data based kinematic model of a multi-flexible-link robot arm for varying payloads

Abstract

Reducing weight and inertias of conventional robot arms with an elastic structure allows safer interactive cooperation between humans and robots. While the end effector pose of a rigid robot is determined by the forward kinematic chain, the pose of elastic arms results from a superposition of the rigid kinematics and the pose dependent deflection caused by gravity. This property complicates the computation of forward and inverse kinematics in particular in case of dynamic loads. This paper presents a machine learning approach to extract various nonlinear regression models of the forward and inverse kinematics of a three degrees of freedom (DOF) flexible-link robot arm with dynamic loads from experimental data. The forward model predicts the target pose, given the joint angles and the strain signals while the inverse kinematic model predicts the joint angles required to assume a target pose. The transformation of the original features onto suitable nonlinear features substantially improves the generalisation ability of the both forward and inverse kinematic model. The closed loop inverse kinematic controller archieves a pose accuracy of 3 mm and the results show that the learned model can solve the inverse kinematics problem of flexible robot arms with sufficient accuracy even with unknown payloads.