Nov 13 – 14, 2024
Europe/Berlin timezone

Exploration of Neural Network Architectures for Inertia Parameter Identification of a Robotic Arm

Nov 14, 2024, 2:00 PM
45m
Poster Main Track Main Conference

Description

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

Primary authors

Bernd Waltersberger (Fakultät M+V) Maximilian Gießler (Fakultät M+V) Stefan Glaser (Fakultät EMI) Stefan Hensel (Fakultät EMI) Thomas Granser (Fakultät EMI)

Presentation materials