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

2025-12-17

AI Summary

To address low localization accuracy, poor environmental perception robustness, and weak human–robot collaboration in purely distributed autonomous mobile robot (AMR) systems within industrial settings, this paper proposes an infrastructure-augmented hybrid intelligence architecture. It integrates multi-source external perception (UWB, visual-inertial SLAM, and IMU), edge–cloud collaborative computing, and a lightweight on-board decision-making module. We introduce, for the first time, a three-tier reference architecture—“infrastructure-aware perception,” “edge–cloud coordination,” and “on-vehicle autonomy”—and systematically validate its novel paradigms in localization, perception, and motion planning. Real-world deployment on a heavy-vehicle manufacturing production line demonstrates a 23.6% improvement in task success rate and a 41.2% reduction in path-planning latency. Human-centered UX evaluation confirms significant gains in operator trust and collaborative efficiency. This work establishes both a theoretical foundation and a practical blueprint for scalable, robust, and human-compatible industrial AMR systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• Addresses underexplored infrastructure-based AMR systems for logistics
• Reviews core technologies like localization, perception, and planning
• Provides a foundation for scalable, robust AMR systems in industry

Innovation

• Infrastructure-based sensing and on-premise cloud computing
• Reference architecture for scalable AMR systems
• Real-world deployment in heavy-vehicle manufacturing