Details
Titel in Übersetzung | Nutzung des menschlichen Expertenwissens zur Automatisierung des Gabelstaplerfahrens |
---|---|
Originalsprache | Englisch |
Seitenumfang | 11 |
Fachzeitschrift | Logistics Journal |
Jahrgang | 2024 |
Ausgabenummer | 20 |
Publikationsstatus | Veröffentlicht - 30 Okt. 2024 |
Veranstaltung | 20. WGTL-Kolloquium 2024 - Dresden, Deutschland Dauer: 26 Sept. 2024 → 27 Sept. 2024 |
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.
ASJC Scopus Sachgebiete
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Management-Informationssysteme
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
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in: Logistics Journal, Jahrgang 2024, Nr. 20, 30.10.2024.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Leveraging human expert knowledge to automate forklift truck driving
AU - Köhne, Phil
AU - Schaper, Mirko
AU - Lübbehusen, Justus
AU - Küster, Benjamin
AU - Stonis, Malte
AU - Overmeyer, Ludger
N1 - Publisher Copyright: © 2024 Logistics Journal: Proceedings.
PY - 2024/10/30
Y1 - 2024/10/30
N2 - 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.
AB - 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.
KW - Imitation Learning (IL)
KW - industrial truck automation
KW - intralogistics
KW - load handling
KW - ROS2
UR - http://www.scopus.com/inward/record.url?scp=85214521971&partnerID=8YFLogxK
U2 - 10.2195/lj_proc_koehne_en_202410_01
DO - 10.2195/lj_proc_koehne_en_202410_01
M3 - Conference article
AN - SCOPUS:85214521971
VL - 2024
JO - Logistics Journal
JF - Logistics Journal
SN - 1860-7977
IS - 20
T2 - 20. WGTL-Kolloquium 2024
Y2 - 26 September 2024 through 27 September 2024
ER -