Infrastructure-based autonomous mobile robots for internal logistics — challenges and future perspectives
Artikel i vetenskaplig tidskrift, 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
Författare
Erik Brorsson
Chalmers, Elektroteknik, System- och reglerteknik
Volvo Group
Kristian Ceder
Chalmers, Elektroteknik, System- och reglerteknik
Ze Zhang
Chalmers, Data- och informationsteknik, Dator- och nätverkssystem
Sabino Francesco Roselli
Chalmers, Elektroteknik, System- och reglerteknik
Chalmers Next Labs AB
Endre Erös
Chalmers Industriteknik (CIT)
Martin Dahl
Chalmers, Elektroteknik, System- och reglerteknik
Beatrice Alenljung
Högskolan i Skövde
Jessica Lindblom
Uppsala universitet
Thanh Bui
RISE Research Institutes of Sweden
A. Hanna
Volvo Group
Emmanuel Dean
Chalmers, Elektroteknik, System- och reglerteknik
Lennart Svensson
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Kristofer Bengtsson
Volvo Group
Per Lage Götvall
Volvo Group
Knut Åkesson
Chalmers, Elektroteknik, System- och reglerteknik
Robotics and Autonomous Systems
0921-8890 (ISSN)
Vol. 207 105798AIHURO-Intelligent människa-robot-samarbete
VINNOVA (2022-03012), 2023-02-01 -- 2026-01-31.
Ämneskategorier (SSIF 2025)
Datorseende och lärande system
Robotik och automation
Människa-datorinteraktion (interaktionsdesign)
DOI
10.1016/j.robot.2026.105798