Infrastructure-based Autonomous Mobile Robots for Internal Logistics -- Challenges and Future Perspectives

TL;DR

This paper reviews infrastructure-based autonomous mobile robots for internal logistics, highlighting key challenges, proposing a reference architecture, and demonstrating a real-world deployment to guide future scalable and robust AMR systems.

Contribution

It introduces a comprehensive reference architecture for infrastructure-based AMRs and evaluates its application in a real industrial environment.

Findings

Successful deployment in a heavy-vehicle manufacturing setting

Positive user experience feedback

Identification of key challenges and opportunities

Abstract

The adoption of Autonomous Mobile Robots (AMRs) for internal logistics is accelerating, with most solutions emphasizing decentralized, onboard intelligence. While AMRs in indoor environments like factories can be supported by infrastructure, involving external sensors and computational resources, such systems remain underexplored in the literature. This paper presents a comprehensive overview of infrastructure-based AMR systems, outlining key opportunities and challenges. To support this, we introduce a reference architecture combining infrastructure-based sensing, on-premise cloud computing, and onboard autonomy. Based on the architecture, we review core technologies for localization, perception, and planning. We demonstrate the approach in a real-world deployment in a heavy-vehicle manufacturing environment and summarize findings from a user experience (UX) evaluation. Our aim is to provide a holistic foundation for future development of scalable, robust, and human-compatible AMR systems in complex industrial environments.