Beschreibung
In this paper, we propose a machine learning based approach for identifying inertia parameters of robotic systems. The method is evaluated in simulation and compared against classical methods. Therefore, parameter identification based upon a numerical optimization is implemented and tested on ground truth data. For a case study, the physical simulation of a four degree of freedom robot arm is setup, formulating the problem with Newton-Euler equations in contrast to the conventional Lagrangian formulation. Additionally, a test methodology for assessing various neural network architectures is derived.
Keywords: Inertia parameters identification, robotics, numerical optimization, Newton-Euler, neural networks
Hauptautoren
Bernd Waltersberger
(Fakultät M+V)
Maximilian Gießler
(Fakultät M+V)
Stefan Glaser
(Fakultät EMI)
Stefan Hensel
(Fakultät EMI)
Thomas Granser
(Fakultät EMI)
