Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/82102

TítuloTrackInFactory: a tight coupling particle filter for industrial vehicle tracking in indoor environments
Autor(es)Silva, Ivo Miguel Menezes
Pendão, Cristiano Gonçalves
Torres-Sospedra, Joaquín
Moreira, Adriano
Palavras-chaveWireless fidelity
Location awareness
Robot sensing systems
Sensor fusion
Reliability
Radiofrequency identification
Production facilities
Bayesian filtering
dead reckoning (DR)
indoor positioning
indoor tracking
industrial vehicle
particle filter (PF)
sensor fusion
tight coupling (TC)
Wi-Fi-based positioning
industry 4.0
industry 4
0
Data2022
EditoraIEEE
RevistaIEEE Transactions on Systems Man Cybernetics-Systems
CitaçãoI. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 7, pp. 4151-4162, July 2022, doi: 10.1109/TSMC.2021.3091987.
Resumo(s)Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle's initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles' weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.
TipoArtigo
URIhttps://hdl.handle.net/1822/82102
DOI10.1109/TSMC.2021.3091987
ISSN2168-2216
Versão da editorahttps://ieeexplore.ieee.org/document/9475592
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CAlg - Artigos em revistas internacionais / Papers in international journals

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