A Concept for Autonomous Problem-Solving in Intralogistics Scenarios

2025-07-04

AI Summary

To address the limited autonomy of intralogistics automation systems in handling unforeseen disruptions and their excessive reliance on human intervention, this paper proposes a three-stage autonomous problem-solving framework: context-aware situational modeling, dynamic situational analysis, and large language model (LLM)-driven strategy generation. The framework integrates structured knowledge representation with dynamic policy search to enable semantic understanding and adaptive responses in unstructured environments. Experimental results demonstrate significant improvements in autonomous decision-making capability and environmental adaptability across diverse disruption scenarios, markedly reducing human intervention frequency; while certain complex tasks still require human–machine collaboration, the system achieves a qualitative leap in overall autonomy. The key contribution lies in the first deep integration of LLMs into the intralogistics autonomous decision-making closed loop, establishing an interpretable and evolvable, context-enhanced decision architecture.

Technology Category

• Multiagent Systems: Distributed Problem Solving
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Enhancing autonomy in intralogistics for unforeseen situations
• Overcoming technological and environmental complexity challenges
• Leveraging AI for adaptive decision-making in automation

Innovation

• Context enrichment for better decision-making
• Situation analysis to handle complexity
• Large Language Models for autonomous solutions