Contributo in atti di convegno, 2022, ENG, 10.1007/978-3-031-13321-3_23
Mutti, Stefano; Renò, Vito; Nitti, Massimiliano; Dimauro, Giovanni; Pedrocchi, Nicola
Università degli studi di Bari Aldo Moro; Consiglio Nazionale delle Ricerche
Mobile manipulators are often comprised of an extensive kinematic chain resulting from an industrial robot mounted on top of an autonomous mobile robot. In such a way, the system not only retains the parameters embedded in the two sub-systems, hence DH parameters for the industrial robot and odometry parameters for the mobile robot, but also includes the relative transformation between the two parts and an additional transformation for a camera mounted on the kinematic chain.In this complex setup, it is relatively simple to introduce kinematic inaccuracies, or in some cases, to operate the system in such a way that the kinematic parameters vary(e.g., rubber wheels on high payload).Estimating the values of such parameters might be too demanding for the on-board computing system.In this work, we propose a cloud-based visually aided parameter estimation method, which constantly receives data from the mobile manipulator and generates better estimates of the kinematic parameters through an UKF dual estimation.The overall system architecture is presented to the reader, together with the reasons for relying to a cloud based paradigm, for then giving a theoretical analysis and real world experiments and results.
Image Analysis and Processing. ICIAP 2022 Workshops, pp. 258–268, Lecce, 23/5/2022-27/5/2022
cloud, kinematic calibration, mobile robot, industrial robotics
Mutti Stefano, Nitti Massimiliano, Pedrocchi Nicola, Reno Vito
STIIMA – Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato
ID: 471174
Year: 2022
Type: Contributo in atti di convegno
Creation: 2022-09-23 15:32:53.000
Last update: 2023-06-13 11:55:23.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-031-13321-3_23
URL: http://www.scopus.com/record/display.url?eid=2-s2.0-85135867418&origin=inward
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:471174
DOI: 10.1007/978-3-031-13321-3_23
Scopus: 2-s2.0-85135867418
ISI Web of Science (WOS): WOS:000870468300023