Leveraging human expert knowledge to automate forklift truck driving

authored by

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.

Organisation(s)

Institute of Transport and Automation Technology

External Organisation(s)

Institut für integrierte Produktion Hannover (IPH)

Type

Conference article

Journal

Logistics Journal

Volume

2024

No. of pages

11

ISSN

1860-7977

Publication date

30.10.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Management Information Systems, Control and Systems Engineering, Management Science and Operations Research

Electronic version(s)

https://doi.org/10.2195/lj_proc_koehne_en_202410_01 (Access: Open)