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Schyga J, Rose H, Hinckeldeyn J, Kreutzfeldt J (2021). Analysis of the Operation of Industrial Trucks based on Position Data. Logistics Journal : Proceedings, Vol. 2021. (urn:nbn:de:0009-14-54415)

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%0 Journal Article
%T Analysis of the Operation of Industrial Trucks based on Position Data
%A Schyga, Jakob
%A Rose, Hendrik
%A Hinckeldeyn, Johannes
%A Kreutzfeldt, Jochen
%J Logistics Journal : Proceedings
%D 2021
%V 2021
%N 17
%@ 2192-9084
%F schyga2021
%X Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by presenting a concept for the analysis of industrial truck operations based merely on position data. The concept consists of a signal processing scheme to derive kinematic data and three analysis methods – Monitoring, Area analysis, and Motion analysis. Schemes for the signal processing and detection of motion events were developed and implemented as part of the TrOpLocerApp (Truck Operation Localization Analyzer-Application) for recording, displaying, and processing position data, according to the proposed system concept. The TrOpLocer-App source code is published under an open-source license and is publicly available on GitLab [RS21]. Different filter algorithms were examined, as part of the signal processing scheme, from which the low pass Butterworth filter has shown the best results in static experiments.  Validation of the motion detection scheme shows good detection quality for distinct events in a realistic movement experiment.
%L 620
%K Analysekonzept
%K Analysis Concept
%K Bewegungserkennung
%K Flurförderzeug
%K Indoor-Localization
%K Indoor-Lokalisierung
%K Industrial Truck
%K Movement detection
%K Warehouse
%K Warenlager
%R 10.2195/lj_Proc_schyga _en_202112_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-54415
%U http://dx.doi.org/10.2195/lj_Proc_schyga _en_202112_01
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@Article{schyga2021,
  author = 	"Schyga, Jakob
		and Rose, Hendrik
		and Hinckeldeyn, Johannes
		and Kreutzfeldt, Jochen",
  title = 	"Analysis of the Operation of Industrial Trucks based on Position Data",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2021",
  volume = 	"2021",
  number = 	"17",
  keywords = 	"Analysekonzept; Analysis Concept; Bewegungserkennung; Flurf{\"o}rderzeug; Indoor-Localization; Indoor-Lokalisierung; Industrial Truck; Movement detection; Warehouse; Warenlager",
  abstract = 	"Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by presenting a concept for the analysis of industrial truck operations based merely on position data. The concept consists of a signal processing scheme to derive kinematic data and three analysis methods -- Monitoring, Area analysis, and Motion analysis. Schemes for the signal processing and detection of motion events were developed and implemented as part of the TrOpLocerApp (Truck Operation Localization Analyzer-Application) for recording, displaying, and processing position data, according to the proposed system concept. The TrOpLocer-App source code is published under an open-source license and is publicly available on GitLab [RS21]. Different filter algorithms were examined, as part of the signal processing scheme, from which the low pass Butterworth filter has shown the best results in static experiments.  Validation of the motion detection scheme shows good detection quality for distinct events in a realistic movement experiment.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_schyga _en_202112_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-54415"
}
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RIS

TY  - JOUR
AU  - Schyga, Jakob
AU  - Rose, Hendrik
AU  - Hinckeldeyn, Johannes
AU  - Kreutzfeldt, Jochen
PY  - 2021
DA  - 2021//
TI  - Analysis of the Operation of Industrial Trucks based on Position Data
JO  - Logistics Journal : Proceedings
VL  - 2021
IS  - 17
KW  - Analysekonzept
KW  - Analysis Concept
KW  - Bewegungserkennung
KW  - Flurförderzeug
KW  - Indoor-Localization
KW  - Indoor-Lokalisierung
KW  - Industrial Truck
KW  - Movement detection
KW  - Warehouse
KW  - Warenlager
AB  - Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by presenting a concept for the analysis of industrial truck operations based merely on position data. The concept consists of a signal processing scheme to derive kinematic data and three analysis methods – Monitoring, Area analysis, and Motion analysis. Schemes for the signal processing and detection of motion events were developed and implemented as part of the TrOpLocerApp (Truck Operation Localization Analyzer-Application) for recording, displaying, and processing position data, according to the proposed system concept. The TrOpLocer-App source code is published under an open-source license and is publicly available on GitLab [RS21]. Different filter algorithms were examined, as part of the signal processing scheme, from which the low pass Butterworth filter has shown the best results in static experiments.  Validation of the motion detection scheme shows good detection quality for distinct events in a realistic movement experiment.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-54415
DO  - 10.2195/lj_Proc_schyga _en_202112_01
ID  - schyga2021
ER  -
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Wordbib

