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.
Imitation Learning, industrial truck automation, intralogistics, ROS2, load handling
Automated industrial trucks master difficult driving situations worse than humans – for now. New approaches based on artificial intelligence (AI) are intended to replicate human driving behavior and give automated systems more flexibility.
artificial intelligence, intralogistics, industrial trucks
Project thesis
Concept for the optimal arrangement of screens on a simulator
Student job, Internship
Testing and development of advanced imitation learning algorithms
Student job, Internship
