Infrastructure-based autonomous mobile robots for internal logistics — challenges and future perspectives
Journal article, 2027
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.
Human–robot interaction
Cloud robotics
Infrastructure sensing
Autonomous mobile robots
Multi-robot systems
Internal logistics
Author
Erik Brorsson
Chalmers, Electrical Engineering, Systems and control
Volvo Group
Kristian Ceder
Chalmers, Electrical Engineering, Systems and control
Ze Zhang
Chalmers, Computer Science and Engineering (Chalmers), Computer and Network Systems
Sabino Francesco Roselli
Chalmers, Electrical Engineering, Systems and control
Chalmers Next Labs AB
Endre Erös
Stiftelsen Chalmers Industriteknik
Martin Dahl
Chalmers, Electrical Engineering, Systems and control
Beatrice Alenljung
University of Skövde
Jessica Lindblom
Uppsala University
Thanh Bui
RISE Research Institutes of Sweden
A. Hanna
Volvo Group
Emmanuel Dean
Chalmers, Electrical Engineering, Systems and control
Lennart Svensson
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Kristofer Bengtsson
Volvo Group
Per Lage Götvall
Volvo Group
Knut Åkesson
Chalmers, Electrical Engineering, Systems and control
Robotics and Autonomous Systems
0921-8890 (ISSN)
Vol. 207 105798AIHURO-Intelligent human-robot collaboration
VINNOVA (2022-03012), 2023-02-01 -- 2026-01-31.
Subject Categories (SSIF 2025)
Computer Vision and learning System
Robotics and automation
Human Computer Interaction
DOI
10.1016/j.robot.2026.105798