Temporal Object Tracking in Large-Scale Production Facilities using Bayesian Estimation

verfasst von

Karl Philipp Kortmann, Johannes Zumsande, Mark Wielitzka, Tobias Ortmaier

Abstract

Moving towards comprehensive digitalization of production facilities, it is critical to know the location of work pieces, charges, or other objects of interest that change location over time during production. For the case of a limited traceability of these objects, we first present a theoretical approach that performs a recursive Bayesian estimation of the object's location over time based on typical passage measurements in production (e. g. light barriers or RFID systems). The probabilistic method is based on a directed acyclic graph modeling the transfer and sojourn of the objects in the production network. Subsequently, the method is validated on simulated data while varying both size and measurement conditions of the process. The results show the benefit of the proposed method against a single estimation and demonstrate its potential for the application in real time scenarios.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

IFAC-PapersOnLine

Band

53

Seiten

11125-11131

Anzahl der Seiten

7

ISSN

2405-8963

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2020.12.271 (Zugang: Offen)