Leveraging human expert knowledge to automate forklift truck driving

verfasst von: Phil Köhne, Mirko Schaper, Justus Lübbehusen, Benjamin Küster, Malte Stonis, Ludger Overmeyer
Abstract: This work explores the challenges of fully automating in-house goods transport in environments where industrial trucks like forklift trucks remain necessary due to undefined load carrier positions and shapes. Imitation Learning (IL) is identified as a promising solution for vehicle control in repetitive tasks, yet its application in intralogistics is challenging by the dynamic complexity of industrial trucks and the large dimensional space involved. A Robot Operating System 2 (ROS2) framework is introduced, enabling the acquisition of driving data from both simulation environments and real-world demonstrators. The study also presents a network architecture combining a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network, facilitating end-to-end learning from spatial and temporal image data. The framework's effectiveness is evaluated using a dataset of expert driving maneuvers to assess the generalization potential of the IL-trained network in vehicle control in different scenarios. The research aims to demonstrate the utility of the proposed framework for data acquisition and validate IL as a control approach for industrial trucks that require generalization.
Organisationseinheit(en): Institut für Transport- und Automatisierungstechnik
Externe Organisation(en): Institut für integrierte Produktion Hannover (IPH) gGmbH
Typ: Konferenzaufsatz in Fachzeitschrift
Journal: Logistics Journal
Band: 2024
Anzahl der Seiten: 11
ISSN: 1860-7977
Publikationsdatum: 30.10.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Management-Informationssysteme, Steuerungs- und Systemtechnik, Managementlehre und Operations Resarch
Elektronische Version(en): https://doi.org/10.2195/lj_proc_koehne_en_202410_01 (Zugang: Offen)

BibTeX

@article{2ba3bfac1cbc4a5f9f3c9ef25931f422,
title = "Leveraging human expert knowledge to automate forklift truck driving",
abstract = "This work explores the challenges of fully automating in-house goods transport in environments where industrial trucks like forklift trucks remain necessary due to undefined load carrier positions and shapes. Imitation Learning (IL) is identified as a promising solution for vehicle control in repetitive tasks, yet its application in intralogistics is challenging by the dynamic complexity of industrial trucks and the large dimensional space involved. A Robot Operating System 2 (ROS2) framework is introduced, enabling the acquisition of driving data from both simulation environments and real-world demonstrators. The study also presents a network architecture combining a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network, facilitating end-to-end learning from spatial and temporal image data. The framework's effectiveness is evaluated using a dataset of expert driving maneuvers to assess the generalization potential of the IL-trained network in vehicle control in different scenarios. The research aims to demonstrate the utility of the proposed framework for data acquisition and validate IL as a control approach for industrial trucks that require generalization.",
keywords = "Imitation Learning (IL), industrial truck automation, intralogistics, load handling, ROS2",
author = "Phil K{\"o}hne and Mirko Schaper and Justus L{\"u}bbehusen and Benjamin K{\"u}ster and Malte Stonis and Ludger Overmeyer",
note = "Publisher Copyright: {\textcopyright} 2024 Logistics Journal: Proceedings.; 20. WGTL-Kolloquium 2024 ; Conference date: 26-09-2024 Through 27-09-2024",
year = "2024",
month = oct,
day = "30",
doi = "10.2195/lj_proc_koehne_en_202410_01",
language = "English",
volume = "2024",
journal = "Logistics Journal",
issn = "1860-7977",
publisher = "Wissenschaftliche Gesellschaft fur Technische Logistik",
number = "20",
}