Autonomous Systems in Intralogistics – State of the Art and Future Research Challenges

Johannes Fottner, Prof. Dr.-Ing. , Dana Clauer , Fabian Hormes , Michael Freitag, Prof. Dr.-Ing. , Thies Beinke, Prof. Dr.-Ing. , Ludger Overmeyer, Prof. Dr.-Ing. , Simon Nicolas Gottwald , Ralf Elbert, Prof. Dr.-Ing. , Tessa Sarnow , Thorsten Schmidt, Prof. Dr.-Ing. habil. , Karl Benedikt Reith , Hartmut Zadek, Prof. Dr.-Ing. , Franziska Thomas

Original Paper

First online: 04.02.2021

DOI: 10.23773/2021_2

Cite this article as: Fottner et al., Logistics Research (2021) 14:02. doi:10.23773/2021_2

Abstract

The paper at hand presents a definition of autonomous intralogistics systems and a classification of intralogistics systems with regard to their degree of autonomy. Intralogistics − a complex interplay of different logistics functions − covers the organization, control, execution and optimization of internal material and information flows. Over the past two decades, numerous authors have observed and proclaimed an increase in complexity in manufacturing and supply chain operations. A key approach to face this challenge is a paradigm shift from centralized, hierarchical organization structures towards, networked and autonomous systems. Autonomous intralogistics systems enable self-contained, decentralized planning, execution, control, and optimization of internal material and information flows through cooperation and interaction with other systems and with humans.Based on the definition of autonomous intralogistics systems, the authors propose a two-dimensional classification framework covering different automation stages for different intralogistics task levels. The developed classification framework is applied to various industry use cases to evaluate and discuss the state of the art regarding the implementation of autonomous intralogistics systems. Finally, the paper provides an outlook on future research and poses key research questions.

Keywords

Logistics intralogistics autonomous systems mobile robotics decentralization classification framework