Infrastructure-based Autonomous Mobile Robots for Internal Logistics

This paper explores the use of infrastructure-based Autonomous Mobile Robots (AMRs) for internal logistics, highlighting key opportunities and challenges. It introduces a reference architecture combining infrastructure-based sensing, on-premise cloud computing, and onboard autonomy.

Details

The paper presents a comprehensive overview of infrastructure-based Autonomous Mobile Robot (AMR) systems for internal logistics. While most current AMR solutions focus on decentralized, onboard intelligence, the authors argue that infrastructure-based systems, involving external sensors and computational resources, can provide additional opportunities and benefits. To support this, the paper introduces a reference architecture that combines infrastructure-based sensing, on-premise cloud computing, and onboard autonomy. The authors then review the core technologies for localization, perception, and planning in this infrastructure-based approach. They demonstrate the approach through a real-world deployment in a heavy-vehicle manufacturing environment and summarize findings from a user experience (UX) evaluation. The goal is to provide a holistic foundation for future development of scalable, robust, and human-compatible AMR systems in complex industrial settings.