A Concept for Autonomous Problem-Solving in Intralogistics Scenarios

TL;DR

This paper proposes a structured concept to enhance autonomy in intralogistics automation systems through context enrichment, situation analysis, and solution generation, leveraging technologies like Large Language Models to enable more adaptive problem-solving.

Contribution

It introduces a novel structured approach for increasing autonomy in automation systems, integrating advanced technologies such as Large Language Models for better decision-making.

Findings

The concept improves system independence and decision-making capabilities.

Discussion of practical realizations, especially using Large Language Models.

Significant enhancement in adaptive problem-solving in intralogistics environments.

Abstract

Achieving greater autonomy in automation systems is crucial for handling unforeseen situations effectively. However, this remains challenging due to technological limitations and the complexity of real-world environments. This paper examines the need for increased autonomy, defines the problem, and outlines key enabling technologies. A structured concept is proposed, consisting of three main steps: context enrichment, situation analysis, and generation of solution strategies. By following this approach, automation systems can make more independent decisions, reducing the need for human intervention. Additionally, possible realizations of the concept are discussed, especially the use of Large Language Models. While certain tasks may still require human assistance, the proposed approach significantly enhances the autonomy of automation systems, enabling more adaptive and intelligent problem-solving capabilities.