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ISI

PT Journal
AU Schyga, J
   Rose, H
   Hinckeldeyn, J
   Kreutzfeldt, J
TI Analysis of the Operation of Industrial Trucks based on Position Data
SO Logistics Journal : Proceedings
PY 2021
VL 2021
IS 17
DI 10.2195/lj_Proc_schyga _en_202112_01
DE Analysekonzept; Analysis Concept; Bewegungserkennung; Flurförderzeug; Indoor-Localization; Indoor-Lokalisierung; Industrial Truck; Movement detection; Warehouse; Warenlager
AB Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by presenting a concept for the analysis of industrial truck operations based merely on position data. The concept consists of a signal processing scheme to derive kinematic data and three analysis methods – Monitoring, Area analysis, and Motion analysis. Schemes for the signal processing and detection of motion events were developed and implemented as part of the TrOpLocerApp (Truck Operation Localization Analyzer-Application) for recording, displaying, and processing position data, according to the proposed system concept. The TrOpLocer-App source code is published under an open-source license and is publicly available on GitLab [RS21]. Different filter algorithms were examined, as part of the signal processing scheme, from which the low pass Butterworth filter has shown the best results in static experiments.  Validation of the motion detection scheme shows good detection quality for distinct events in a realistic movement experiment.
ER
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Mods

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  <abstract>Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by presenting a concept for the analysis of industrial truck operations based merely on position data. The concept consists of a signal processing scheme to derive kinematic data and three analysis methods – Monitoring, Area analysis, and Motion analysis. Schemes for the signal processing and detection of motion events were developed and implemented as part of the TrOpLocerApp (Truck Operation Localization Analyzer-Application) for recording, displaying, and processing position data, according to the proposed system concept. The TrOpLocer-App source code is published under an open-source license and is publicly available on GitLab [RS21]. Different filter algorithms were examined, as part of the signal processing scheme, from which the low pass Butterworth filter has shown the best results in static experiments.  Validation of the motion detection scheme shows good detection quality for distinct events in a realistic movement experiment.</abstract>
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    <topic>Analysis Concept</topic>
    <topic>Bewegungserkennung</topic>
    <topic>Flurförderzeug</topic>
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    <topic>Indoor-Lokalisierung</topic>
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2021, Iss. 17
Title

Analysis of the Operation of Industrial Trucks based on Position Data (eng)
Author Jakob Schyga, Hendrik Rose, Johannes Hinckeldeyn, Jochen Kreutzfeldt
Language eng
Abstract Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by presenting a concept for the analysis of industrial truck operations based merely on position data. The concept consists of a signal processing scheme to derive kinematic data and three analysis methods – Monitoring, Area analysis, and Motion analysis. Schemes for the signal processing and detection of motion events were developed and implemented as part of the TrOpLocerApp (Truck Operation Localization Analyzer-Application) for recording, displaying, and processing position data, according to the proposed system concept. The TrOpLocer-App source code is published under an open-source license and is publicly available on GitLab [RS21]. Different filter algorithms were examined, as part of the signal processing scheme, from which the low pass Butterworth filter has shown the best results in static experiments. Validation of the motion detection scheme shows good detection quality for distinct events in a realistic movement experiment. Indoor-Lokalisierungssysteme (IPSs) können einen wichtigen Beitrag zur Analyse und Optimierung von innerbetrieblichen Transportprozessen leisten. Das übergeordnete Ziel dieser Arbeit besteht darin, zu untersuchen, wie Lokalisierungsdaten zur Analyse des Betriebs von Flurförderzeugen in Lagern genutzt werden können. Dies wird durch die Vorstellung eines Systemkonzeptes zur Analyse des Betriebs von Flurförderzeugen erreicht, welches ausschließlich die Verfügbarkeit von Positionsdaten voraussetzt. Das Konzept besteht aus einem Signalverarbeitungsschemas zur Ableitung kinematischer Daten und drei Analysemethoden – Monitoring, Flächenanalyse und Bewegungsanalyse. Schemen zur Signalverarbeitung und zur Erkennung von Bewegungsereignissen wurden entwickelt und als Teil der TrOpLocer-App (Truck Operation Localization Analyzer-Application) zur Erfassung, Darstellung und Verarbeitung von Lokalisierungsdaten gemäß dem vorgestellten Systemkonzept implementiert. Der Quellcode der TrOpLocer-App ist frei verfügbar über GitLab bereitgestellt [RS21]. Es wurden verschiedene Filteralgorithmen als Teil des Signalverarbeitungsschemas untersucht, von denen ein Tiefpass-Butterworth-Filter in statischen Experimenten die besten Ergebnisse gezeigt hat. Eine Validierung des Bewegungserkennungsschemas zeigt eine gute Detektionsqualität für unterschiedliche Ereignisse in einem praxisnahen Bewegungsexperiment.
Subject Analysekonzept, Analysis Concept, Bewegungserkennung, Flurförderzeug, Indoor-Localization, Indoor-Lokalisierung, Industrial Truck, Movement detection, Warehouse, Warenlager
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-54415
DOI https://doi.org/10.2195/lj_Proc_schyga _en_202112_01