External Torque Estimation for Mobile Manipulators: A Comparison of Model-based and LSTM Methods

Matthias Stueben, Alexander Poeppel, Wolfgang Reif

Abstract: Online monitoring of external forces and torques is highly important for safety and robustness in certain manipulation tasks and close interaction with humans. For fixed-base manipulators, methods using explicit dynamic models as well as neural networks are popular. In this paper, we address the problem of estimating external torques on a mobile manipulator, where the mobile base introduces additional dynamic effects on the manipulator joints. We adapt a model-based method that is established for fixed-base manipulators to the mobile manipulator case. We identify the relevant dynamic parameters and use a momentum observer for online torque estimation. A learning-based method using long short-term memory (LSTM) neural networks is presented afterwards. The accuracy of the two methods is compared in an evaluation with a real mobile manipulator with attached weights.